ANALYSIS OF AGE-DEPENDENT RESILIENCE FOR A HIGHWAY …

The Pennsylvania State University

The Graduate School

College of Engineering

ANALYSIS OF AGE-DEPENDENT RESILIENCE

FOR A HIGHWAY NETWORK

WITH AGING BRIDGES

A Thesis in

Civil Engineering

by

Alben Jose Kezhiyur

© 2015 Alben Jose Kezhiyur

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

May 2015

ii

The thesis of Alben Jose Kezhiyur will be reviewed and approved* by the following:

Swagata Banerjee Basu

Assistant Professor of Civil Engineering

Thesis Adviser

Prasenjit Basu

Assistant Professor of Civil Engineering

Venky Shankar

Professor of Civil Engineering

Peggy Johnson

Professor of Civil Engineering

Department Head

*Signatures are on file in the Graduate School

iii

Abstract Bridges are key links connecting different critical facilities in a highway transportation network.

Bridge damage in the event of an extreme hazard may cause severe traffic disruption and thus

directly affect the functionality of a highway network. The extent of damage that a particular

bridge may experience under a certain extreme event condition depends on various factors

including severity of the extreme event (e.g., earthquake magnitude), proximity of the bridge

with reference to the event location (e.g., the distance from the epicenter of an earthquake),

structural health (e.g., chloride induced deterioration, aging), to name a few. Based on the extent

of damage that bridges in a highway network may undergo, efforts are made to restore the

original functionality of a network in a fast and economically efficient way. The concept of

network resilience is thus closely tied to promptness in restoring the original functionality of a

network after an extreme event occurs. Although the quantification of resilience for highway

network greatly depends on the post-event recovery model, network resilience inherently

depends on the pre-event structural condition of constituent bridges.

The present study considers a small highway network (with 35 bridges) in the Memphis

region. Structural deterioration of constituent bridges due to chlorine diffusion over the bridge

life span is studied. The highway network is studied primarily at two different time scenarios,

year 2010 and year 2050 for an earthquake of magnitude 6. For each corresponding year, ages of

the bridges are identified based on their year of construction and accordingly bridge fragility

curves are developed at various stages of bridge life based on past research. Thus developed

fragility curves are used in conjunction with recovery patterns to explore time-dependent change

of network resilience considering total travel time in the network as the functionality measure.

Also, the percentage number of links having different velocity to capacity ratios for each time

scenarios is calculated to understand the congestion in different links. It is observed that aging

due to chloride deterioration has an adverse impact on seismic resilience of bridge network.

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Table of Contents

List of Figures .............................................................................................................................................. vi

List of Tables .............................................................................................................................................. vii

Acknowledgment ....................................................................................................................................... viii

Chapter 1 ....................................................................................................................................................... 1

INTRODUCTION ........................................................................................................................................ 1

1.1 Background and Motivation: ........................................................................................................ 1

1.2 Research Objective and Scope: ..................................................................................................... 2

1.3 Thesis Structure: ........................................................................................................................... 4

Chapter 2 ....................................................................................................................................................... 6

LITERATURE REVIEW ............................................................................................................................. 6

2.1 Fragility Curve Analysis: .............................................................................................................. 6

2.2 Recovery function: ........................................................................................................................ 8

2.3 Network Resilience: ...................................................................................................................... 9

Chapter 3 ..................................................................................................................................................... 11

BRIDGE NETWORK ................................................................................................................................. 11

3.1 Study Area: ................................................................................................................................. 11

3.2 Developing and Validation of Network Model:.......................................................................... 14

3.2.1 Node-Link Data: ................................................................................................................. 15

3.2.2 Origin-Destination Data: ..................................................................................................... 16

3.3 Validation of Model: ................................................................................................................... 18

3.4 Attenuation Equations: ................................................................................................................ 19

Chapter 4 ..................................................................................................................................................... 22

MODELING OF FRAGILITY DEGRADATION ..................................................................................... 22

4.1 Time Variant Quadratic Model: .................................................................................................. 22

4.2 Bridge Damage States: ................................................................................................................ 25

4.3 Recovery Patterns: ...................................................................................................................... 28

Chapter 5 ..................................................................................................................................................... 32

NETWORK RESILIENCE ......................................................................................................................... 32

5.1 Methodology: .............................................................................................................................. 32

5.2 Results: ........................................................................................................................................ 33

5.3 Observations: .............................................................................................................................. 35

v

Chapter 6 ..................................................................................................................................................... 37

Conclusions and Future Scope of work ...................................................................................................... 37

References ................................................................................................................................................... 38

Appendix A: Node-Link table ...................................................................................................................... 40

Appendix B: Origin-Destination matrix ...................................................................................................... 59

vi

List of Figures

Figure 1-1 Earthquake hazard Map of United States…………………………………………… 4

Figure 1-2 Annual average snowfall in inches for United States………………………….….. 4

Figure 3-1 Map of Memphis region identifying the study area network……………………… 10

Figure 3-2 Zoomed-in- Network area for Resilience Analysis………………………………… 11

Figure 3-3 Identification of links and nodes in the bridge network …......................................... 13

Figure 3-4 Node-link data table as inputted in XXE software ……………………………….... 15

Figure 3-5 Origin-Destination data table as inputted in XXE software …………………….…. 16

Figure 3-6 Ground Motion PGA’s for the network as obtained from REDARS ……………… 21

Figure 4-1 System level fragility curves at different points of time for each damage states …... 23

Figure 4-2 Polynomial fit of median ratio values in each damage states ……………………… 24

Figure 4-3 Capacity and Speed reduction values for minor and moderate damage states….….. 29

Figure 4-4 Capacity and Speed reduction values for major and collapse damage states….….. 30

Figure 5-1 Functionality curve for determining resilience in year 2010….……………………. 34

Figure 5-2 Functionality curve for determining resilience in year 2050….……………………. 35

vii

List of Tables

Table 1.1 Mean and standard deviation of post-event recovery times for highway bridges….. 5

Table 3.1 Bridges details……………………………………………………….……………… 12

Table 3.2 Capacity and Speed limits for Memphis region roadways…..……………………... 14

Table 3.3 Observed and Calculated flow differences for different links ……...……………… 17

Table 3.4 Coefficients and period independent constants for determining PGA …...………… 19

Table 3.5 Adjusted PGA values at the bridge site locations………………………...………… 20

Table 4.1 Median PGA Values at different time scenarios for all damage states ......………… 24

Table 4.2 Coefficients of quadratic interpolation of median values…………………………... 25

Table 4.3 Median values of each bridge group in the year 2050...……………………………. 26

Table 4.4 Median values of each bridge group in the year 2010………………......………... 26

Table 4.5 Damage state for the Bridges and links associated in year 2010 and

2050……………………………………………………………..............………...

27

Table 4.6 Mean and Standard deviation of restoration functions for each damage state……… 28

Table 5.1 Damage states of links in year 2010 and 2050 for an EQ of magnitude 6…..……… 33

Table 5.2 Network Functionality for year 2010 and 2050 at different time scenarios...……… 34

Table 5.3 Network Resilience for year 2010 and 2050 at different controlled time sets……… 35

Table 5.4 Percentage number of links in different v/c ratios for year 2010 and 2050………… 36

viii

Acknowledgment

Thanks to the Almighty for blessing me with the right wisdom to work on my Master’s thesis. I

would like to express my deepest gratitude and sincere thanks to my advisor, guide Dr. Swagata Banerjee

Basu for all the faith she kept in me and considering me in her research group. Thank you Dr. Banerjee

for all the remarks, patience, invaluable advice, suggestions and help.

I would like to thank Dr. Prasenjit Basu for taking time and providing me a direction in defining

the scope of my thesis and the constant encouragement he gave to me whenever we met. Also, I would

like to thank Prof. Venky Shankar for accepting to be a part of my thesis committee and helping me more

like a thesis adviser. He has helped me in building the network to the point of conceptualizing recovery

patterns which play an important role in calculating the network resilience. Also, Thanks you Prof. Venky

for taking your invaluable time to help me in my future decisions. The support of Prof. Venky lab group

Baradhwaj Hariharan and Jungeol Hong while working with Network Modelling cannot be forgotten. My

special thanks go to Memphis MPO team, Mr. Andrew Ray, Mr. Kwasi Agyakwa who has been a great

help in providing me O-D data for building my network. I would like to thank the whole of REDARS

team especially Mr. Stu Werner, Mr. Charles Huyck, Mr. ZhengHui Hu who has helped me with the

REDARS 3 version of the software and with the technical documentation. Finally, I would like to thank

Dr. Scott Washburn for helping me out with XXE software and build a real transportation network.

I am grateful for the funding opportunities I had at Penn State University that allowed me to

pursue my graduate studies and also allowing me to take a decision for my future academic plans. I would

like to thank Department of Physics and Prof. Richard W Robinett for giving me a TA opportunity during

spring 2014 which allowed me to take a decision to undertake research as my career option. Also, I would

like to thank once again my advisor, Prof. Swagata Banerjee for the financial support during the summer

and my second year of masters. Finally I would like to acknowledge my friends, family, parents and sister

who constantly supported me during my stay here.

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Chapter 1

INTRODUCTION

1.1 Background and Motivation:

Aging is a natural phenomenon which we see and experience in our day to day lives. Every

living being has a definite life span in this universe and to that extent, even the machinery and

equipment which we develop through our technology has a defined life span. This is true with

infrastructure too. Nothing can perform at its best permanently. Hence, it is general norm to define the

average life span of a machinery or infrastructure such as buildings and bridges. During the lifespan

of any infrastructure say bridges, performance degrades as aging occurs due to various internal and

external factors. This performance degradation might be due to usual wear and tear in case of

machinery, amount of traffic on the structure in case of bridges and roads, or some environmental

stressor such as corrosion etc. apart from performance degradation due to natural hazards.

The focus of this research is on spatially distributed aging bridges due to induced chloride

corrosion in a highway transportation network. Highway bridges have a very important role in

transportation networks and act as major links to various critical routes such as hospitals, schools etc.

Frangopol and Bocchini (2012) defined bridge network as a transportation network in which bridges

are the only elements which can experience structural damage during an extreme event. Damage of

bridges in the event of an extreme natural hazard causes severe disruption to the traffic and can affect

the functionality of a part or entire portion of the highway network. The extent of damage a particular

bridge experiences depends on various factors such as intensity of the extreme event, location of the

bridge with reference to the epicenter of the extreme event, chloride induced deterioration, aging etc.

Based on the damage extent the bridges undergo, efforts are made to restore the complete network to

its original functionality or close to its normal functionality in a fast and economically efficient way.

To quantify this promptness of restoration, the concept of ‘Resilience’ is used.

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Many definitions of resilience have been defined in literature and also resilience has been

calculated for various lifeline systems, networks and infrastructure groups. The most widely accepted

definition of resilience in the literature is of Bruneau et al. (2003) who conceptualized resilience of a

social system to be having 4 R’s – Robustness, Redundancy, Resourcefulness and Rapidity. A system

is considered to be robust based on the resistance offered by the system for an external demand or

extreme event and is able to withstand the adverse conditions. A redundant system is one which has

alternative paths and options during an extreme event without affecting the total system equilibrium.

System resourcefulness is measured by the ability to devise ways and means to address the

emergency situations during the extreme event. System rapidity, as the word suggests, can be

measured based on the speed at which the system recovers and overcome the losses due to the

extreme event. This study tries to addresses the robustness, redundancy and rapidity questions for the

bridge network chosen for an earthquake scenario.

1.2 Research Objective and Scope:

The main objective of this research is to obtain the seismic resilience of a highway bridge

network due to aging using suitable recovery patterns for each damage state.

To achieve this objective, the following major tasks are carried out.

a) Task 1: Develop a user-equilibrium model for the chosen network and validate the model

with real-time data

b) Task 2: Develop the time dependent polynomial fit for the median values considering a

bridge life span of 100 years.

c) Task 3: Calculate adjusted Peak Ground Acceleration (PGA) values at bridge sites using

suitable ground motion attenuation model.

d) Task 4: Define bridge damage states as well as the associated links damage states by

using attenuated PGA values at bridge sites and fragility curves of bridges

e) Task 5: Develop post-event recovery patterns for network capacities and corresponding

gain in speed limits at each damage level

3

f) Task 6: Calculate the network functionality in terms of total travel time at different

scenarios and calculate network resilience

Within the scope of this research, time-variant seismic vulnerability model of bridges, and

seismic recovery models are studied. The combined effect of aging of bridges in a network and

seismic event on bridge network resilience over a period of 40 years is studied. The resilience of the

bridge network is compared between the initial year of observation and 40th year of observation.

As can be seen from Figure 1-1 and 1-2, Memphis region in Tennessee State is moderate to

high seismically active and may experience 12-24 inches of annual snowfall. Deicing salt used during

the winter season may deteriorate health of bridges in this region, and hence make bridges more

vulnerable under seismic ground motion. Hence, it is important to study the change in seismic

resilience of the bridge network in Memphis region due to aging.

Fig 1-1: Earthquake Hazard Map of United States

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1.3 Thesis Structure:

This thesis is organized into six chapters. The initial chapter introduces the concepts involved

in this study and gives a background and importance of this study. Also, the research objective is

discussed and the important tasks undertaken to achieve this objective has been detailed.

Chapter 2 discusses on the past work done by researchers in the major areas of this study which

are on Fragility curves for Memphis region and fragility degradation for different types of bridges,

recovery patterns used and developed in different studies and also studies on network resilience.

Chapter 3 introduces the bridge network considered for analysis and resilience calculations. It

defines the basic details of the bridge network and also validating the model developed. Ground

motion attenuation equations details used for getting the peak ground acceleration (PGA) at each

bridge site locations are also discussed.

Chapter 4 details the fragility degradation modelling for the type of bridge chosen and also

defines the damage states of bridges for a given earthquake during the observed year. Recovery

patterns developed for capacity and speed reductions at different time scenarios for each damage state

are also discussed in detail.

Fig 1-2: Annual Average Snowfall in inches for United States

5

Chapter 5 defines the methodology developed for calculating network functionality and further

obtaining the network resilience. Primary results along with the observations are documented in this

chapter.

Chapter 6 presents the overall conclusion for this work and the effect of aging due to chloride

deterioration in resilience of a bridge network. Major assumptions are also discussed and also the

probable sources of error involved while achieving the objective of this study are detailed. The impact

and future scope of work based on this thesis work is also identified and discussed in this chapter.

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Chapter 2

LITERATURE REVIEW

2.1 Fragility Curve Analysis:

Fragility curves are basically used as a measure of bridge vulnerability due to a hazard or an

extreme event. Bridge vulnerability and network robustness can be calculated mainly through fragility

curves depending on the damage state (k) the bridge experiences such as minor, moderate, major and

complete collapse. For each damage state, the probability of exceeding that particular damage state

for a chosen PGA of a ground motion j (PGAj). Thus, analytically, Fragility curves are developed

using log-normal distributions with median (ck) and standard deviation (ζk) as input fragility

parameters for each damage state. The analytical expression is given as

k

kj

kkj

cPGAcPGAF

ln,,

(1)

As part of the current research, a sample bridge network in Memphis region has been

considered for resilience calculations. Hence, past literature regarding Memphis bridges and typical

bridge configurations in Central and Southeastern United States (CSEUS) have also been studied to

some extent.

Bridges and highway systems in Memphis, Shelby County, Tennessee region have been studied

extensively for probable seismic hazard by various researchers. Hwang et al. (2000) have classified

the bridges in Memphis region into several bridge types using a bridge classification system as

detailed in National Bridge Inventory (NBI)/Federal Highway Administration guidelines and fragility

curves were developed for a representative bridge class for different damage states. Choi et al. (2003)

have also developed a set of fragility curves for four different bridge types - Multi-span simply

supported steel girder bridge(MSSS-SG), Multi-span continuous steel girder bridge(MSC-SG), Multi-

span simply supported pre-stressed concrete girder bridge(MSSS-PSC), Multi-span continuous pre-

stressed concrete girder bridge(MSC-PSC) which are commonly found in Central and Southeastern

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United States (CSEUS). Nielson and DesRoches (2006) have developed fragility curves for major

components of the bridge such as abutments, columns and bearings and their overall contribution to

the bridge system fragility. Nielson (2005) has proposed median PGA values and dispersion values of

system fragilities for nine different bridge types in CSEUS region.

Considering the effects of fragility degradation is also an important factor and has been widely

studied and emphasized by various researchers in literature. Erberik (2011) has studied the effect of

degradation characteristics on seismic performance of simple structural systems and concluded that in

performance-based assessment approaches, analytical modelling of degrading structures should be

carried out carefully. Choe et al. (2008) have developed fragility increment functions for deteriorating

reinforced concrete bridge columns. Also, the developed methodology is demonstrated by presenting

fragilities of a deteriorated bridge column which is typical of current California’s practice. Alipour et

al. (2011) have studied the effect of scour on seismic fragility curves for long-span, medium-span and

short-span bridges. Kumar and Gardoni (2012) have initially developed a probabilistic model to

compute the degraded deformation capacity of flexural reinforced concrete bridge columns as a

function of cumulative low-cycle fatigue damage incurred in past earthquakes. Later, Kumar and

Gardoni (2014) have investigated the seismic degradation of reinforced (RC) concrete highway

bridges and the effect of degradation on the performance and reliability of bridges subject to future

seismic events. Gardoni et al. (2010) and Simon et al. (2010) have studied the effect of aging and

deterioration due to chloride corrosion on seismic fragilities of RC bridges during a bridge lifetime.

Similar studies have been conducted by Zanini et al. (2013) on a small transportation network located

in Italy subjected to environmental deterioration. They have developed fragility curves of the

highway bridges in the network taking into account the corrosion of reinforcing steel and later

analyzed the seismic vulnerability of the transportation network. Sung and Su (2009) have developed

time dependent seismic fragility curves over a span of 60 years in 30 year time interval for neutralized

reinforced concrete bridges. It is worth mentioning that neutralization (carbonation) of concrete has a

major impact in degrading the seismic capacity of any structure over time and even reinforced

8

concrete bridges in particular. Ghosh and Padgett (2010) have formulated time-dependent seismic

fragilities for multi-span continuous reinforced concrete highway bridges.

2.2 Recovery function:

Resilience calculations can vary depending on the type of restoration model considered.

Hence, it is important to choose the right recovery curve based on the kind of damage incurred to the

bridges in a network. Recovery time of a bridge greatly depends on the severity of bridge damage

due to the extreme event. Recovery times for different seismic damage states of highway bridges are

modeled in the seismic loss estimation manual (HAZUS 2003). Recovery times are observed to

follow normal distribution which are developed based on earthquake damage evaluation data acquired

for California (ATC-13 1985). The mean and standard deviations in days for different damage states

are tabulated in Table 2.1.

Table 2.1: Mean and standard deviation of post-event recovery times for highway bridges

Bridge damage state Slight/Minor Moderate Extensive Complete

Mean (Days) 0.6 2.5 75 230

SD (Days) 0.6 2.7 42 110

Developing a suitable mathematical model for the recovery patterns is quite challenging due to

the dependence of bridge recovery process on various factors such as type of damage incurred,

availability of resources at site, level of expertise in dealing with the recovery process and also on

availability of funds. Hence, a variety of post-event bridge recovery patterns such as uniform, step-

wise, triangular, exponential etc. are assumed to all damage states uniformly in order to ease the

process of resilience calculations. Zhou et al. (2010) have proposed a linear recovery model based on

the bridge recovery pattern observed in real-time after severe earthquakes in the past. Venkittaraman

and Banerjee (2014) have also studied seismic resilience considering linear, negative exponential and

trigonometric recovery functions and observed that linear and trigonometric functions are close to

past literature as well as realistic. However, they have considered linear recovery function in their

resilience calculations. In reality, the recovery patterns as well as the number of days required to

9

recover varies with the type of damage state associated with the bridge. In line with that, Deco et al.

(2013) have defined qualitative recovery functions which propose different types of recovery patterns

for different bridge damage states. The current research will look into more mathematical models and

try to develop different recovery patterns for each damage state based on subjective judgments.

2.3 Network Resilience:

As discussed, transportation networks play a very important role in the society and hence, it is

very much essential to get back the network to its original functionality or close to it after an extreme

hazard. In this process of maximizing network resilience, many other objectives also should be taken

into consideration such as minimizing the time required to reach a target functionality level,

minimizing the total cost which includes direct and indirect losses, minimizing the social disruptions

in the community such as psychological problems, separation of families and destroyed social

relationships among people in the community. Bocchini and Frangopol (2012) have proposed a

methodology for the restoration activities associated with bridges of a transportation network severely

damaged due to an earthquake. They have considered two such objectives and tried to optimize these

objectives with a certain trade-off while assessing transportation network resilience. However,

analyzing an actual transportation network analytically with all the parameters taken into

consideration without many assumptions is in itself a challenging task. This requires lot of data with

regarding to the traffic, types of bridges, the detour times, time horizon of investigation, economic

parameters such as maximum total cost available, annual discount rate of money, restoration pace and

times etc. This study tries to develop an analytical process for calculating Network Resilience by

considering a real bridge network in Tennessee, Memphis region.

Mathematically, resilience R can be expressed as shown in the following equation

(Venkittaraman and Banerjee-2014).

dtT

tQR

LCE

E

Tt

t LC

0

0

(2)

10

where t0E represents the time when the extreme event E occurs and TLC is a controlled time set

to evaluate resilience. Q(t) represents system functionality which, in our study, is expressed as system

Total Travel Time(TTT) for the network chosen. Functionality at time ti (𝑄(𝑡𝑖)) is measured

analytically in terms of percentage change in total travel time on day of observation (TTTi) to the

total travel time for the intact model (TTT0).

𝑄(𝑡𝑖) = 100 − (𝑇𝑇𝑇𝑖−𝑇𝑇𝑇0

𝑇𝑇𝑇0∗ 100) (3)

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Chapter 3

BRIDGE NETWORK

The focus of this research is on bridges in a highway transportation network. Frangopol and

Bocchini (2012) defined bridge network as a transportation network in which bridges are the only

elements which can experience structural damage during an extreme event. Damage of bridges in the

event of an extreme natural hazard causes severe disruption to the traffic and can affect the

functionality of a part or entire portion of the highway network.

3.1 Study Area:

The bridge network chosen is from the north-eastern part of Memphis, Shelby County in

Tennessee region. This region is considered primarily because of the available seismic fragility

analysis data of bridges from past studies. Also, this is a region where there is chance of snow as well

as having the probability of experiencing a major earthquake.

Fig 3-1: Map of Memphis region identifying the study area network

Study Area

Network

Source: Travel demand

model documentation by

Memphis MPO

12

Fig 3-2: Zoomed-in-network area for resilience analysis

The induction of chlorine due to the salts into the concrete columns deteriorates the bridge over

its life span which is reflected in the fragility curve patterns as developed by Ghosh and Padgett

(2010). The study area of the network chosen can be seen in figure 3-1 which shows a map of

Memphis with the study area highlighted. Figure 3-2 shows the highlighted area along with the

markings of the bridges and the epicenter of the earthquake. Three types of bridges are observed in

this considered network – Multi-Span Continuous (MSC) concrete bridge, Multi-Span Continuous

(MSC) steel bridge and Multi-Span Simply Supported Concrete Box (MSSS concrete-box).

Among these types, as can be seen in figure 3-2, most of the bridges are of MSC concrete type.

It is worth mentioning that the bridge fragility parameters vary with the type of bridge. It is assumed

that the links which has bridges associated are the only ones which experience damage and failure

Source: Travel demand

model documentation by Memphis MPO

13

and the other links remain intact without any damage. The bridge details have been obtained from

National Bridge Inventory (NBI) database for the selected network and the same is tabulated below in

table 3.1.

Table 3.1: Bridges Details

Bridge

ID LOCATION NBI No

MSC

Concrete

MSC

STEEL

MSSS

concrete-

box

Year of

Construction

1 P

AU

L B

AR

RE

T P

AR

KW

AY

79SR0010045 1 0 0 1996

2 79SR0010046 1 0 0 1996

3 79SR2040015 1 0 0 1996

4 79SR2040016 1 0 0 1996

5 79SR3850033 1 0 0 1996

6 79SR3850034 1 0 0 1996

7 79SR3850035 1 0 0 1996

8 79SR3850036 1 0 0 1996

9 79SR3850029 1 0 0 1996

10 79SR3850030 1 0 0 1996

11 79SR3850037 1 0 0 1996

12 79SR3850038 1 0 0 1996

13 79SR3850039 1 0 0 1996

14 79SR3850040 1 0 0 1996

15 79SR3850045 0 1 0 1996

16 79SR3850046 0 1 0 1996

17 79SR3850047 1 0 0 1996

18 79SR3850048 1 0 0 1996

19 79SR3850079 1 0 0 1996

20 79SR3850006 1 0 0 1997

21 79SR3850005 1 0 0 1997

22 79SR0140071 1 0 0 1997

23 79SR0140072 1 0 0 1997

24 79SR3850001 1 0 0 1981

25 79SR3850002 1 0 0 1981

26 AUSTIN

PEAVY HWY

79SR0140043 1 0 0 2003

27 79SR0140037 0 1 0 1952

28 79SR0140069 0 1 0 1997

29

SINGLETON

AVE 79SR2040009 1 0 0 1977

30

RALEIGH-

MILLINGTON

RD

79SR1030001 0 1 0 1953

31 79008030003 0 1 0 1955

32 79008030004 0 0 1 1976

33 79SR3850003 1 0 0 1980

34 79SR3850004 1 0 0 1980

35 79SR0140067 0 1 0 1997

14

As can be seen from table 3.1, each bridge is identified by a local ID number and that is linked

to the NBI reference number which helps us know all the major details of the bridge such as type of

the bridge, where it is located, year of construction, number of lanes on the bridge etc.

3.2 Developing and Validation of Network Model:

The bridge network consists of 163 nodes with 8 external nodes acting as external

Transportation Analysis Zones (TAZ) for the network. Apart from the 8 external TAZ’s, 48 internal

TAZ’s have been identified for the network. These nodes are marked and are as shown in figure 3-3.

Fig 3-3: Identification of links and nodes in the bridge network

It can be observed from the figure 3-3 that the first physical network node starts at node 113

and the last physical network node is 163. Each closed geometry/shape represents an interior TAZ.

There are 91 physical links in each direction for the network which can be seen connecting two

physical nodes. A user equilibrium model is developed for this chosen network using XXE, Network

Travel Demand Analysis software developed by Mannering F.L. and Washburn S.S. (2008).

15

3.2.1 Node-Link Data:

Node-Link data and origin-destination data are two main sets of input data in XXE which are

considered for developing a model for network analysis. The node-link data consists of from node

which represents the origin, To node which represents the destination nodes, capacity (in vehicles per

hour) for all the node-links considered, length (in miles) of the link connecting the origin and

destination node, free flow speed (in miles per hour) and Free flow travel time (in hours). The length

of each link and number of lanes for each link in both directions is identified using Google Earth

.Based on the type of roadways, the capacities and speed limits are determined.

Table 3.2: Capacities and Speed limits for Memphis region roadways

Roadway type Capacity per

lane(veh/hr)

Speed Limit(mph)

Interstate 2000 65

National highway- divided roads 1500 55

Undivided roads 1500 45

Residential areas 1000 30

Table 3.1 details the capacities and speed limits developed for different types of roadways

present in the network. This data is inputted in XXE software in the Node-Link screen. Once the

capacities, free flow speed (in miles per hour) and length (in miles) for each origin-destination node

are updated in the software, the free flow travel time (in hours) is auto-calculated by the software. The

TAZ nodes which are not a part of the physical network are considered to have a length of 0.25 miles

and speed on 25 miles per hour (mph). This is considered as a safe assumption as these TAZ nodes

are intended to represent local street networks where most of them have speed limits of 25 mph. Also,

in the description tab of the software, each physical link is identified as Network whereas the links

connecting the TAZ node to different nodes in the physical network are identified as Access. A screen

shot of part of the node-link table as inputted in the XXE software is shown in figure 3-4. The

complete node-link data which is used to develop the user-equilibrium model is attached in

Appendix-1.

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Fig 3-4: Node-link data table as inputted in XXE software

3.2.2 Origin-Destination Data:

The origin-destination (O-D) data contains the number of vehicle trips per day during the peak

hour that travel to and from the various TAZs. The main O-D data is obtained from the Memphis

Metropolitan Planning Organization (MPO) authorities. The data obtained contains details of morning

(AM), mid-day (MD), Evening (PM), Off-Peak (OP) hour vehicle trip data for Single occupancy

vehicles (SOV), High-Occupancy vehicles (HOV) and Single Unit (SU) vehicles for all of the

Memphis TAZs.

17

For the network considered in this study, based on the TAZ numbers defined as described in

earlier section, the corresponding TAZ’s involved are identified and the data is linked up with the

new TAZ numbers. For these TAZ’s identified, the total number of vehicle trips for the morning peak

hours for all the SOV, HOV and SU vehicles is aggregated and calculated on hourly basis. Thus the

cumulative O-D data along the vehicle trips during the hour for the TAZ’s identified is developed.

This data is inputted in the XXE software. A screen-shot of part of the data is shown in Figure 3-5.

Complete O-D data can be found in Appendix 2.

Fig 3-5: Origin-Destination data table as inputted in XXE software

18

3.3 Validation of Model:

Any mathematical model is significant and useful only if it is validated and matches real-time

data. Same is the case with this bridge network which is modelled with all the Node-link data and O-

D data in XXE travel demand analysis software. The network model developed is analyzed to obtain

the traffic flow in different links and these flows are checked with the average annual daily traffic

(ADT) obtained from Tennessee DOT website. Since, only the AM peak traffic is considered for our

network analysis, it is safely assumed that it constitutes 6% of the daily traffic data. Hence, the flow

calculated from software analysis for certain links is compared with the flow as observed from ADT

data and the percentage difference between them is calculated and tabulated in Table 3.3. The links

which pass the ± 20% criteria are color coded with green whereas the ones in between 20% -30%

ranges are color coded brownish yellow and the ones with greater than 30% error margin are color

coded red.

Table 3.3: Observed and Calculated flow differences for different links

Fro

m

Node

To

Node

Obser

ved

flow

calculat

ed flow

%

difference

From

Node

To

Node

Obser

ved

flow

calcula

ted

flow

%

differenc

e

113 126 1099 1202 -9.37 132 136 627 592 5.58

115 116 582 503 13.57 133 134 19 4 78.95

118 120 1415 984 30.46 134 116 173 257 -48.55

119 118 79 57 27.85 137 142 79 6 92.41

119 123 260 274 -5.38 139 140 870 734 15.63

120 121 618 531 14.08 140 142 1880 1603 14.73

122 131 11 0 100 143 160 538 505 6.14

123 124 506 363 28.26 145 144 266 257 3.38

124 130 859 1035 -20.49 146 145 439 344 21.64

125 129 625 804 -28.64 147 122 743 740 0.404

126 120 74 12 83.78 150 153 1273 921 27.65

126 127 1380 1130 18.12 152 153 1273 1403 -10.21

129 130 73 0 100 153 154 1585 1231 22.33

129 149 378 381 -0.79 154 155 1795 1270 29.25

130 122 50 41 18 155 156 332 247 25.60

130 129 73 0 100 155 158 1962 2115 -7.80

130 148 827 964 -16.57 157 160 3683 1944 47.22

131 132 573 487 15.01 160 140 3359 1392 58.56

19

As can be seen from the table 3.3, Out of 36 selected links in the network, 75% links have

flows well within ± 20% range error among which there are few links with 100% deviation too.

However, they are very small flows and so the percentage error looks high even though their

difference between them is small and hence error in those links can be ignored. Of the remaining

links, 14% of them have their percentage error in the rage 25%-30%. 3 links out of the 36 links have a

very high percent difference error and they are the links on the interstate (I-40). It is felt that the

possibility in error is mainly due to the insufficiency in O-D trip data in those links which mainly

comes from the external TAZ nodes 4 and 5.

3.4 Attenuation Equations:

For a spatially distributed bridge network, attenuation relationships play a very important role

as they define the exact peak ground acceleration (PGA) at a particular site location due to an

earthquake. This in turn helps us to predict how much damage has been encountered by a bridge and

the corresponding link so that necessary analysis can be done. Since the network chosen is situated in

such region which has low rates of seismicity, there is no much database which gives the details of

earthquake magnitude, epicenter distance and site conditions for the Central and Eastern United

States. Based on the past earthquake history data for the Memphis region, it is observed that Memphis

region has experienced an earthquake of Magnitude 5.5 which is the worst till date. Hence, a synthetic

ground motion is generated for analysis purposes with magnitude of 6.0 at a location close to the

network region which is marked with a red star on the map (figure 3-2) at the beginning of this

chapter.

The PGA’s at the corresponding bridge site locations are calculated based on Silva et al.

(2002) who has developed hard rock attenuation relationships for central and northeastern America

sites. Stuart D. Werner et al. (2006) have incorporated these attenuation equations for CSEUS bridges

into one common platform ‘Risks from Earthquake Damage to Highway Systems’ (REDARS).

20

Table 3.5: Adjusted PGA values at the bridge site locations

Bridge

ID LOCATION NBI No

Distance from

epicenter( Rrup

in km)

Adjusted PGA

(in terms of ‘g’)

1

PA

UL

BA

RR

ET

PA

RK

WA

Y

79SR0010045 17.87 0.23

2 79SR0010046 17.87 0.23

3 79SR2040015 4.81 0.51

4 79SR2040016 4.81 0.51

5 79SR3850033 15.12 0.25

6 79SR3850034 15.12 0.25

7 79SR3850035 16.6 0.24

8 79SR3850036 16.6 0.24

9 79SR3850029 13.77 0.29

10 79SR3850030 13.77 0.29

11 79SR3850037 18.5 0.22

12 79SR3850038 18.5 0.22

13 79SR3850039 19.2 0.21

14 79SR3850040 19.2 0.21

15 79SR3850045 8.84 0.37

16 79SR3850046 8.84 0.37

17 79SR3850047 10.02 0.34

18 79SR3850048 10.02 0.34

19 79SR3850079 5.8 0.47

20 79SR3850006 6.4 0.46

21 79SR3850005 6.4 0.46

22 79SR0140071 7.2 0.43

23 79SR0140072 7.2 0.43

24 79SR3850001 4.9 0.5

25 79SR3850002 4.9 0.5

26 AUSTIN

PEAVY HWY 79SR0140043 6.8 0.44

27 79SR0140037 9.2 0.37

28 79SR0140069 15.89 0.25

29

SINGLETON

AVE 79SR2040009 9.98 0.36

30

RALEIGH-

MILLINGTON

RD

79SR1030001 12.17 0.31

31 79008030003 4.8 0.51

32 79008030004 4.8 0.51

33 79SR3850003 5.3 0.49

34 79SR3850004 5.3 0.49

35 79SR0140067 17.11 0.23

21

Since REDARS has incorporated all the details required for network analysis and the

attenuation equations have also been programmed based on Silva et al (2002), the same network is

modelled in REDARS and the attenuated PGA values for the corresponding magnitude are obtained

at corresponding links and bridges from REDARS. The same details are tabulated in table 3.5. Figure

3-6 also represents a visual graphic of varying PGA’s at different bridge locations of the network as

obtained from REDARS.

Fig 3-6: Ground Motion PGA’s for the network as obtained from REDARS

22

Chapter 4

MODELING OF FRAGILITY DEGRADATION

As can be observed from past literature, most of the researchers have focused either on fragility

degradation for a particular kind of bridge over its life span or on the bridge network resilience based

on pristine fragility parameters. This research combines both these concepts and obtains the network

resilience based on updated bridge fragility parameters.

4.1 Time Variant Quadratic Model:

As detailed in 2.1, lot of work has been done on studying the fragility degradation either due to

series of earthquakes, carbonation or chloride induced corrosion over the bridge life span. In our case,

chloride induced corrosion shall be considered as a major factor which causes degradation in bridges

during their life-span. This corrosion might be due to de-icing salts or also might be due to marine

environment. Fragility degradation due to chloride corrosion as developed in the past literature has

been studied and the model developed by Ghosh and Padgett (2010) shall be considered for our

further analysis primarily because of two reasons. Their study focusses on the CSEUS bridges. They

have developed fragility curves for MSC-SG Bridge which is among the most vulnerable bridge types

in Central United States.

The corresponding fragility curves for each damage state at different points of time are shown

is Figure 4-1. Based on their study, a new ratio ct / cp have been defined which determines the ratio of

median value at any given point of time t (ct) to median value at the pristine time of bridge (cp). The

median values for the fragility curves for each damage state at different points of time over the bridge

life-span as obtained from Ghosh and Padgett (2010) literature are tabulated in Table 4.1.

23

-0.200

0.000

0.200

0.400

0.600

0.800

1.000

0 0.2 0.4 0.6 0.8 1 1.2

P[C

om

ple

te|

PG

A]

PGA (g)

Pristine

25 years

50 years

75 years

100 years

0.000

0.200

0.400

0.600

0.800

1.000

0 0.2 0.4 0.6 0.8 1 1.2

P[E

xte

nsi

ve|

PG

A]

PGA (g)

Pristine

25 years

50 years

75 years

100 years

0.000

0.200

0.400

0.600

0.800

1.000

0 0.2 0.4 0.6 0.8 1 1.2

P[S

ligh

t |

PG

A]

PGA (g)

Pristine

25 years

50 Years

75 years

100 years

0.000

0.200

0.400

0.600

0.800

1.000

0 0.2 0.4 0.6 0.8 1 1.2

P[M

od

era

te|

PG

A]

PGA (g)

Pristine

25 years

50 years

75 years

100 years

Fig 4-1: System level fragility curves at different points of time for each damage states

24

Table 4.1: Median PGA Values at different time scenarios for all damage states

Time in years Slight Damage Moderate Damage Extensive Damage Complete Collapse

tc

p

t

cc

tc

p

t

cc

tc

p

t

cc

tc

p

t

cc

0 0.269 1 0.517 1 0.657 1 0.888 1

25 0.266 0.988848 0.48 0.928433 0.608 0.925419 0.789 0.888514

50 0.261 0.97026 0.467 0.903288 0.596 0.907154 0.788 0.887387

75 0.235 0.873606 0.395 0.764023 0.508 0.773212 0.674 0.759009

100 0.208 0.773234 0.35 0.676983 0.455 0.692542 0.634 0.713964

Ref: Ghosh and Padgett (2010)

These values of ct / cp which have been tabulated in table 3.1 are plotted over the bridge life

span and curve fitting techniques are applied in order to obtain the median fragility values at any

point of time during the bridge life span. Based on data points available, it is observed that a quadratic

model of the form cbtat 2shall be a best fit where a, b, c are the quadratic coefficients as

obtained from the regression analysis and t is the observed time. Fig. 4-2 shows the quadratic fits for

ct / cp ratio over the bridge life span of 100 years in all damage states. The coefficients of the quadratic

fit of ct / cp ratio in all damage states are tabulated in Table 4.2.

Fig 4-2: Polynomial fit of median ratio values in each damage states

0.5

0.6

0.7

0.8

0.9

1

1.1

0 20 40 60 80 100

Ct/

Cp

Bridge life span (years)

SLIGHT DAMAGE

MODERATE DAMAGE

EXTENSIVE DAMAGE

COMPLETE DAMAGE

25

Table 4.2: Coefficients of quadratic interpolation of median values

Quadratic Coefficients for p

t

cc

values

Damage State a b c

Slight -3.00E-05 0.0007 0.9983

Moderate -2.00E-05 -0.0016 0.9959

Extensive -1.00E-05 -0.0016 0.9948

Complete 6.00E-07 -0.0029 0.9909

The dispersion values are directly adopted from Ghosh and Padgett (2010) and since, their

influence on the fragilities is minimal, the variation of dispersion over the life span of a bridge is not

considered. For the corresponding median and standard deviation values, probability of occurrence of

a particular damage state for a given PGA can be calculated using equation 3. Thus, the new set of

updated fragility data points at any point of time can be obtained for any given bridge provided the

year of construction is known.

4.2 Bridge Damage States:

The bridge network considered has 35 bridges which are constructed in different years

spanning from 1953 to 1997 and one bridge reconstructed in the year 2003. The median ratio values

developed in the previous section for each damage state and from the fragility curves, the adjusted

PGA value in each damage state for all the bridges based on their age in the year 2010 and 2050 are

developed. It is observed that all the bridges can be grouped in 10 different groups based on their age.

The median fragility values for these 10 groups in the year 2050 as well as year 2010 are calculated

based on the quadratic co-efficient developed and is tabulated in table 4.3 and table 4.4 respectively.

Once the fragility parameters, median and standard deviation are obtained, the probability of

exceedance for a given PGA in a particular damage state can be obtained from the log-normal

distribution formulation.

26

Table 4.3: Median values of each bridge group in the year 2050

Life span

(yrs) Slight Damage Moderate Damage Extensive Damage Complete Collapse

p

t

cc

tc p

t

cc

tc p

t

cc

tc p

t

cc

tc

53 0.95 0.256 0.85 0.442 0.88 0.579 0.84 0.745

54 0.95 0.255 0.85 0.440 0.88 0.578 0.84 0.742

69 0.90 0.243 0.79 0.409 0.84 0.550 0.79 0.705

70 0.90 0.242 0.79 0.406 0.83 0.548 0.79 0.702

74 0.89 0.238 0.77 0.397 0.82 0.540 0.78 0.692

95 0.79 0.214 0.66 0.343 0.75 0.494 0.72 0.640

97 0.78 0.211 0.65 0.337 0.75 0.490 0.72 0.635

98 0.78 0.209 0.65 0.335 0.74 0.487 0.71 0.633

73 0.89 0.239 0.77 0.399 0.82 0.542 0.78 0.695

47 0.96 0.260 0.88 0.453 0.90 0.590 0.86 0.760

Table 4.4: Median values of each bridge group in the year 2010

Life span

(yrs) Slight Damage Moderate Damage Extensive Damage Complete Collapse

p

t

cc

tc p

t

cc

tc p

t

cc

tc p

t

cc

tc

7 1.00 0.269 0.98 0.509 0.98 0.646 0.97 0.862

13 1.00 0.270 0.97 0.502 0.97 0.639 0.95 0.847

14 1.00 0.270 0.97 0.501 0.97 0.638 0.95 0.844

29 0.99 0.267 0.93 0.482 0.94 0.618 0.91 0.806

30 0.99 0.267 0.93 0.481 0.94 0.616 0.90 0.803

34 0.99 0.266 0.92 0.475 0.93 0.610 0.89 0.793

55 0.95 0.254 0.85 0.438 0.88 0.576 0.83 0.740

57 0.94 0.253 0.84 0.434 0.87 0.572 0.83 0.735

58 0.94 0.252 0.84 0.432 0.87 0.571 0.82 0.732

33 0.99 0.266 0.92 0.476 0.93 0.612 0.90 0.796

27

Based on the probabilities of exceedance obtained in each damage state for the corresponding

PGA, the actual damage state for each bridge shall be determined based on the rules identified below:

1. The damage state of the bridge corresponds to that damage state which has a highest

probability of exceedance compared to all other damage states.

2. If a lower damage state has 100% probability of occurrence and the next higher damage state

has a probability of exceedance 50% or more, then there is a probability of bridge

experiencing the higher damage state than the lower damage state.

3. If two or more damage states have the same probability of exceedance, then the bridge shall

fail in the highest damage state among all the damage states available.

Based on the rules mentioned above, the damage state of each bridge in year 2010 as well as

2050 are identified and tabulated in table 4.5. They are color coded to distinguish one damage state

from the other.

Table 4.5: Damage state for the Bridges and links associated in year 2010 and 2050

Brid

-ge

ID LOCATION NBI No

link/node

associated

Year of

Constr-

uction

Age

(2010)

Damage

State-2010

Age

(2050)

Damage

State-

2050

1

PA

UL

BA

RR

ET

PA

RK

WA

Y

79SR0010045 139 1996 14 Minor 54 Minor

2 79SR0010046 139 1996 14 Minor 54 Minor

3 79SR2040015 124 1996 14 Moderate 54 Major

4 79SR2040016 124 1996 14 Moderate 54 Major

5 79SR3850033 136-139 1996 14 Minor 54 Minor

6 79SR3850034 136-139 1996 14 Minor 54 Minor

7 79SR3850035 136-139 1996 14 Minor 54 Minor

8 79SR3850036 136-139 1996 14 Minor 54 Minor

9 79SR3850029 136 1996 14 Minor 54 Minor

10 79SR3850030 136 1996 14 Minor 54 Minor

11 79SR3850037 139-140 1996 14 Minor 54 Minor

12 79SR3850038 139-140 1996 14 Minor 54 Minor

13 79SR3850039 139-140 1996 14 Minor 54 Minor

14 79SR3850040 139-140 1996 14 Minor 54 Minor

15 79SR3850045 131-132 1996 14 Minor 54 Minor

16 79SR3850046 131-132 1996 14 Minor 54 Minor

17 79SR3850047 132 1996 14 Minor 54 Minor

18 79SR3850048 132 1996 14 Minor 54 Minor

19 79SR3850079 123-124 1996 14 Moderate 54 Moderate

20 79SR3850006 123 1997 13 Moderate 53 Moderate

28

Table 4.5: Damage state for the Bridges and links associated in year 2010 and 2050(cntd.)

Brid

ge

ID LOCATION NBI No

link/node

associated

Year of

Constr

uction

Age

(2010)

Damage

State-2010

Age

(2050)

Damage

State-2050

21 P

AU

L B

AR

RE

T

PA

RK

WA

Y 79SR3850005 123 1997 13 Moderate 53 Moderate



24 79SR3850001 124-125 1981 29 Moderate 69 Major


26 AUSTIN

PEAVY

HWY

79SR0140043 120-121 2003 7 Moderate 47 Moderate

27 79SR0140037 122-147 1952 58 Moderate 98 Moderate

28 79SR0140069 151-152 1997 13 Minor 53 Minor

29

SINGLETON

AVE 79SR2040009 130-148 1977 33 Minor 73 Moderate

30

RALEIGH-

MILLINGTO

N RD

79SR1030001 129-149 1953 57 Minor 97 Moderate

31 79008030003 125-127 1955 55 Major 95 Major

32 79008030004 125-127 1976 34 Moderate 74 Major



35 79SR0140067 151 1997 13 Minor 53 Minor

4.3 Recovery Patterns:

As described in literature review, multiple recovery patterns have been adopted and used for

resilience calculations. Deco et al. (2013) have defined qualitative recovery patterns associated with

different damage states and recovery options. Also, HAZUS 2011 gives mean and standard deviation

of restoration functions for each damage state and these values are tabulated in table 4.6.

Table 4.6: Mean and Standard deviation of restoration functions for each damage state

Damage State Highway Bridges Restoration functions

Mean (Days) SD (Days)

Slight/ Minor 0.6 0.6

Moderate 2.5 2.7

Extensive 75 42

Complete 230 110

29

0

25

50

75

100

0 6 12 18 24

Cap

aci

ty (

%)

Time (in hours)

Capacity -

Minor Damage

0

20

40

60

80

100

0 6 12 18 24

Sp

eed

re

du

ctio

n (

%)

Time (Days)

Speed reduction - Minor damage

0

20

40

60

80

100

120

0 1 2 3 4 5 6

Ca

pa

city

(%

)

Time (days)

Capacity - Moderate damage

0

5

10

15

20

25

0 2 4 6

Sp

eed

red

uct

ion

(d

el U

)

Time (days)

Speed reduction - Moderate damage

Fig 4-3: Capacity and Speed reduction values for minor and moderate damage states

30

0

20

40

60

80

100

0 20 40 60 80 100

Cap

aci

ty (

%)

Time (days)

Capacity - Major damage

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Sp

eed

re

du

ctio

n (

%)

Time (days)

Speed reduction - Major damage

0

25

50

75

100

0 50 100 150 200

Ca

pa

city

(%

)

Time (days)

Capacity-Complete collapse

0

20

40

60

80

100

120

0 50 100 150 200 250

Sp

eed

re

du

ctio

n (

%)

Time (Days)

Speed reduction- Complete Collapse

Fig 4-4: Capacity and Speed reduction values for major and collapse damage states

31

Based on the mean values from table 4.6 and also the qualitative recovery functions adopted by

Deco et. al. (2013) for each damage state, Capacity per lane and speed reduction values are developed

for all the damage states to take into account the recovery pattern as shown in figures 4-3 and 4-4.

These parameters are modelled as step functions to match with realistic patterns. It is clear that in

case of any maintenance or repair work on roads/ bridges, the speed limits get reduced by 10-20 mph

depending on the kind of repair work and close some lanes which relates to reduction in capacity.

However, these restrictions are kept throughout the stretch of the work without any change in speed

limits and capacity changes. Hence, step functions which represent similar recovery pattern as

described by Deco et.al. (2013) have been considered in this research for calculating network

resilience.

32

Chapter 5

NETWORK RESILIENCE

5.1 Methodology:

Once the fragility parameters are obtained and the bridge damage states are defined as detailed in

chapter 4, recovery patterns are applied for each scenario and network functionality has been determined.

Network functionality is defined based on the total travel time of the network in vehicle-hours which can

be obtained from XXE software once the analysis is run. As the system recovers, the total travel time for

the network chosen reduces due to increase in capacity to original capacities and increase in speed limits to

free flow speed.

In order to define the capacity and speed limits based on recovery patterns for the associated links,

the damage state of the links are to be determined. As discussed earlier, highway bridges are a part of the

road and hence, the links are associated to the bridges present in that link. Hence, the failure of the bridges

will cause the failure of the associated links in the network. Also, failure of a bridge present at the

intersection of multiple links associated, all the links associated shall have the same damage state of the

bridge present at the intersection. In addition, in case of multiple bridges present on the link have multiple

damage states, then the associated link shall be considered experiencing the worst case damage state.

Accordingly, the links damage states have been determined for year 2010 and year 2050 and are

tabulated in table 5.1. It is observed that for a magnitude of 6 in the year 2010, there is only one link which

experiences a major damage while 50% of the links experience Moderate damage. The remaining

experience minor damage leaving 5 links which have no damage associated due to the absence of bridges

in those links. For the year 2050 with the network experiencing same magnitude earthquake, it is observed

that 5 links experience major damage and 7 of them experiencing moderate damage state. Six of the links

experience minor damage and the remaining links are not associated with any bridge and hence experience

no damage. The same observations can be found in table 5.1.

33

Table 5.1: Damage states of links in year 2010 and 2050 for an EQ of magnitude 6

Links

Damage

State -2010

Damage

State -2050

125-127 Major Major

124-125 Moderate Major


123-121 Moderate Moderate


131-132 Minor Minor

132-136 Minor Minor

136-139 Minor Minor

139-140 Minor Minor

151-152 Minor Minor

152-147 No damage No damage








129-149 Minor Moderate

149-151 Minor Minor


130-148 Minor Moderate


5.2 Results:

Once the damage states are defined for the links, the network travel time has been studied at different

time scenarios Day 0 (just immediately after the event), after 6hrs of the extreme event, Day 1, Day 3, Day

7, Day 15, Day 30, Day 60, Day 120. For each time scenario, based on the damage state, the corresponding

capacity and speed reductions values are updated in the software and the total travel time in vehicle hours

is obtained. Functionality at time ti (𝑄(𝑡𝑖)) is determined in terms of percentage change in total travel time

on day of observation (TTTi) to the total travel time for the intact model (TTT0).

𝑄(𝑡𝑖) = 100 − (𝑇𝑇𝑇𝑖−𝑇𝑇𝑇0

𝑇𝑇𝑇0∗ 100) (4)

34

75.00

80.00

85.00

90.00

95.00

100.00

0 10 20 30 40 50 60 70 80 90 100 110 120

Fun

ctio

nal

ity

(%)

Time ( days)

FUNCTIONALITY CURVE FOR DETERMINING RESILIENCE ( YEAR 2010)

Table 5.2: Network Functionality for year 2010 and 2050 at different time scenarios

YEAR 2010 YEAR 2050

Time

(DAYS)

Total travel

time (veh-

hrs)

Functionality

(%)

Total travel

time (veh-hrs)

Functionality

(%)

0(before the

event) 2021.5 100.00 2021.5 100.00

0 (after the

event) 2417.7 80.40 2458.7 78.37

0.5 2404.1 81.07 2444.8 79.06

1 2374.5 82.54 2425.5 80.01

3 2276.5 87.39 2317.2 85.37

7 2242.8 89.05 2268.7 87.77

15 2242.8 89.05 2268.7 87.77

30 2221.9 90.09 2230.7 89.65

60 2037.2 99.22 2050.8 98.55

120 2021.5 100.00 2021.5 100.00

Table 5.2 shows the functionality values at different time scenarios and also the total travel time in

vehicle hours for year 2010 and 2050. The same has been plotted and change in functionality with time can

be seen in figure 5-1 and figure 5-2.

Fig 5-1: Functionality curve for determining resilience in year 2010

35

75.00

80.00

85.00

90.00

95.00

100.00

0 10 20 30 40 50 60 70 80 90 100 110 120

Fun

ctio

nal

ity

(%)

Time ( days)

FUNCTIONALITY CURVE FOR DETERMINING RESILIENCE ( YEAR 2050)

5.3 Observations:

From the functionality plots, as defined in introduction chapter, the area under the functionality

versus time plot according to equation 1 shall give resilience for the corresponding scenario. Trapezoidal

rule has been applied by dividing the region into strips of area between two time scenarios for year 2010

and 2050. Since resilience is normalized based on the controlled time set as per the mathematical

definition, resilience has been calculated at different controlled time scenarios and the same is tabulated in

table 5.3.

Controlled time

set (TLC)

in days

Resilience

(Year 2010)

in %

Resilience

(Year 2050)

in %

3 83.73 81.5

15 87.77 86.2

30 88.67 87.45

60 91.66 90.78

120 95.64 95.03

Fig 5-2: Functionality curve for determining resilience in year 2050

Table 5.3: Network Resilience for year 2010 and 2050 at different controlled time sets

36

Also, for different time scenarios during and after the extreme event for which the total travel time

and delay are calculated, the volume demand to capacity (v/c) ratios for all links are obtained from XXE

software. This v/c ratio helps in identifying the performance of a chosen link depending on the ratio value.

If the ratio is close to 0, then there is very much less demand in the link than the capacity it has and if the

ratio is equal to 1, then the traffic demand is same as the capacity of the link. The same data is consolidated

and percentage number of links having a v/c ratio between 0 and 0.5 and percentage number of links

having v/c ratio of 0.5 to 1 and those having greater than 1 are obtained and tabulated for year 2010 and

2050 at different controlled time set scenarios. This primarily can be used as a measure of functionality in

calculating resilience of the network and understanding how robust is the transportation network system.

YEAR 2010 YEAR 2050

Time

%

number

of links

for

0≤v/c≤0.5

%

number

of links

for

0.5≤v/c≤1

%

number

of links

for v/c≥1

%

number

of links

for

0≤v/c≤0.5

%

number of

links for

0.5≤v/c≤1

%

number of

links for

v/c≥1 (DAYS)

0(before the event) 97.70 2.30 0.00 97.70 2.30 0.00

0 (after the event) 93.10 5.17 1.72 87.36 5.75 6.90

0.5 91.95 5.75 2.30 87.36 5.75 6.90

1 90.23 7.47 2.30 86.21 6.90 6.90

3 92.53 6.32 1.15 87.93 6.32 5.75

7 93.68 5.17 1.15 89.08 5.17 5.75

15 93.68 5.17 1.15 89.08 5.17 5.75

30 95.40 3.45 1.15 93.10 5.75 1.15

60 97.70 2.30 0.00 98.28 1.72 0.00

120 97.70 2.30 0.00 97.70 2.30 0.00

Table 5.4: Percentage number of links in different v/c ratios for year 2010 and 2050

37

Chapter 6

Conclusions and Future Scope of work

A user equilibrium model for the bridge network in Tennessee region with 35 bridges is developed.

Fragility patterns for the bridges considering the chloride induced corrosion are developed at different ages

of the bridge. The network is analyzed for an earthquake of magnitude 6 and adjusted PGA’s are calculated

at different bridge site locations using the attenuation equations. Recovery patterns are developed for

different damage states and at different time scenarios, the functionality of the network in terms of total

travel time is calculated for years 2010 and 2050. Further, Resilience is calculated for the network in years

2010 and 2050 and compared at different controlled time sets.

It is observed that aging due to chloride deterioration has an adverse impact on seismic resilience of

bridge network. Also, bridge network resilience is affected by earthquake magnitude, location of bridges

from the epicenter and thus, they play an important role in deciding the damage state of the bridge as well

as associated links. In reality, all of the roadways experience damage in the event of an earthquake.

However, for simplifying the analysis, it is assumed that only the links with bridges shall experience

damage and the rest of the links in the network remain undamaged. This can be improved in future to

create a realistic network analysis and understand the robustness of the network.

Also, while validating the model, it is observed that the flow in few links has approximately 50%

error when compared to original flow as given by Tennessee DOT. One of the primary reasons for this is

due to the insufficient O-D data and number of trips in those TAZ’s considered. This can be improved by

considering more number of TAZ’s while developing the O-D input table. In this study, time-dependent

fragility parameters considered are only of the MSC concrete bridge and since, most of the bridges are of

this type, same fragility parameters are used for analysis and to define the bridge damage states. However,

this can be changed to take into consideration type of bridge also.

38

References

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Bocchini. P., Frangapol. D. M., (2012). “Restoration of Bridge Networks after an Earthquake: Multicriteria

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Choi. E., Desroches. R., Nielson. B., (2003). “Seismic Fragility of Typical Bridges in Moderate Seismic

Zones”. Engineering Structures 26:187-199

Deco. A., Bocchini. P., Frangapol. D. M., (2013). “A probabilistic approach for the prediction of seismic

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Erberik. M. A., (2011). “Importance of Degrading Behavior for Seismic Performance Evaluation of Simple

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Hwang. H., Jernigan. J. B., Lin. Y. (2000). “Evaluation of Seismic Damage to Memphis Bridges and

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Nielson. B. G., (2005). “Analytical Fragility Curves for Highway Bridges in Moderate Seismic Zones”.

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Nielson. B. G., Desroches. R., (2006). “Seismic fragility methodology for highway bridges using a

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REDARS 3: User Guide. Technical manual (2013)

Silva W., Gregor N., Darragh R., (2002). “Development of Regional hard rock attenuation relations for

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Simon. J., Bracci. J. M., Gardoni. P., (2010). “Seismic Response and Fragility of Deteriorated Reinforced

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“REDARS 2: Methodology and Software for Seismic Risk Analysis of Highway Systems.” MCEER-06-

SP08

XXE: User Guide. Mannering F.L., Washburn S.S., (2008)

Zanini. M. A., Pellegrino. C., Morbin. R., Modena. C., (2013). “Seismic Vulnerability of Bridges in

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40

Appendix A:

Node-Link table

41

From Node

To Node

Capacity (veh/hr)

Length (mi)

Free flow speed (mi/hr)

Free flow travel time(h) Description

1 113 1000 1 25 0.04 Access

1 114 1000 1 25 0.04 Access

1 115 1000 1 25 0.04 Access

2 116 1000 1 25 0.04 Access

3 116 1000 1 25 0.04 Access

3 138 1000 1 25 0.04 Access

4 138 1000 1 25 0.04 Access

4 142 1000 1 25 0.04 Access

4 163 1000 1 25 0.04 Access

5 157 1000 1 25 0.04 Access

5 158 1000 1 25 0.04 Access

5 161 1000 1 25 0.04 Access

5 162 1000 1 25 0.04 Access

5 163 1000 1 25 0.04 Access

6 151 1000 1 25 0.04 Access

6 153 1000 1 25 0.04 Access

6 154 1000 1 25 0.04 Access

6 155 1000 1 25 0.04 Access

7 128 1000 1 25 0.04 Access

7 149 1000 1 25 0.04 Access

7 150 1000 1 25 0.04 Access

8 126 1000 1 25 0.04 Access

8 127 1000 1 25 0.04 Access

8 128 1000 1 25 0.04 Access

9 114 1000 0.25 25 0.01 Access

9 115 1000 0.25 25 0.01 Access

9 116 1000 0.25 25 0.01 Access

9 117 1000 0.25 25 0.01 Access

10 114 1000 0.25 25 0.01 Access

10 117 1000 0.25 25 0.01 Access

10 118 1000 0.25 25 0.01 Access

10 119 1000 0.25 25 0.01 Access

11 113 1000 0.25 25 0.01 Access

11 114 1000 0.25 25 0.01 Access

11 119 1000 0.25 25 0.01 Access

12 113 1000 0.25 25 0.01 Access

12 119 1000 0.25 25 0.01 Access

42

From Node

To Node

Capacity (veh/hr)

Length (mi)



12 126 1000 0.25 25 0.01 Access

13 118 1000 0.25 25 0.01 Access

13 119 1000 0.25 25 0.01 Access

13 120 1000 0.25 25 0.01 Access

14 118 1000 0.25 25 0.01 Access

14 120 1000 0.25 25 0.01 Access

14 133 1000 0.25 25 0.01 Access

14 134 1000 0.25 25 0.01 Access

15 116 1000 0.25 25 0.01 Access

15 117 1000 0.25 25 0.01 Access

15 118 1000 0.25 25 0.01 Access

15 134 1000 0.25 25 0.01 Access

16 116 1000 0.25 25 0.01 Access

16 134 1000 0.25 25 0.01 Access

16 135 1000 0.25 25 0.01 Access

16 137 1000 0.25 25 0.01 Access

16 138 1000 0.25 25 0.01 Access

17 133 1000 0.25 25 0.01 Access

17 134 1000 0.25 25 0.01 Access

17 135 1000 0.25 25 0.01 Access

18 132 1000 0.25 25 0.01 Access

18 133 1000 0.25 25 0.01 Access

18 135 1000 0.25 25 0.01 Access

18 136 1000 0.25 25 0.01 Access

19 120 1000 0.25 25 0.01 Access

19 131 1000 0.25 25 0.01 Access

19 132 1000 0.25 25 0.01 Access

19 133 1000 0.25 25 0.01 Access

20 120 1000 0.25 25 0.01 Access

20 121 1000 0.25 25 0.01 Access

20 131 1000 0.25 25 0.01 Access

21 120 1000 0.25 25 0.01 Access

21 121 1000 0.25 25 0.01 Access

21 123 1000 0.25 25 0.01 Access

22 123 1000 0.25 25 0.01 Access

22 124 1000 0.25 25 0.01 Access

22 125 1000 0.25 25 0.01 Access

22 126 1000 0.25 25 0.01 Access

23 122 1000 0.25 25 0.01 Access

23 131 1000 0.25 25 0.01 Access

43

From Node

To Node

Capacity (veh/hr)

Length (mi)



23 132 1000 0.25 25 0.01 Access

23 144 1000 0.25 25 0.01 Access

23 145 1000 0.25 25 0.01 Access

23 146 1000 0.25 25 0.01 Access

23 147 1000 0.25 25 0.01 Access

24 125 1000 0.25 25 0.01 Access

24 126 1000 0.25 25 0.01 Access

24 127 1000 0.25 25 0.01 Access

25 125 1000 0.25 25 0.01 Access

25 127 1000 0.25 25 0.01 Access

25 128 1000 0.25 25 0.01 Access

25 129 1000 0.25 25 0.01 Access

26 124 1000 0.25 25 0.01 Access

26 125 1000 0.25 25 0.01 Access

26 129 1000 0.25 25 0.01 Access

26 130 1000 0.25 25 0.01 Access

27 121 1000 0.25 25 0.01 Access

27 122 1000 0.25 25 0.01 Access

27 123 1000 0.25 25 0.01 Access

27 124 1000 0.25 25 0.01 Access

27 130 1000 0.25 25 0.01 Access

28 121 1000 0.25 25 0.01 Access

28 122 1000 0.25 25 0.01 Access

28 131 1000 0.25 25 0.01 Access

29 135 1000 0.25 25 0.01 Access

29 136 1000 0.25 25 0.01 Access

29 137 1000 0.25 25 0.01 Access

29 139 1000 0.25 25 0.01 Access

30 132 1000 0.25 25 0.01 Access

30 136 1000 0.25 25 0.01 Access

30 139 1000 0.25 25 0.01 Access

30 143 1000 0.25 25 0.01 Access

31 137 1000 0.25 25 0.01 Access

32 137 1000 0.25 25 0.01 Access

32 138 1000 0.25 25 0.01 Access

33 138 1000 0.25 25 0.01 Access

33 142 1000 0.25 25 0.01 Access

34 141 1000 0.25 25 0.01 Access

35 139 1000 0.25 25 0.01 Access

35 140 1000 0.25 25 0.01 Access

44

From Node

To Node

Capacity (veh/hr)

Length (mi)



36 139 1000 0.25 25 0.01 Access

36 140 1000 0.25 25 0.01 Access

37 143 1000 0.25 25 0.01 Access

37 160 1000 0.25 25 0.01 Access

38 143 1000 0.25 25 0.01 Access

38 158 1000 0.25 25 0.01 Access

38 159 1000 0.25 25 0.01 Access

38 160 1000 0.25 25 0.01 Access

39 143 1000 0.25 25 0.01 Access

39 159 1000 0.25 25 0.01 Access

40 144 1000 0.25 25 0.01 Access

40 156 1000 0.25 25 0.01 Access

40 159 1000 0.25 25 0.01 Access

41 144 1000 0.25 25 0.01 Access

41 145 1000 0.25 25 0.01 Access

41 155 1000 0.25 25 0.01 Access

41 156 1000 0.25 25 0.01 Access

42 145 1000 0.25 25 0.01 Access

42 146 1000 0.25 25 0.01 Access

42 154 1000 0.25 25 0.01 Access

42 155 1000 0.25 25 0.01 Access

43 122 1000 0.25 25 0.01 Access

43 130 1000 0.25 25 0.01 Access

43 146 1000 0.25 25 0.01 Access

43 148 1000 0.25 25 0.01 Access

44 129 1000 0.25 25 0.01 Access

44 130 1000 0.25 25 0.01 Access

44 148 1000 0.25 25 0.01 Access

44 149 1000 0.25 25 0.01 Access

45 147 1000 0.25 25 0.01 Access

45 148 1000 0.25 25 0.01 Access

45 152 1000 0.25 25 0.01 Access

46 148 1000 0.25 25 0.01 Access

46 149 1000 0.25 25 0.01 Access

46 152 1000 0.25 25 0.01 Access

47 149 1000 0.25 25 0.01 Access

47 151 1000 0.25 25 0.01 Access

48 149 1000 0.25 25 0.01 Access

48 150 1000 0.25 25 0.01 Access

49 152 1000 0.25 25 0.01 Access

45

From Node

To Node

Capacity (veh/hr)

Length (mi)



49 153 1000 0.25 25 0.01 Access

50 147 1000 0.25 25 0.01 Access

50 152 1000 0.25 25 0.01 Access

50 153 1000 0.25 25 0.01 Access

50 154 1000 0.25 25 0.01 Access

51 150 1000 0.25 25 0.01 Access

51 151 1000 0.25 25 0.01 Access

52 155 1000 0.25 25 0.01 Access

52 156 1000 0.25 25 0.01 Access

52 157 1000 0.25 25 0.01 Access

53 156 1000 0.25 25 0.01 Access

53 157 1000 0.25 25 0.01 Access

53 158 1000 0.25 25 0.01 Access

53 159 1000 0.25 25 0.01 Access

54 158 1000 0.25 25 0.01 Access

54 160 1000 0.25 25 0.01 Access

54 161 1000 0.25 25 0.01 Access

55 160 1000 0.25 25 0.01 Access

55 161 1000 0.25 25 0.01 Access

55 162 1000 0.25 25 0.01 Access

56 140 1000 0.25 25 0.01 Access

56 141 1000 0.25 25 0.01 Access

56 142 1000 0.25 25 0.01 Access

56 162 1000 0.25 25 0.01 Access

56 163 1000 0.25 25 0.01 Access

57 113 1 1 25 0.04 Dummy

57 114 1 1 25 0.04 Dummy

57 115 1 1 25 0.04 Dummy

58 116 1 1 25 0.04 Dummy

59 116 1 1 25 0.04 Dummy

59 138 1 1 25 0.04 Dummy

60 138 1 1 25 0.04 Dummy

60 142 1 1 25 0.04 Dummy

60 163 1 1 25 0.04 Dummy

61 157 1 1 25 0.04 Dummy

61 158 1 1 25 0.04 Dummy

61 162 1 1 25 0.04 Dummy

61 163 1 1 25 0.04 Dummy

62 151 1 1 25 0.04 Dummy

62 153 1 1 25 0.04 Dummy

46

From Node

To Node

Capacity (veh/hr)

Length (mi)



62 154 1 1 25 0.04 Dummy

62 155 1 1 25 0.04 Dummy

63 149 1 1 25 0.04 Dummy

63 150 1 1 25 0.04 Dummy

64 126 1 1 25 0.04 Dummy

64 127 1 1 25 0.04 Dummy

64 128 1 1 25 0.04 Dummy

65 114 1 0.25 25 0.01 Dummy

65 115 1 0.25 25 0.01 Dummy

65 116 1 0.25 25 0.01 Dummy

65 117 1 0.25 25 0.01 Dummy

66 114 1 0.25 25 0.01 Dummy

66 117 1 0.25 25 0.01 Dummy

66 118 1 0.25 25 0.01 Dummy

66 119 1 0.25 25 0.01 Dummy

67 113 1 0.25 25 0.01 Dummy

67 114 1 0.25 25 0.01 Dummy

67 119 1 0.25 25 0.01 Dummy

68 113 1 0.25 25 0.01 Dummy

68 119 1 0.25 25 0.01 Dummy

68 126 1 0.25 25 0.01 Dummy

69 118 1 0.25 25 0.01 Dummy

69 119 1 0.25 25 0.01 Dummy

69 120 1 0.25 25 0.01 Dummy

70 118 1 0.25 25 0.01 Dummy

70 120 1 0.25 25 0.01 Dummy

70 133 1 0.25 25 0.01 Dummy

70 134 1 0.25 25 0.01 Dummy

71 116 1 0.25 25 0.01 Dummy

71 117 1 0.25 25 0.01 Dummy

71 118 1 0.25 25 0.01 Dummy

71 134 1 0.25 25 0.01 Dummy

72 116 1 0.25 25 0.01 Dummy

72 134 1 0.25 25 0.01 Dummy

72 135 1 0.25 25 0.01 Dummy

72 138 1 0.25 25 0.01 Dummy

73 133 1 0.25 25 0.01 Dummy

73 134 1 0.25 25 0.01 Dummy

73 135 1 0.25 25 0.01 Dummy

74 132 1 0.25 25 0.01 Dummy

47

From Node

To Node

Capacity (veh/hr)

Length (mi)



74 133 1 0.25 25 0.01 Dummy

74 135 1 0.25 25 0.01 Dummy

74 136 1 0.25 25 0.01 Dummy

75 131 1 0.25 25 0.01 Dummy

75 132 1 0.25 25 0.01 Dummy

75 133 1 0.25 25 0.01 Dummy

76 120 1 0.25 25 0.01 Dummy

76 121 1 0.25 25 0.01 Dummy

76 131 1 0.25 25 0.01 Dummy

77 120 1 0.25 25 0.01 Dummy

77 121 1 0.25 25 0.01 Dummy

77 123 1 0.25 25 0.01 Dummy

78 123 1 0.25 25 0.01 Dummy

78 124 1 0.25 25 0.01 Dummy

78 125 1 0.25 25 0.01 Dummy

78 126 1 0.25 25 0.01 Dummy

79 122 1 0.25 25 0.01 Dummy

79 131 1 0.25 25 0.01 Dummy

79 132 1 0.25 25 0.01 Dummy

79 144 1 0.25 25 0.01 Dummy

79 145 1 0.25 25 0.01 Dummy

79 146 1 0.25 25 0.01 Dummy

79 147 1 0.25 25 0.01 Dummy

80 125 1 0.25 25 0.01 Dummy

80 126 1 0.25 25 0.01 Dummy

80 127 1 0.25 25 0.01 Dummy

81 125 1 0.25 25 0.01 Dummy

81 127 1 0.25 25 0.01 Dummy

81 128 1 0.25 25 0.01 Dummy

81 129 1 0.25 25 0.01 Dummy

82 124 1 0.25 25 0.01 Dummy

82 125 1 0.25 25 0.01 Dummy

82 129 1 0.25 25 0.01 Dummy

82 130 1 0.25 25 0.01 Dummy

83 122 1 0.25 25 0.01 Dummy

83 123 1 0.25 25 0.01 Dummy

83 124 1 0.25 25 0.01 Dummy

83 130 1 0.25 25 0.01 Dummy

84 121 1 0.25 25 0.01 Dummy

84 122 1 0.25 25 0.01 Dummy

48

From Node

To Node

Capacity (veh/hr)

Length (mi)



84 131 1 0.25 25 0.01 Dummy

85 135 1 0.25 25 0.01 Dummy

85 136 1 0.25 25 0.01 Dummy

85 137 1 0.25 25 0.01 Dummy

86 143 1 0.25 25 0.01 Dummy

87 137 1 0.25 25 0.01 Dummy

88 137 1 0.25 25 0.01 Dummy

88 138 1 0.25 25 0.01 Dummy

89 138 1 0.25 25 0.01 Dummy

89 142 1 0.25 25 0.01 Dummy

90 141 1 0.25 25 0.01 Dummy

90 142 1 0.25 25 0.01 Dummy

91 139 1 0.25 25 0.01 Dummy

91 140 1 0.25 25 0.01 Dummy

91 141 1 0.25 25 0.01 Dummy

92 139 1 0.25 25 0.01 Dummy

92 140 1 0.25 25 0.01 Dummy

93 143 1 0.25 25 0.01 Dummy

93 160 1 0.25 25 0.01 Dummy

94 143 1 0.25 25 0.01 Dummy

94 158 1 0.25 25 0.01 Dummy

94 159 1 0.25 25 0.01 Dummy

94 160 1 0.25 25 0.01 Dummy

95 143 1 0.25 25 0.01 Dummy

95 159 1 0.25 25 0.01 Dummy

96 144 1 0.25 25 0.01 Dummy

96 156 1 0.25 25 0.01 Dummy

96 159 1 0.25 25 0.01 Dummy

97 144 1 0.25 25 0.01 Dummy

97 145 1 0.25 25 0.01 Dummy

97 156 1 0.25 25 0.01 Dummy

98 145 1 0.25 25 0.01 Dummy

98 146 1 0.25 25 0.01 Dummy

98 154 1 0.25 25 0.01 Dummy

98 155 1 0.25 25 0.01 Dummy

99 122 1 0.25 25 0.01 Dummy

99 130 1 0.25 25 0.01 Dummy

99 146 1 0.25 25 0.01 Dummy

99 148 1 0.25 25 0.01 Dummy

100 129 1 0.25 25 0.01 Dummy

49

From Node

To Node

Capacity (veh/hr)

Length (mi)



100 130 1 0.25 25 0.01 Dummy

100 148 1 0.25 25 0.01 Dummy

101 147 1 0.25 25 0.01 Dummy

101 148 1 0.25 25 0.01 Dummy

101 152 1 0.25 25 0.01 Dummy

102 148 1 0.25 25 0.01 Dummy

102 149 1 0.25 25 0.01 Dummy

102 152 1 0.25 25 0.01 Dummy

103 149 1 0.25 25 0.01 Dummy

103 151 1 0.25 25 0.01 Dummy

103 152 1 0.25 25 0.01 Dummy

104 149 1 0.25 25 0.01 Dummy

104 150 1 0.25 25 0.01 Dummy

105 152 1 0.25 25 0.01 Dummy

105 153 1 0.25 25 0.01 Dummy

106 146 1 0.25 25 0.01 Dummy

106 147 1 0.25 25 0.01 Dummy

106 152 1 0.25 25 0.01 Dummy

106 153 1 0.25 25 0.01 Dummy

106 154 1 0.25 25 0.01 Dummy

107 150 1 0.25 25 0.01 Dummy

107 151 1 0.25 25 0.01 Dummy

108 155 1 0.25 25 0.01 Dummy

108 156 1 0.25 25 0.01 Dummy

108 157 1 0.25 25 0.01 Dummy

109 156 1 0.25 25 0.01 Dummy

109 157 1 0.25 25 0.01 Dummy

109 158 1 0.25 25 0.01 Dummy

109 159 1 0.25 25 0.01 Dummy

110 160 1 0.25 25 0.01 Dummy

110 161 1 0.25 25 0.01 Dummy

111 160 1 0.25 25 0.01 Dummy

111 161 1 0.25 25 0.01 Dummy

111 162 1 0.25 25 0.01 Dummy

112 140 1 0.25 25 0.01 Dummy

112 141 1 0.25 25 0.01 Dummy

112 142 1 0.25 25 0.01 Dummy

112 162 1 0.25 25 0.01 Dummy

112 163 1 0.25 25 0.01 Dummy

113 57 1000 1 25 0.04 Access

50

From Node

To Node

Capacity (veh/hr)

Length (mi)



113 67 1000 0.25 25 0.01 Access

113 68 1000 0.25 25 0.01 Access

113 114 3000 2.41 55 0.0438 Network

114 57 1000 1 25 0.04 Access

114 65 1000 0.25 25 0.01 Access

114 66 1000 0.25 25 0.01 Access

114 67 1000 0.25 25 0.01 Access

114 113 3000 2.41 55 0.0438 Network

114 115 3000 3.44 55 0.0625 Network

114 117 1000 5.13 30 0.171 Network

114 119 1000 3.03 40 0.0758 Network

115 57 1000 1 25 0.04 Access

115 65 1000 0.25 25 0.01 Access

115 114 3000 3.44 55 0.0625 Network

115 116 1000 5.77 30 0.1923 Network

116 58 1000 1 25 0.04 Access

116 59 1000 1 25 0.04 Access

116 65 1000 0.25 25 0.01 Access

116 71 1000 0.25 25 0.01 Access

116 72 1000 0.25 25 0.01 Access

116 115 1000 5.77 30 0.1923 Network

116 117 3000 2.43 45 0.054 Network

116 134 1000 3.66 30 0.122 Network

117 65 1000 0.25 25 0.01 Access

117 66 1000 0.25 25 0.01 Access

117 71 1000 0.25 25 0.01 Access

117 114 1000 5.13 30 0.171 Network

117 118 1500 1.49 45 0.0331 Network

118 66 1000 0.25 25 0.01 Access

118 69 1000 0.25 25 0.01 Access

118 70 1000 0.25 25 0.01 Access

118 71 1000 0.25 25 0.01 Access

118 117 1500 1.49 45 0.0331 Network

118 119 1000 3.73 30 0.1243 Network

118 120 1500 3.48 45 0.0773 Network

118 134 1000 1.91 30 0.0637 Network

119 66 1000 0.25 25 0.01 Access

119 67 1000 0.25 25 0.01 Access

119 68 1000 0.25 25 0.01 Access

119 69 1000 0.25 25 0.01 Access

51

From Node

To Node

Capacity (veh/hr)

Length (mi)



119 113 1000 2.55 30 0.085 Network

119 114 1000 3.03 40 0.0758 Network

119 118 1000 3.73 30 0.1243 Network

120 69 1000 0.25 25 0.01 Access

120 70 1000 0.25 25 0.01 Access

120 75 1000 0.25 25 0.01 Access

120 76 1000 0.25 25 0.01 Access

120 77 1000 0.25 25 0.01 Access

120 118 1500 3.48 45 0.0773 Network

120 121 1500 3.07 45 0.0682 Network

120 126 2000 6.89 30 0.2297 Network

120 131 1000 2.4 30 0.08 Network

120 133 1000 1.88 30 0.0627 Network

121 76 1000 0.25 25 0.01 Access

121 77 1000 0.25 25 0.01 Access

121 83 1000 0.25 25 0.01 Access

121 84 1000 0.25 25 0.01 Access

121 120 1500 3.07 45 0.0682 Network

121 122 1500 1.22 45 0.0271 Network

121 123 3000 2.8 45 0.0622 Network

121 131 3000 1.68 55 0.0305 Network

122 79 1000 0.25 25 0.01 Access

122 83 1000 0.25 25 0.01 Access

122 84 1000 0.25 25 0.01 Access

122 99 1000 0.25 25 0.01 Access

122 121 1500 1.22 45 0.0271 Network

122 123 1000 0.67 30 0.0223 Network

122 131 1000 2.23 30 0.0743 Network

122 147 1500 3.59 55 0.0653 Network

123 77 1000 0.25 25 0.01 Access

123 78 1000 0.25 25 0.01 Access

123 83 1000 0.25 25 0.01 Access

123 122 1000 0.67 30 0.0223 Network

123 119 1000 4.8 35 0.1371 Network

123 121 3000 2.8 45 0.0622 Network

123 124 3000 2.8 45 0.0622 Network

124 78 1000 0.25 25 0.01 Access

124 82 1000 0.25 25 0.01 Access

124 83 1000 0.25 25 0.01 Access

124 123 3000 2.8 45 0.0622 Network

52

From Node

To Node

Capacity (veh/hr)

Length (mi)



124 125 2000 1.6 45 0.0356 Network

124 130 2000 1 45 0.0222 Network

125 78 1000 0.25 25 0.01 Access

125 80 1000 0.25 25 0.01 Access

125 82 1000 0.25 25 0.01 Access

125 124 2000 1.6 45 0.0356 Network

125 127 2000 1.25 55 0.0227 Network

125 129 3000 2.1 50 0.042 Network

126 64 1000 1 25 0.04 Access

126 68 1000 0.25 25 0.01 Access

126 78 1000 0.25 25 0.01 Access

126 80 1000 0.25 25 0.01 Access

126 113 3000 3.8 55 0.0691 Network

126 120 2000 6.89 30 0.2297 Network

126 127 3000 0.91 55 0.0165 Network

127 64 1000 1 25 0.04 Access

127 80 1000 0.25 25 0.01 Access

127 81 1000 0.25 25 0.01 Access

127 125 2000 1.25 55 0.0227 Network

127 126 3000 0.91 55 0.0165 Network

127 128 3000 1.87 55 0.034 Network

128 63 1000 1 25 0.04 Access

128 64 1000 1 25 0.04 Access

128 81 1000 0.25 25 0.01 Access

128 127 3000 1.87 55 0.034 Network

129 82 1000 0.25 25 0.01 Access

129 100 1000 0.25 25 0.01 Access

129 125 3000 2.1 50 0.042 Network

129 130 1000 1.8 30 0.06 Network

129 149 3000 3 55 0.0545 Network

130 82 1000 0.25 25 0.01 Access

130 83 1000 0.25 25 0.01 Access

130 99 1000 0.25 25 0.01 Access

130 100 1000 0.25 25 0.01 Access

130 122 1000 2.88 30 0.096 Network

130 124 2000 1 45 0.0222 Network

130 129 1000 1.8 30 0.06 Network

130 148 2000 3.15 45 0.07 Network

131 75 1000 0.25 25 0.01 Access

131 76 1000 0.25 25 0.01 Access

53

From Node

To Node

Capacity (veh/hr)

Length (mi)



131 79 1000 0.25 25 0.01 Access

131 84 1000 0.25 25 0.01 Access

131 120 1000 2.4 30 0.08 Network

131 121 3000 1.68 55 0.0305 Network

131 122 1000 2.23 30 0.0743 Network

132 74 1000 0.25 25 0.01 Access

132 75 1000 0.25 25 0.01 Access

132 79 1000 0.25 25 0.01 Access

132 86 1000 0.25 25 0.01 Access

132 131 3000 1.65 55 0.03 Network

132 133 1000 1.66 40 0.0415 Network

132 136 3000 1.87 55 0.034 Network

132 159 1000 4.45 30 0.1483 Network

133 70 1000 0.25 25 0.01 Access

133 73 1000 0.25 25 0.01 Access

133 74 1000 0.25 25 0.01 Access

133 75 1000 0.25 25 0.01 Access

133 120 1000 1.88 30 0.0627 Network

133 132 1000 1.66 40 0.0415 Network

133 134 1000 3.54 30 0.118 Network

133 135 1000 1.44 30 0.048 Network

134 70 1000 0.25 25 0.01 Access

134 71 1000 0.25 25 0.01 Access

134 72 1000 0.25 25 0.01 Access

134 116 1000 3.66 30 0.122 Network

134 118 1000 1.91 30 0.0637 Network

134 133 1000 3.54 30 0.118 Network

134 135 1000 3.08 30 0.1027 Network

135 72 1000 0.25 25 0.01 Access

135 73 1000 0.25 25 0.01 Access

135 74 1000 0.25 25 0.01 Access

135 85 1000 0.25 25 0.01 Access

135 133 1000 1.44 30 0.048 Network

135 134 1000 3.08 30 0.1027 Network

135 136 1000 1.54 30 0.0513 Network

135 137 1000 3.02 30 0.1007 Network

136 74 1000 0.25 25 0.01 Access

136 85 1000 0.25 25 0.01 Access

136 86 1000 0.25 25 0.01 Access

136 132 3000 1.87 55 0.034 Network

54

From Node

To Node

Capacity (veh/hr)

Length (mi)



136 135 1000 1.54 30 0.0513 Network

136 139 3000 2.8 55 0.0509 Network

137 72 1000 0.25 25 0.01 Access

137 85 1000 0.25 25 0.01 Access

137 87 1000 0.25 25 0.01 Access

137 88 1000 0.25 25 0.01 Access

137 138 1000 3.01 30 0.1003 Network

137 141 1000 2.24 35 0.064 Network

137 142 1000 2.14 30 0.0713 Network

138 59 1000 1 25 0.04 Access

138 60 1000 1 25 0.04 Access

138 72 1000 0.25 25 0.01 Access

138 88 1000 0.25 25 0.01 Access

138 89 1000 0.25 25 0.01 Access

138 137 1000 3.01 30 0.1003 Network

138 139 1000 4.24 30 0.1413 Network

139 85 1000 0.25 25 0.01 Access

139 86 1000 0.25 25 0.01 Access

139 91 1000 0.25 25 0.01 Access

139 92 1000 0.25 25 0.01 Access

139 136 3000 2.8 55 0.0509 Network

139 138 1000 4.24 30 0.1413 Network

139 140 3000 2.3 55 0.0418 Network

140 91 1000 0.25 25 0.01 Access

140 92 1000 0.25 25 0.01 Access

140 112 1000 0.25 25 0.01 Access

140 139 3000 2.3 55 0.0418 Network

140 141 4000 0.82 75 0.0109 Network

140 160 4000 4.07 75 0.0543 Network

141 90 1000 0.25 25 0.01 Access

141 91 1000 0.25 25 0.01 Access

141 112 1000 0.25 25 0.01 Access

141 137 1000 2.24 35 0.064 Network

141 140 4000 0.82 70 0.0117 Network

141 142 4000 0.95 70 0.0136 Network

142 60 1000 1 25 0.04 Access

142 89 1000 0.25 25 0.01 Access

142 90 1000 0.25 25 0.01 Access

142 112 1000 0.25 25 0.01 Access

142 137 1000 2.14 30 0.0713 Network

55

From Node

To Node

Capacity (veh/hr)

Length (mi)



142 141 4000 0.95 70 0.0136 Network

142 163 1000 4.06 30 0.1353 Network

143 86 1000 0.25 25 0.01 Access

143 93 1000 0.25 25 0.01 Access

143 94 1000 0.25 25 0.01 Access

143 95 1000 0.25 25 0.01 Access

143 139 2000 4.14 30 0.138 Network

143 159 2000 2.84 30 0.0947 Network

143 160 3000 2.26 45 0.0502 Network

144 79 1000 0.25 25 0.01 Access

144 96 1000 0.25 25 0.01 Access

144 97 1000 0.25 25 0.01 Access

144 143 1500 2.12 45 0.0471 Network

144 145 3000 3.14 45 0.0698 Network

144 156 1000 1.52 30 0.0507 Network

145 79 1000 0.25 25 0.01 Access

145 97 1000 0.25 25 0.01 Access

145 98 1000 0.25 25 0.01 Access

145 144 3000 3.14 45 0.0698 Network

145 146 3000 1.11 45 0.0247 Network

145 155 1000 3.44 30 0.1147 Network

146 79 1000 0.25 25 0.01 Access

146 99 1000 0.25 25 0.01 Access

146 106 1000 0.25 25 0.01 Access

146 145 3000 1.11 45 0.0247 Network

146 147 1000 0.68 30 0.0227 Network

146 154 1000 3.64 30 0.1213 Network

147 79 1000 0.25 25 0.01 Access

147 101 1000 0.25 25 0.01 Access

147 106 1000 0.25 25 0.01 Access

147 122 1500 3.59 55 0.0653 Network

147 146 1000 0.68 30 0.0227 Network

147 148 1000 1.86 30 0.062 Network

147 152 1500 2.11 45 0.0469 Network

148 99 1000 0.25 25 0.01 Access

148 100 1000 0.25 25 0.01 Access

148 101 1000 0.25 25 0.01 Access

148 102 1000 0.25 25 0.01 Access

148 130 2000 3.15 45 0.07 Network

148 147 1000 1.86 30 0.062 Network

56

From Node

To Node

Capacity (veh/hr)

Length (mi)



148 149 1000 2.39 30 0.0797 Network

148 152 2000 1.75 45 0.0389 Network

149 63 1000 1 25 0.04 Access

149 100 1000 0.25 25 0.01 Access

149 102 1000 0.25 25 0.01 Access

149 103 1000 0.25 25 0.01 Access

149 104 1000 0.25 25 0.01 Access

149 129 3000 3 55 0.0545 Network

149 150 1000 4.1 30 0.1367 Network

149 151 2000 4.6 30 0.1533 Network

149 152 1000 2.08 30 0.0693 Network

150 63 1000 1 25 0.04 Access

150 104 1000 0.25 25 0.01 Access

150 107 1000 0.25 25 0.01 Access

150 149 1000 4.1 30 0.1367 Network

150 151 2000 1.93 30 0.0643 Network

150 153 3000 4.16 65 0.064 Network

151 62 1000 1 25 0.04 Access

151 103 1000 0.25 25 0.01 Access

151 107 1000 0.25 25 0.01 Access

151 149 2000 4.6 30 0.1533 Network

151 152 4500 3.4 55 0.0618 Network

152 101 1000 0.25 25 0.01 Access

152 102 1000 0.25 25 0.01 Access

152 103 1000 0.25 25 0.01 Access

152 105 1000 0.25 25 0.01 Access

152 106 1000 0.25 25 0.01 Access

152 147 1500 2.11 45 0.0469 Network

152 148 2000 1.75 45 0.0389 Network

152 149 1000 2.08 30 0.0693 Network

152 151 4500 3.4 55 0.0618 Network

152 153 2000 1.9 45 0.0422 Network

153 62 1000 1 25 0.04 Access

153 105 1000 0.25 25 0.01 Access

153 106 1000 0.25 25 0.01 Access

153 150 3000 4.16 65 0.064 Network

153 152 2000 1.9 45 0.0422 Network

153 154 3000 1.7 65 0.0262 Network

154 62 1000 1 25 0.04 Access

154 98 1000 0.25 25 0.01 Access

57

From Node

To Node

Capacity (veh/hr)

Length (mi)



154 106 1000 0.25 25 0.01 Access

154 146 1000 3.64 30 0.1213 Network

154 153 3000 1.7 65 0.0262 Network

154 155 3000 1.69 65 0.026 Network

155 62 1000 1 25 0.04 Access

155 97 1000 0.25 25 0.01 Access

155 98 1000 0.25 25 0.01 Access

155 108 1000 0.25 25 0.01 Access

155 145 1000 3.44 30 0.1147 Network

155 156 3000 2.38 55 0.0433 Network

155 158 4500 3.53 65 0.0543 Network

156 96 1000 0.25 25 0.01 Access

156 97 1000 0.25 25 0.01 Access

156 108 1000 0.25 25 0.01 Access

156 109 1000 0.25 25 0.01 Access

156 144 1000 1.52 30 0.0507 Network

156 155 3000 2.38 55 0.0433 Network

156 157 3000 2.98 35 0.0851 Network

156 159 2000 1.1 30 0.0367 Network

157 61 1000 1 25 0.04 Access

157 109 1000 0.25 25 0.01 Access

157 156 3000 2.98 35 0.0851 Network

157 158 8000 1.28 70 0.0183 Network

158 61 1000 1 25 0.04 Access

158 94 1000 0.25 25 0.01 Access

158 109 1000 0.25 25 0.01 Access

158 110 1000 0.25 25 0.01 Access

158 155 4500 3.53 65 0.0543 Network

158 157 8000 1.28 70 0.0183 Network

158 160 4000 2.32 70 0.0331 Network

158 161 3000 2.16 65 0.0332 Network

159 94 1000 0.25 25 0.01 Access

159 95 1000 0.25 25 0.01 Access

159 96 1000 0.25 25 0.01 Access

159 109 1000 0.25 25 0.01 Access

159 132 1000 4.45 30 0.1483 Network

159 143 2000 2.84 30 0.0947 Network

159 156 2000 1.1 30 0.0367 Network

160 93 1000 0.25 25 0.01 Access

160 94 1000 0.25 25 0.01 Access

58

From Node

To Node

Capacity (veh/hr)

Length (mi)



160 110 1000 0.25 25 0.01 Access

160 111 1000 0.25 25 0.01 Access

160 140 4000 4.07 70 0.0581 Network

160 143 3000 2.26 45 0.0502 Network

160 158 4000 2.32 70 0.0331 Network

160 161 1000 1.24 30 0.0413 Network

161 61 1000 1 25 0.04 Access

161 110 1000 0.25 25 0.01 Access

161 111 1000 0.25 25 0.01 Access

161 158 3000 2.16 65 0.0332 Network

161 162 3000 2.92 55 0.0531 Network

162 61 1000 1 25 0.04 Access

162 111 1000 0.25 25 0.01 Access

162 112 1000 0.25 25 0.01 Access

162 161 3000 2.92 55 0.0531 Network

162 163 3000 2.58 55 0.0469 Network

163 60 1000 1 25 0.04 Access

163 61 1000 1 25 0.04 Access

163 112 1000 0.25 25 0.01 Access

163 142 1000 4.06 30 0.1353 Network

163 162 3000 2.58 55 0.0469 Network

59

Appendix B:

Origin-Destination matrix

60

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

1 1 1402 1 37 3 2 17 6

1 2 270 1 38 3 2 18 10

1 3 50 1 39 3 2 19 37

1 4 13 1 40 3 2 20 1

1 5 15 1 41 9 2 21 3

1 6 68 1 42 15 2 22 46

1 7 61 1 43 29 2 23 20

1 8 291 1 44 19 2 24 5

1 9 102 1 45 1 2 25 10

1 10 24 1 46 10 2 26 10

1 11 21 1 47 8 2 27 3

1 12 108 1 48 3 2 28 12

1 13 34 1 49 14 2 29 35

1 14 11 1 50 10 2 30 2

1 15 9 1 51 2 2 31 8

1 16 18 1 52 15 2 32 1

1 17 5 1 53 4 2 33 8

1 18 9 1 54 1 2 34 10

1 19 33 1 55 3 2 35 19

1 20 2 1 56 10 2 36 8

1 21 4 2 1 524 2 37 5

1 22 85 2 2 504 2 38 4

1 23 22 2 3 93 2 39 3

1 24 15 2 4 33 2 40 9

1 25 19 2 5 44 2 41 12

1 26 18 2 6 58 2 42 22

1 27 4 2 7 33 2 43 11

1 28 2 2 8 105 2 44 1

1 29 9 2 9 68 2 45 7

1 30 24 2 10 17 2 46 6

1 31 1 2 11 8 2 47 2

1 32 4 2 12 57 2 48 11

1 33 1 2 13 24 2 49 8

1 34 4 2 14 9 2 50 2

1 35 5 2 15 12 2 51 25

1 36 10 2 16 26 2 52 1

61

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

2 53 7 3 38 4 4 23 8

2 54 2 3 39 3 4 24 1

2 55 6 3 40 2 4 25 2

2 56 20 3 41 7 4 26 2

3 1 59 3 42 7 4 27 1

3 2 63 3 43 10 4 28 0

3 3 269 3 44 4 4 29 10

3 4 41 3 45 0 4 30 44

3 5 68 3 46 3 4 31 2

3 6 32 3 47 3 4 32 15

3 7 12 3 48 1 4 33 4

3 8 28 3 49 5 4 34 25

3 9 18 3 50 4 4 35 13

3 10 7 3 51 1 4 36 23

3 11 2 3 52 29 4 37 25

3 12 19 3 53 8 4 38 4

3 13 12 3 54 3 4 39 3

3 14 6 3 55 8 4 40 2

3 15 9 3 56 23 4 41 6

3 16 44 4 1 18 4 42 7

3 17 5 4 2 26 4 43 6

3 18 8 4 3 42 4 44 3

3 19 42 4 4 199 4 45 0

3 20 1 4 5 149 4 46 2

3 21 1 4 6 36 4 47 1

3 22 18 4 7 6 4 48 0

3 23 12 4 8 12 4 49 3

3 24 1 4 9 6 4 50 4

3 25 3 4 10 3 4 51 1

3 26 3 4 11 1 4 52 36

3 27 1 4 12 10 4 53 10

3 28 1 4 13 5 4 54 4

3 29 18 4 14 3 4 55 10

3 30 48 4 15 2 4 56 36

3 31 2 4 16 9

3 32 12 4 17 2 5 1 12

3 33 2 4 18 5 5 2 18

3 34 21 4 19 13 5 3 44

3 35 11 4 20 0 5 4 78

3 36 22 4 21 1 5 5 3039

3 37 14 4 22 8 5 6 379

62

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

5 7 55 5 48 5 6 33 1

5 8 35 5 49 27 6 34 3

5 9 7 5 50 33 6 35 5

5 10 3 5 51 7 6 36 13

5 11 1 5 52 489 6 37 10

5 12 21 5 53 125 6 38 10

5 13 7 5 54 76 6 39 4

5 14 5 5 55 111 6 40 7

5 15 3 5 56 156 6 41 76

5 16 13 6 1 20 6 42 159

5 17 4 6 2 11 6 43 19

5 18 11 6 3 10 6 44 17

5 19 12 6 4 8 6 45 3

5 20 1 6 5 200 6 46 36

5 21 2 6 6 1184 6 47 52

5 22 20 6 7 148 6 48 26

5 23 44 6 8 52 6 49 52

5 24 2 6 9 9 6 50 124

5 25 6 6 10 4 6 51 31

5 26 8 6 11 2 6 52 242

5 27 2 6 12 21 6 53 37

5 28 1 6 13 6 6 54 13

5 29 16 6 14 3 6 55 15

5 30 69 6 15 2 6 56 18

5 31 4 6 16 4 7 1 27

5 32 15 6 17 2 7 2 9

5 33 5 6 18 4 7 3 6

5 34 22 6 19 7 7 4 2

5 35 30 6 20 1 7 5 50

5 36 62 6 21 1 7 6 259

5 37 73 6 22 23 7 7 756

5 38 38 6 23 34 7 8 114

5 39 14 6 24 3 7 9 5

5 40 13 6 25 7 7 10 2

5 41 59 6 26 9 7 11 2

5 42 73 6 27 2 7 12 23

5 43 21 6 28 1 7 13 4

5 44 13 6 29 4 7 14 2

5 45 2 6 30 21 7 15 1

5 46 17 6 31 1 7 16 3

5 47 15 6 32 2 7 17 1

63

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

7 18 43 8 3 13 8 44 26

7 19 8 8 4 3 8 45 1

7 20 3 8 5 22 8 46 13

7 21 1 8 6 79 8 47 11

7 22 4 8 7 117 8 48 4

7 23 11 8 8 903 8 49 14

7 24 6 8 9 16 8 50 12

7 25 26 8 10 6 8 51 3

7 26 10 8 11 8 8 52 18

7 27 4 8 12 84 8 53 4

7 28 11 8 13 15 8 54 1

7 29 23 8 14 4 8 55 3

7 30 11 8 15 3 8 56 8

7 31 1 8 16 7 9 1 212

7 32 1 8 17 2 9 2 72

7 33 2 8 18 5 9 3 33

7 34 1 8 19 17 9 4 8

7 35 1 8 20 1 9 5 16

7 36 9 8 21 3 9 6 47

7 37 0 8 22 86 9 7 20

7 38 1 8 23 17 9 8 54

7 39 14 8 24 39 9 9 135

7 40 4 8 25 30 9 10 28

7 41 2 8 26 26 9 11 10

7 42 2 8 27 4 9 12 38

7 43 2 8 28 1 9 13 36

7 44 1 8 29 5 9 14 10

7 45 0 8 30 16 9 15 13

7 46 1 8 31 1 9 16 16

7 47 3 8 32 2 9 17 5

7 48 0 8 33 1 9 18 8

7 49 0 8 34 2 9 19 49

7 50 0 8 35 4 9 20 1

7 51 0 8 36 8 9 21 2

7 52 1 8 37 3 9 22 28

7 53 1 8 38 2 9 23 14

7 54 1 8 39 2 9 24 2

7 55 0 8 40 2 9 25 5

7 56 0 8 41 9 9 26 5

8 1 0 8 42 16 9 27 2

8 2 1 8 43 25 9 28 1

64

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

9 29 7 10 15 7 11 1 43

9 30 19 10 16 7 11 2 8

9 31 1 10 17 2 11 3 3

9 32 3 10 18 3 11 4 1

9 33 1 10 19 21 11 5 4

9 34 3 10 20 0 11 6 11

9 35 3 10 21 1 11 7 6

9 36 7 10 22 9 11 8 26

9 37 3 10 23 4 11 9 10

9 38 3 10 24 1 11 10 4

9 39 2 10 25 1 11 11 11

9 40 2 10 26 2 11 12 23

9 41 6 10 27 1 11 13 11

9 42 8 10 28 0 11 14 1

9 43 14 10 29 2 11 15 1

9 44 6 10 30 6 11 16 2

9 45 1 10 31 0 11 17 1

9 46 5 10 32 1 11 18 1

9 47 4 10 33 0 11 19 9

9 48 1 10 34 1 11 20 0

9 49 7 10 35 1 11 21 1

9 50 6 10 36 2 11 22 14

9 51 2 10 37 1 11 23 4

9 52 13 10 38 1 11 24 2

9 53 4 10 39 1 11 25 2

9 54 1 10 40 1 11 26 2

9 55 3 10 41 2 11 27 1

9 56 8 10 42 2 11 28 0

10 1 33 10 43 4 11 29 1

10 2 12 10 44 2 11 30 3

10 3 8 10 45 0 11 31 0

10 4 2 10 46 1 11 32 0

10 5 5 10 47 1 11 33 0

10 6 13 10 48 0 11 34 0

10 7 6 10 49 2 11 35 1

10 8 16 10 50 2 11 36 1

10 9 20 10 51 0 11 37 1

10 10 23 10 52 4 11 38 0

10 11 3 10 53 1 11 39 0

10 12 12 10 54 0 11 40 0

10 13 21 10 55 1 11 41 1

10 14 5 10 56 2 11 42 2

65

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

11 43 5 12 28 1 13 13 37

11 44 2 12 29 1 13 14 6

11 45 0 12 30 3 13 15 2

11 46 1 12 31 0 13 16 5

11 47 1 12 32 0 13 17 2

11 48 0 12 33 0 13 18 4

11 49 2 12 34 0 13 19 37

11 50 2 12 35 1 13 20 1

11 51 0 12 36 1 13 21 2

11 52 4 12 37 1 13 22 16

11 53 1 12 38 0 13 23 6

11 54 0 12 39 0 13 24 1

11 55 1 12 40 0 13 25 2

11 56 2 12 41 2 13 26 2

12 1 28 12 42 3 13 27 1

12 2 9 12 43 8 13 28 1

12 3 4 12 44 4 13 29 3

12 4 1 12 45 0 13 30 6

12 5 5 12 46 2 13 31 0

12 6 12 12 47 2 13 32 1

12 7 9 12 48 1 13 33 0

12 8 39 12 49 2 13 34 1

12 9 6 12 50 2 13 35 1

12 10 3 12 51 1 13 36 2

12 11 6 12 52 3 13 37 1

12 12 43 12 53 1 13 38 1

12 13 10 12 54 0 13 39 1

12 14 2 12 55 1 13 40 1

12 15 1 12 56 1 13 41 2

12 16 2 13 1 19 13 42 3

12 17 1 13 2 9 13 43 6

12 18 1 13 3 6 13 44 2

12 19 11 13 4 2 13 45 0

12 20 1 13 5 6 13 46 2

12 21 2 13 6 14 13 47 1

12 22 32 13 7 6 13 48 0

12 23 5 13 8 18 13 49 2

12 24 7 13 9 10 13 50 2

12 25 4 13 10 7 13 51 0

12 26 5 13 11 4 13 52 4

12 27 2 13 12 21 13 53 1

66

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

13 54 0 14 39 1 15 24 0

13 55 1 14 40 1 15 25 1

13 56 2 14 41 2 15 26 1

14 1 9 14 42 2 15 27 0

14 2 5 14 43 3 15 28 0

14 3 6 14 44 1 15 29 1

14 4 2 14 45 0 15 30 3

14 5 7 14 46 1 15 31 0

14 6 9 14 47 1 15 32 1

14 7 4 14 48 0 15 33 0

14 8 9 14 49 1 15 34 1

14 9 5 14 50 1 15 35 1

14 10 4 14 51 0 15 36 1

14 11 1 14 52 4 15 37 1

14 12 7 14 53 1 15 38 1

14 13 12 14 54 0 15 39 0

14 14 11 14 55 1 15 40 0

14 15 2 14 56 2 15 41 1

14 16 9 15 1 10 15 42 1

14 17 4 15 2 7 15 43 2

14 18 5 15 3 9 15 44 1

14 19 24 15 4 1 15 45 0

14 20 0 15 5 5 15 46 1

14 21 1 15 6 6 15 47 1

14 22 7 15 7 2 15 48 0

14 23 5 15 8 6 15 49 1

14 24 0 15 9 7 15 50 1

14 25 1 15 10 6 15 51 0

14 26 1 15 11 0 15 52 3

14 27 1 15 12 4 15 53 1

14 28 0 15 13 6 15 54 0

14 29 3 15 14 2 15 55 1

14 30 8 15 15 11 15 56 2

14 31 0 15 16 8 16 1 12

14 32 1 15 17 1 16 2 11

14 33 0 15 18 1 16 3 25

14 34 1 15 19 11 16 4 4

14 35 1 15 20 0 16 5 15

14 36 2 15 21 0 16 6 11

14 37 1 15 22 3 16 7 5

14 38 1 15 23 2 16 8 11

67

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

16 9 6 16 50 2 17 35 1

16 10 4 16 51 0 17 36 2

16 11 1 16 52 7 17 37 1

16 12 7 16 53 2 17 38 1

16 13 9 16 54 1 17 39 1

16 14 6 16 55 2 17 40 1

16 15 5 16 56 5 17 41 2

16 16 45 17 1 5 17 42 1

16 17 6 17 2 4 17 43 2

16 18 6 17 3 6 17 44 1

16 19 26 17 4 1 17 45 0

16 20 0 17 5 6 17 46 1

16 21 1 17 6 5 17 47 1

16 22 7 17 7 2 17 48 0

16 23 5 17 8 5 17 49 1

16 24 0 17 9 3 17 50 1

16 25 1 17 10 2 17 51 0

16 26 1 17 11 0 17 52 3

16 27 0 17 12 4 17 53 1

16 28 0 17 13 4 17 54 0

16 29 7 17 14 4 17 55 1

16 30 11 17 15 1 17 56 2

16 31 1 17 16 11 18 1 3

16 32 3 17 17 7 18 2 2

16 33 1 17 18 4 18 3 3

16 34 4 17 19 25 18 4 1

16 35 3 17 20 0 18 5 5

16 36 5 17 21 0 18 6 3

16 37 2 17 22 4 18 7 2

16 38 1 17 23 4 18 8 3

16 39 1 17 24 0 18 9 2

16 40 1 17 25 1 18 10 1

16 41 2 17 26 1 18 11 0

16 42 2 17 27 0 18 12 2

16 43 4 17 28 0 18 13 2

16 44 1 17 29 3 18 14 2

16 45 0 17 30 6 18 15 1

16 46 1 17 31 0 18 16 3

16 47 1 17 32 1 18 17 2

16 48 0 17 33 0 18 18 12

16 49 2 17 34 1 18 19 15

68

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

18 20 0 19 5 7 19 46 1

18 21 0 19 6 8 19 47 1

18 22 2 19 7 4 19 48 0

18 23 4 19 8 8 19 49 1

18 24 0 19 9 8 19 50 2

18 25 0 19 10 3 19 51 0

18 26 0 19 11 1 19 52 4

18 27 0 19 12 6 19 53 2

18 28 0 19 13 8 19 54 0

18 29 5 19 14 5 19 55 1

18 30 10 19 15 2 19 56 2

18 31 0 19 16 5 20 1 1

18 32 1 19 17 4 20 2 0

18 33 0 19 18 5 20 3 0

18 34 1 19 19 52 20 4 0

18 35 1 19 20 1 20 5 1

18 36 2 19 21 1 20 6 1

18 37 1 19 22 6 20 7 1

18 38 0 19 23 9 20 8 2

18 39 0 19 24 0 20 9 0

18 40 1 19 25 1 20 10 0

18 41 1 19 26 1 20 11 0

18 42 1 19 27 1 20 12 2

18 43 2 19 28 1 20 13 1

18 44 1 19 29 3 20 14 0

18 45 0 19 30 6 20 15 0

18 46 1 19 31 0 20 16 0

18 47 0 19 32 1 20 17 0

18 48 0 19 33 0 20 18 0

18 49 1 19 34 1 20 19 3

18 50 1 19 35 1 20 20 0

18 51 0 19 36 2 20 21 1

18 52 2 19 37 1 20 22 3

18 53 1 19 38 1 20 23 1

18 54 0 19 39 1 20 24 0

18 55 1 19 40 1 20 25 0

18 56 2 19 41 2 20 26 0

19 1 9 19 42 2 20 27 0

19 2 8 19 43 4 20 28 0

19 3 6 19 44 1 20 29 0

19 4 3 19 45 0 20 30 1

69

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

20 31 0 21 16 0 22 1 18

20 32 0 21 17 0 22 2 7

20 33 0 21 18 0 22 3 4

20 34 0 21 19 5 22 4 1

20 35 0 21 20 1 22 5 6

20 36 0 21 21 1 22 6 16

20 37 0 21 22 6 22 7 13

20 38 0 21 23 2 22 8 40

20 39 0 21 24 0 22 9 4

20 40 0 21 25 0 22 10 2

20 41 0 21 26 0 22 11 3

20 42 0 21 27 1 22 12 29

20 43 1 21 28 1 22 13 7

20 44 0 21 29 0 22 14 2

20 45 0 21 30 1 22 15 1

20 46 0 21 31 0 22 16 2

20 47 0 21 32 0 22 17 1

20 48 0 21 33 0 22 18 2

20 49 0 21 34 0 22 19 16

20 50 0 21 35 0 22 20 2

20 51 0 21 36 0 22 21 3

20 52 0 21 37 0 22 22 65

20 53 0 21 38 0 22 23 7

20 54 0 21 39 0 22 24 10

20 55 0 21 40 0 22 25 6

20 56 0 21 41 0 22 26 8

21 1 2 21 42 1 22 27 5

21 2 1 21 43 2 22 28 2

21 3 1 21 44 0 22 29 1

21 4 0 21 45 0 22 30 4

21 5 1 21 46 0 22 31 0

21 6 3 21 47 0 22 32 1

21 7 1 21 48 0 22 33 0

21 8 3 21 49 0 22 34 1

21 9 1 21 50 1 22 35 1

21 10 0 21 51 0 22 36 2

21 11 0 21 52 1 22 37 1

21 12 4 21 53 0 22 38 1

21 13 2 21 54 0 22 39 0

21 14 1 21 55 0 22 40 0

21 15 0 21 56 0 22 41 2

70

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

22 42 4 23 27 2 24 12 11

22 43 16 23 28 1 24 13 1

22 44 6 23 29 4 24 14 0

22 45 0 23 30 30 24 15 0

22 46 3 23 31 0 24 16 0

22 47 2 23 32 2 24 17 0

22 48 1 23 33 0 24 18 0

22 49 3 23 34 2 24 19 1

22 50 3 23 35 3 24 20 0

22 51 1 23 36 9 24 21 0

22 52 5 23 37 12 24 22 14

22 53 1 23 38 7 24 23 2

22 54 0 23 39 6 24 24 13

22 55 1 23 40 7 24 25 5

22 56 2 23 41 51 24 26 6

23 1 11 23 42 31 24 27 1

23 2 7 23 43 21 24 28 0

23 3 7 23 44 7 24 29 0

23 4 3 23 45 2 24 30 1

23 5 45 23 46 8 24 31 0

23 6 61 23 47 6 24 32 0

23 7 23 23 48 2 24 33 0

23 8 20 23 49 10 24 34 0

23 9 4 23 50 18 24 35 0

23 10 2 23 51 2 24 36 1

23 11 1 23 52 49 24 37 0

23 12 12 23 53 14 24 38 0

23 13 5 23 54 3 24 39 0

23 14 3 23 55 6 24 40 0

23 15 1 23 56 7 24 41 1

23 16 4 24 1 8 24 42 1

23 17 2 24 2 2 24 43 3

23 18 7 24 3 1 24 44 3

23 19 40 24 4 0 24 45 0

23 20 1 24 5 2 24 46 1

23 21 2 24 6 6 24 47 1

23 22 13 24 7 7 24 48 0

23 23 91 24 8 31 24 49 1

23 24 2 24 9 1 24 50 1

23 25 3 24 10 0 24 51 0

23 26 5 24 11 1 24 52 1

71

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

24 53 0 25 38 0 26 23 3

24 54 0 25 39 0 26 24 8

24 55 0 25 40 0 26 25 7

24 56 1 25 41 0 26 26 22

25 1 3 25 42 1 26 27 1

25 2 1 25 43 1 26 28 0

25 3 1 25 44 3 26 29 0

25 4 0 25 45 0 26 30 2

25 5 1 25 46 1 26 31 0

25 6 3 25 47 1 26 32 0

25 7 5 25 48 0 26 33 0

25 8 13 25 49 0 26 34 0

25 9 1 25 50 1 26 35 0

25 10 0 25 51 0 26 36 1

25 11 0 25 52 0 26 37 0

25 12 2 25 53 0 26 38 0

25 13 0 25 54 0 26 39 0

25 14 0 25 55 0 26 40 0

25 15 0 25 56 0 26 41 2

25 16 0 26 1 7 26 42 3

25 17 0 26 2 2 26 43 10

25 18 0 26 3 1 26 44 7

25 19 0 26 4 0 26 45 0

25 20 0 26 5 3 26 46 2

25 21 0 26 6 11 26 47 1

25 22 3 26 7 11 26 48 1

25 23 1 26 8 25 26 49 2

25 24 2 26 9 1 26 50 2

25 25 5 26 10 0 26 51 0

25 26 3 26 11 1 26 52 3

25 27 0 26 12 9 26 53 1

25 28 0 26 13 1 26 54 0

25 29 0 26 14 0 26 55 0

25 30 0 26 15 0 26 56 1

25 31 0 26 16 1 27 1 2

25 32 0 26 17 0 27 2 1

25 33 0 26 18 1 27 3 1

25 34 0 26 19 1 27 4 0

25 35 0 26 20 0 27 5 1

25 36 0 26 21 0 27 6 3

25 37 0 26 22 13 27 7 2

72

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

27 8 4 27 49 0 28 34 0

27 9 1 27 50 1 28 35 0

27 10 0 27 51 0 28 36 0

27 11 0 27 52 1 28 37 0

27 12 4 27 53 0 28 38 0

27 13 1 27 54 0 28 39 0

27 14 0 27 55 0 28 40 0

27 15 0 27 56 0 28 41 0

27 16 0 28 1 1 28 42 0

27 17 0 28 2 0 28 43 1

27 18 0 28 3 0 28 44 0

27 19 2 28 4 0 28 45 0

27 20 0 28 5 1 28 46 0

27 21 0 28 6 1 28 47 0

27 22 9 28 7 1 28 48 0

27 23 2 28 8 2 28 49 0

27 24 1 28 9 0 28 50 0

27 25 1 28 10 0 28 51 0

27 26 1 28 11 0 28 52 0

27 27 2 28 12 2 28 53 0

27 28 0 28 13 1 28 54 0

27 29 0 28 14 0 28 55 0

27 30 1 28 15 0 28 56 0

27 31 0 28 16 0 29 1 4

27 32 0 28 17 0 29 2 4

27 33 0 28 18 0 29 3 11

27 34 0 28 19 3 29 4 4

27 35 0 28 20 0 29 5 12

27 36 0 28 21 1 29 6 6

27 37 0 28 22 3 29 7 3

27 38 0 28 23 1 29 8 5

27 39 0 28 24 0 29 9 2

27 40 0 28 25 0 29 10 1

27 41 0 28 26 0 29 11 0

27 42 1 28 27 0 29 12 3

27 43 4 28 28 0 29 13 2

27 44 1 28 29 0 29 14 2

27 45 0 28 30 1 29 15 1

27 46 0 28 31 0 29 16 6

27 47 0 28 32 0 29 17 2

27 48 0 28 33 0 29 18 6

73

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

29 19 13 30 4 6 30 31 0

29 20 0 30 5 16 30 32 1

29 21 0 30 6 8 30 33 1

29 22 3 30 7 3 30 34 3

29 23 4 30 8 5 30 35 2

29 24 0 30 9 3 30 36 10

29 25 1 30 10 1 30 37 6

29 26 1 30 11 1 30 38 3

29 27 0 30 12 2 30 39 2

29 28 0 30 13 2 30 40 2

29 29 21 30 14 2 30 41 5

29 30 24 30 15 1 30 42 4

29 31 1 30 16 3 30 43 2

29 32 3 30 17 2 30 44 1

29 33 1 30 18 5 30 45 0

29 34 6 30 19 10 30 46 1

29 35 3 30 20 0 30 47 1

29 36 7 30 21 0 30 48 0

29 37 3 30 22 2 30 49 1

29 38 1 30 23 11 30 50 2

29 39 1 30 24 0 30 51 0

29 40 1 30 25 1 30 52 9

29 41 2 30 26 1 30 53 4

29 42 1 30 27 0 30 54 1

29 43 2 30 28 0 30 55 2

29 44 1 30 29 6 30 56 3

29 45 0 30 30 41 31 1 0

29 46 1 30 31 0 31 2 0

29 47 1 30 32 1 31 3 1

29 48 0 30 33 1 31 4 0

29 49 1 30 34 3 31 5 1

29 50 1 30 35 2 31 6 0

29 51 0 30 36 10 31 7 0

29 52 5 30 37 6 31 8 0

29 53 2 30 38 3 31 9 0

29 54 1 30 39 2 31 10 0

29 55 1 30 40 2 31 11 0

29 56 5 30 41 5 31 12 0

30 1 4 30 42 4 31 13 0

30 2 6 30 29 6 31 14 0

30 3 8 30 30 41 31 15 0

74

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

31 16 0 32 1 1 32 42 0

31 17 0 32 2 1 32 43 0

31 18 0 32 3 4 32 44 0

31 19 0 32 4 4 32 45 0

31 20 0 32 5 5 32 46 0

31 21 0 32 6 1 32 47 0

31 22 0 32 7 0 32 48 0

31 23 0 32 8 1 32 49 0

31 24 0 32 9 0 32 50 0

31 25 0 32 10 0 32 51 0

31 26 0 32 11 0 32 52 1

31 27 0 32 12 0 32 53 0

31 28 0 32 13 0 32 54 0

31 29 0 32 14 0 32 55 0

31 30 1 32 15 0 32 56 2

31 31 0 32 16 1 33 1 1

31 32 0 32 17 0 33 2 1

31 33 0 32 18 0 33 3 2

31 34 1 32 19 1 33 4 2

31 35 0 32 20 0 33 5 6

31 36 1 32 21 0 33 6 2

31 37 0 32 22 0 33 7 0

31 38 0 32 23 1 33 8 1

31 39 0 32 24 0 33 9 0

31 40 0 32 25 0 33 10 0

31 41 0 32 26 0 33 11 0

31 42 0 32 27 0 33 12 1

31 43 0 32 28 0 33 13 0

31 44 0 32 29 2 33 14 0

31 45 0 32 30 4 33 15 0

31 46 0 32 31 0 33 16 1

31 47 0 32 32 4 33 17 0

31 48 0 32 33 1 33 18 0

31 49 0 32 34 4 33 19 1

31 50 0 32 35 1 33 20 0

31 51 0 32 36 2 33 21 0

31 52 0 32 37 1 33 22 0

31 53 0 32 38 0 33 23 1

31 54 0 32 39 0 33 24 0

31 55 0 32 40 0 33 25 0

31 56 0 32 41 0 33 26 0

75

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

33 27 0 34 12 1 34 52 5

33 28 0 34 13 0 34 53 1

33 29 1 34 14 0 34 54 1

33 30 5 34 15 0 34 55 2

33 31 0 34 15 0 34 56 8

33 32 2 34 16 2 35 1 1

33 33 2 34 17 0 35 2 1

33 34 7 34 18 1 35 3 3

33 35 2 34 19 1 35 4 3

33 36 2 34 20 0 35 5 12

33 37 3 34 21 0 35 6 4

33 38 0 34 22 1 35 7 1

33 39 0 34 23 1 35 8 2

33 40 0 34 24 0 35 9 0

33 41 0 34 25 0 35 10 0

33 42 0 34 26 0 35 11 0

33 43 0 34 27 0 35 12 1

33 44 0 34 28 0 35 13 0

33 45 0 34 29 3 35 14 0

33 46 0 34 30 13 35 15 0

33 47 0 34 31 1 35 16 1

33 48 0 34 32 4 35 17 0

33 49 0 34 33 2 35 18 1

33 50 0 34 34 20 35 19 1

33 51 0 34 35 7 35 20 0

33 52 2 34 36 7 35 21 0

33 53 1 34 37 5 35 22 1

33 54 0 34 38 1 35 23 1

33 55 1 34 39 1 35 24 0

33 56 3 34 40 0 35 25 0

34 1 1 34 41 1 35 26 0

34 2 2 34 42 1 35 27 0

34 3 5 34 43 1 35 28 0

34 4 5 34 44 0 35 29 1

34 5 12 34 45 0 35 30 8

34 6 5 34 46 0 35 31 1

34 7 1 34 47 0 35 32 2

34 8 2 34 48 0 35 33 1

34 9 1 34 49 0 35 34 10

34 10 0 34 50 0 35 35 10

34 11 0 34 51 0 35 36 5

76

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

35 37 8 36 22 1 37 7 2

35 38 1 36 23 3 37 8 2

35 39 0 36 24 0 37 9 1

35 40 0 36 25 0 37 10 0

35 41 1 36 26 0 37 11 0

35 42 1 36 27 0 37 12 1

35 43 1 36 28 0 37 13 1

35 44 0 36 29 3 37 14 0

35 45 0 36 30 20 37 15 0

35 46 0 36 31 1 37 16 1

35 47 0 36 32 2 37 17 0

35 48 0 36 33 1 37 18 1

35 49 0 36 34 4 37 19 1

35 50 0 36 35 3 37 20 0

35 51 0 36 36 38 37 21 0

35 52 4 36 37 12 37 22 1

35 53 1 36 38 2 37 23 4

35 54 1 36 39 2 37 24 0

35 55 1 36 40 1 37 25 0

35 56 8 36 41 2 37 26 0

36 1 2 36 42 2 37 27 0

36 2 3 36 43 1 37 28 0

36 3 6 36 44 0 37 29 1

36 4 5 36 45 0 37 30 10

36 5 18 36 46 0 37 31 0

36 6 6 36 47 0 37 32 1

36 7 2 36 48 0 37 33 0

36 8 3 36 49 1 37 34 2

36 9 1 36 50 1 37 35 2

36 10 0 36 51 0 37 36 11

36 11 0 36 52 7 37 37 26

36 12 1 36 53 2 37 38 6

36 13 1 36 54 1 37 39 2

36 14 1 36 55 3 37 40 2

36 15 0 36 56 6 37 41 4

36 16 2 37 1 1 37 42 3

36 17 0 37 2 2 37 43 1

36 18 1 37 3 3 37 44 0

36 19 1 37 4 4 37 45 0

36 20 0 37 5 30 37 46 1

36 21 0 37 6 10 37 47 0

77

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

37 48 0 38 33 0 39 18 0

37 49 1 38 34 1 39 19 1

37 50 1 38 35 2 39 20 0

37 51 0 38 36 7 39 21 0

37 52 14 38 37 15 39 22 0

37 53 4 38 38 25 39 23 3

37 54 2 38 39 3 39 24 0

37 55 8 38 40 4 39 25 0

37 56 8 38 41 11 39 26 0

38 1 2 38 42 7 39 27 0

38 2 2 38 43 2 39 28 0

38 3 2 38 44 1 39 29 0

38 4 2 38 45 0 39 30 4

38 5 75 38 46 1 39 31 0

38 6 23 38 47 1 39 32 0

38 7 4 38 48 0 39 33 0

38 8 4 38 49 2 39 34 0

38 9 1 38 50 3 39 35 0

38 10 0 38 51 0 39 36 2

38 11 0 38 52 52 39 37 3

38 12 2 38 53 24 39 38 2

38 13 1 38 54 10 39 39 2

38 14 1 38 55 12 39 40 1

38 15 0 38 56 7 39 41 3

38 16 1 39 1 1 39 42 1

38 17 0 39 2 1 39 43 1

38 18 1 39 3 1 39 44 0

38 19 2 39 4 1 39 45 0

38 20 0 39 5 6 39 46 0

38 21 0 39 6 3 39 47 0

38 22 2 39 7 1 39 48 0

38 23 7 39 8 1 39 49 0

38 24 0 39 9 0 39 50 1

38 25 1 39 10 0 39 51 0

38 26 1 39 11 0 39 52 5

38 27 0 39 12 0 39 53 2

38 28 0 39 13 0 39 54 1

38 29 1 39 14 0 39 55 1

38 30 12 39 15 0 39 56 1

38 31 0 39 16 0 40 1 2

38 32 1 39 17 0 40 2 1

78

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

40 3 2 40 44 1 41 29 2

40 4 1 40 45 0 41 30 16

40 5 23 40 46 1 41 31 0

40 6 20 40 47 1 41 32 1

40 7 4 40 48 0 41 33 0

40 8 4 40 49 2 41 34 2

40 9 1 40 50 3 41 35 2

40 10 0 40 51 0 41 36 8

40 11 0 40 52 36 41 37 8

40 12 2 40 53 11 41 38 7

40 13 1 40 54 2 41 39 4

40 14 1 40 55 3 41 40 9

40 15 0 40 56 3 41 41 134

40 16 1 41 1 6 41 42 77

40 17 0 41 2 3 41 43 8

40 18 1 41 3 4 41 44 5

40 19 3 41 4 3 41 45 1

40 20 0 41 5 65 41 46 8

40 21 0 41 6 127 41 47 7

40 22 2 41 7 21 41 48 2

40 23 10 41 8 13 41 49 12

40 24 0 41 9 2 41 50 31

40 25 1 41 10 1 41 51 2

40 26 1 41 11 1 41 52 133

40 27 0 41 12 7 41 53 21

40 28 0 41 13 2 41 54 5

40 29 1 41 14 1 41 55 6

40 30 10 41 15 1 41 56 7

40 31 0 41 16 2 42 1 10

40 32 1 41 17 1 42 2 5

40 33 0 41 18 3 42 3 5

40 34 1 41 19 4 42 4 3

40 35 1 41 20 0 42 5 79

40 36 4 41 21 1 42 6 294

40 37 6 41 22 8 42 7 47

40 38 6 41 23 37 42 8 24

40 39 3 41 24 1 42 9 3

40 40 11 41 25 2 42 10 1

40 41 24 41 26 3 42 11 1

40 42 7 41 27 1 42 12 12

40 43 2 41 28 0 42 13 3

79

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

42 14 1 42 55 8 43 40 1

42 15 1 42 56 9 43 41 3

42 16 2 43 1 6 43 42 5

42 17 1 43 2 3 43 43 33

42 18 2 43 3 2 43 44 5

42 19 5 43 4 1 43 45 1

42 20 0 43 5 5 43 46 3

42 21 1 43 6 12 43 47 2

42 22 14 43 7 12 43 48 1

42 23 45 43 8 11 43 49 3

42 24 1 43 9 2 43 50 5

42 25 4 43 10 1 43 51 1

42 26 5 43 11 1 43 52 4

42 27 1 43 12 6 43 53 1

42 28 0 43 13 2 43 54 0

42 29 2 43 14 1 43 55 0

42 30 14 43 15 0 43 56 1

42 31 0 43 16 1 44 1 8

42 32 2 43 17 0 44 2 3

42 33 0 43 18 1 44 3 2

42 34 2 43 19 8 44 4 1

42 35 3 43 20 0 44 5 8

42 36 9 43 21 1 44 6 25

42 37 6 43 22 14 44 7 75

42 38 6 43 23 12 44 8 27

42 39 3 43 24 2 44 9 1

42 40 5 43 25 2 44 10 1

42 41 101 43 26 6 44 11 1

42 42 234 43 27 2 44 12 8

42 43 15 43 28 0 44 13 1

42 44 9 43 29 1 44 14 1

42 45 3 43 30 3 44 15 0

42 46 18 43 31 0 44 16 1

42 47 20 43 32 0 44 17 0

42 48 6 43 33 0 44 18 1

42 49 31 43 34 0 44 19 2

42 50 109 43 35 0 44 20 0

42 51 6 43 36 1 44 21 0

42 52 142 43 37 1 44 22 11

42 53 21 43 38 0 44 23 6

42 54 6 43 39 0 44 24 4

80

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

44 25 7 45 10 0 45 51 0

44 26 9 45 11 0 45 52 2

44 27 1 45 12 1 45 53 0

44 28 0 45 13 0 45 54 0

44 29 1 45 14 0 45 55 0

44 30 3 45 15 0 45 56 0

44 31 0 45 16 0 46 1 6

44 32 0 45 17 0 46 2 3

44 33 0 45 18 0 46 3 2

44 34 0 45 19 0 46 4 1

44 35 1 45 20 0 46 5 15

44 36 1 45 21 0 46 6 65

44 37 1 45 22 1 46 7 85

44 38 0 45 23 2 46 8 18

44 39 0 45 24 0 46 9 1

44 40 0 45 25 0 46 10 1

44 41 3 45 26 0 46 11 0

44 42 6 45 27 0 46 12 6

44 43 10 45 28 0 46 13 1

44 44 44 45 29 0 46 14 1

44 45 1 45 30 0 46 15 0

44 46 9 45 31 0 46 16 1

44 47 7 45 32 0 46 17 0

44 48 3 45 33 0 46 18 1

44 49 5 45 34 0 46 19 2

44 50 6 45 35 0 46 20 0

44 51 2 45 36 0 46 21 0

44 52 7 45 37 0 46 22 7

44 53 1 45 38 0 46 23 11

44 54 0 45 39 0 46 24 1

44 55 1 45 40 0 46 25 3

44 56 1 45 41 1 46 26 3

45 1 1 45 42 2 46 27 0

45 2 0 45 43 1 46 28 0

45 3 0 45 44 1 46 29 1

45 4 0 45 45 1 46 30 4

45 5 1 45 46 2 46 31 0

45 6 6 45 47 1 46 32 0

45 7 4 45 48 0 46 33 0

45 8 1 45 49 2 46 34 0

45 9 0 45 50 4 46 35 1

81

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

46 36 2 47 21 0 48 6 60

46 37 1 47 22 4 48 7 69

46 38 1 47 23 5 48 8 10

46 39 1 47 24 1 48 9 1

46 40 1 47 25 2 48 10 0

46 41 8 47 26 2 48 11 0

46 42 15 47 27 0 48 12 3

46 43 8 47 28 0 48 13 1

46 44 14 47 29 1 48 14 0

46 45 2 47 30 2 48 15 0

46 46 47 47 31 0 48 16 0

46 47 20 47 32 0 48 17 0

46 48 7 47 33 0 48 18 0

46 49 28 47 34 0 48 19 1

46 50 27 47 35 0 48 20 0

46 51 4 47 36 1 48 21 0

46 52 16 47 37 1 48 22 4

46 53 3 47 38 1 48 23 4

46 54 1 47 39 0 48 24 1

46 55 2 47 40 1 48 25 2

46 56 2 47 41 5 48 26 2

47 1 4 47 42 12 48 27 0

47 2 2 47 43 4 48 28 0

47 3 1 47 44 7 48 29 0

47 4 1 47 45 1 48 30 2

47 5 12 47 46 15 48 31 0

47 6 65 47 47 32 48 32 0

47 7 60 47 48 12 48 33 0

47 8 12 47 49 16 48 34 0

47 9 1 47 50 17 48 35 0

47 10 0 47 51 6 48 36 1

47 11 0 47 52 12 48 37 1

47 12 4 47 53 2 48 38 1

47 13 1 47 54 1 48 39 0

47 14 0 47 55 1 48 40 0

47 15 0 47 56 2 48 41 4

47 16 1 48 1 3 48 42 7

47 17 0 48 2 1 48 43 3

47 18 1 48 3 1 48 44 5

47 19 1 48 4 0 48 45 1

47 20 0 48 5 10 48 46 9

82

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

48 47 20 49 32 0 50 17 1

48 48 15 49 33 0 50 18 2

48 49 10 49 34 0 50 19 4

48 50 10 49 35 0 50 20 0

48 51 6 49 36 0 50 21 1

48 52 10 49 37 0 50 22 10

48 53 2 49 38 0 50 23 26

48 54 1 49 39 0 50 24 1

48 55 1 49 40 0 50 25 3

48 56 2 49 41 3 50 26 5

49 1 2 49 42 9 50 27 1

49 2 1 49 43 2 50 28 0

49 3 1 49 44 2 50 29 2

49 4 0 49 45 1 50 30 8

49 5 5 49 46 13 50 31 0

49 6 24 49 47 9 50 32 1

49 7 13 49 48 3 50 33 0

49 8 4 49 49 18 50 34 1

49 9 1 49 50 16 50 35 2

49 10 0 49 51 2 50 36 5

49 11 0 49 52 5 50 37 3

49 12 1 49 53 1 50 38 3

49 13 0 49 54 0 50 39 2

49 14 0 49 55 0 50 40 2

49 15 0 49 56 0 50 41 32

49 16 0 50 1 8 50 42 84

49 17 0 50 2 4 50 43 12

49 18 0 50 3 4 50 44 8

49 19 0 50 4 2 50 45 4

49 20 0 50 5 41 50 46 24

49 21 0 50 6 192 50 47 23

49 22 1 50 7 50 50 48 7

49 23 3 50 8 20 50 49 42

49 24 0 50 9 2 50 50 111

49 25 0 50 10 1 50 51 6

49 26 1 50 11 1 50 52 59

49 27 0 50 12 10 50 53 10

49 28 0 50 13 2 50 54 3

49 29 0 50 14 1 50 55 4

49 30 1 50 15 1 50 56 5

49 31 0 50 16 2 51 1 1

83

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

51 2 1 51 43 2 52 28 0

51 3 1 51 44 2 52 29 2

51 4 0 51 45 0 52 30 13

51 5 7 51 46 4 52 31 0

51 6 52 51 47 9 52 32 1

51 7 24 51 48 7 52 33 1

51 8 5 51 49 6 52 34 2

51 9 0 51 50 8 52 35 3

51 10 0 51 51 8 52 36 8

51 11 0 51 52 7 52 37 9

51 12 2 51 53 1 52 38 19

51 13 0 51 54 0 52 39 4

51 14 0 51 55 1 52 40 11

51 15 0 51 56 1 52 41 73

51 16 0 52 1 4 52 42 68

51 17 0 52 2 4 52 43 5

51 18 0 52 3 7 52 44 4

51 19 1 52 4 8 52 45 1

51 20 0 52 5 202 52 46 7

51 21 0 52 6 163 52 47 6

51 22 2 52 7 20 52 48 3

51 23 2 52 8 9 52 49 8

51 24 0 52 9 2 52 50 27

51 25 1 52 10 1 52 51 3

51 26 1 52 11 1 52 52 385

51 27 0 52 12 4 52 53 60

51 28 0 52 13 1 52 54 25

51 29 0 52 14 1 52 55 13

51 30 1 52 15 1 52 56 10

51 31 0 52 16 2 53 1 1

51 32 0 52 17 1 53 2 1

51 33 0 52 18 2 53 3 2

51 34 0 52 19 3 53 4 2

51 35 0 52 20 0 53 5 47

51 36 1 52 21 0 53 6 18

51 37 0 52 22 5 53 7 3

51 38 0 52 23 20 53 8 2

51 39 0 52 24 0 53 9 1

51 40 0 52 25 1 53 10 0

51 41 2 52 26 2 53 11 0

51 42 5 52 27 0 53 12 1

84

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

53 13 0 53 54 8 54 39 1

53 14 0 53 55 4 54 40 2

53 15 0 53 56 2 54 41 6

53 16 1 54 1 1 54 42 7

53 17 0 54 2 1 54 43 1

53 18 1 54 3 2 54 44 1

53 19 1 54 4 2 54 45 0

53 20 0 54 5 124 54 46 1

53 21 0 54 6 26 54 47 1

53 22 1 54 7 4 54 48 0

53 23 5 54 8 2 54 49 2

53 24 0 54 9 0 54 50 3

53 25 0 54 10 0 54 51 0

53 26 0 54 11 0 54 52 61

53 27 0 54 12 1 54 53 20

53 28 0 54 13 0 54 54 30

53 29 1 54 14 0 54 55 13

53 30 4 54 15 0 54 56 8

53 31 0 54 16 1 55 1 1

53 32 0 54 17 0 55 2 1

53 33 0 54 18 1 55 3 2

53 34 1 54 19 1 55 4 2

53 35 1 54 20 0 55 5 59

53 36 2 54 21 0 55 6 9

53 37 3 54 22 1 55 7 2

53 38 10 54 23 4 55 8 1

53 39 1 54 24 0 55 9 0

53 40 4 54 25 0 55 10 0

53 41 11 54 26 0 55 11 0

53 42 8 54 27 0 55 12 1

53 43 1 54 28 0 55 13 0

53 44 1 54 29 1 55 14 0

53 45 0 54 30 5 55 15 0

53 46 1 54 31 0 55 16 0

53 47 1 54 32 1 55 17 0

53 48 0 54 33 0 55 18 0

53 49 1 54 34 1 55 19 0

53 50 3 54 35 1 55 20 0

53 51 0 54 36 4 55 21 0

53 52 59 54 37 5 55 22 1

53 53 23 54 38 10 55 23 2

85

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

Origin

Zone

Destination

Zone

Number

of Trips

55 24 0 56 9 1 56 50 1

55 25 0 56 10 0 56 51 0

55 26 0 56 11 0 56 52 6

55 27 0 56 12 1 56 53 2

55 28 0 56 13 0 56 54 2

55 29 1 56 14 0 56 55 7

55 30 3 56 15 0 56 56 25

55 31 0 56 16 1

55 32 0 56 17 0

55 33 0 56 18 1

55 34 1 56 19 0

55 35 1 56 20 0

55 36 5 56 21 0

55 37 12 56 22 1

55 38 5 56 23 1

55 39 1 56 24 0

55 40 1 56 25 0

55 41 2 56 26 0

55 42 3 56 27 0

55 43 0 56 28 0

55 44 0 56 29 1

55 45 0 56 30 3

55 46 1 56 31 0

55 47 0 56 32 1

55 48 0 56 33 1

55 49 1 56 34 3

55 50 1 56 35 3

55 51 0 56 36 4

55 52 16 56 37 7

55 53 5 56 38 2

55 54 6 56 39 0

55 55 32 56 40 0

55 56 11 56 41 1

56 1 1 56 42 2

56 2 2 56 43 0

56 3 4 56 44 0

56 4 7 56 45 0

56 5 41 56 46 0

56 6 5 56 47 0

56 7 1 56 48 0

56 8 2 56 49 0

Documents

ANALYSIS OF AGE-DEPENDENT RESILIENCE FOR A HIGHWAY …