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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.
iv
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.
1
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.
2
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
4
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.
6
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
7
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)
11
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.
16
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
22 79SR0140071 121 1997 13 Moderate 53 Moderate
23 79SR0140072 121 1997 13 Moderate 53 Moderate
24 79SR3850001 124-125 1981 29 Moderate 69 Major
25 79SR3850002 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
33 79SR3850003 125-129 1980 30 Moderate 70 Major
34 79SR3850004 125-129 1980 30 Moderate 70 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
123-124 Moderate Major
123-121 Moderate Moderate
121-131 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
147-122 Moderate Moderate
122-121 Moderate Moderate
121-120 Moderate Moderate
120-118 No damage No damage
118-117 No damage No damage
117-116 No damage No damage
125-129 Moderate Major
129-149 Minor Moderate
149-151 Minor Minor
124-130 Moderate Major
130-148 Minor Moderate
148-152 No damage No damage
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
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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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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)
Free flow speed (mi/hr)
Free flow travel time(h) Description
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
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