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DEPLOYMENT AND CALIBRATION CONSIDERATIONS FOR LARGE-SCALE
REGIONAL DYNAMIC TRAFFIC ASSIGNMENT: A CASE STUDY FOR SYDNEY,
AUSTRALIA
Melissa Duell (corresponding)
School of Civil and Environmental Engineering
University of New South Wales, Sydney, Australia
Email: m.duell@unsw.edu.au
Neeraj Saxena
School of Civil and Environmental Engineering
University of New South Wales, Sydney, Australia
Sai Chand
School of Civil and Environmental Engineering
University of New South Wales, Sydney, Australia
Nima Amini
School of Civil and Environmental Engineering
University of New South Wales, Sydney, Australia
Hanna Grzybowska
School of Civil and Environmental Engineering
University of New South Wales, Sydney, Australia
S. Travis Waller
School of Civil and Environmental Engineering
University of New South Wales, Sydney, Australia
Word Count
4,668 words + 6 Figures + 3 Tables = 6,918 total words
Re-submitted to be considered for Publication in the Journal of the Transportation Research Board:
Transportation Research Record
2
ABSTRACT
Dynamic traffic assignment has received an increasing amount of attention in recent years, with
numerous examples of practical implementations. This work adds to the existing body of literature by
describing the ongoing experience of building the first large-scale simulation-based DTA model in
Australia. We provide a summary of the input data for the model and then focus on an in-depth
discussion and analysis of model output and the calibration process. Current results put 80% of the
322 calibration points spread across the network within an acceptable bound of error, but the project
found that it was also important to consider alternative metrics of network performance so as not to
neglect other aspects of model realism. In the future, the DTA model described here could aid in
evaluating important policy decisions and infrastructural development in the context of the
macro/meso-scale network operation. Additionally, this project serves as a proof of concept for the
Australia region and may provide valuable insight to other practitioners interested in emerging areas
of transport planning and traffic modeling.
Keywords—dynamic traffic assignment; large-scale; practical traffic modelling; calibration;
3
1. INTRODUCTION
Dynamic traffic assignment (DTA) has been a popular field in recent years, both in terms of research and
practice. While static transport planning models have a rich history, it is well acknowledged that they are
chosen for tractability and solution uniqueness, rather than their ability to realistically represent traffic for
planning or operational purposes. DTA is able to more realistically capture time-dependent phenomena
such as queue spillback, as well as the temporal aspects of bottlenecks and congestion and thus, at least in
theory, may offer an appealing alternative to traditional models.
However, DTA applications remain relatively scarce in practical settings, possibly due to model
complexity and general confusion regarding the practicalities of large-scale implementation and calibration.
Thus, this work intends to share insight and offer practical knowledge about building and implementing a
large-scale DTA model.
This paper presents the development of a dynamic traffic assignment model for Sydney, Australia,
with a focus on understanding model output. This application consists of a two-hour AM-peak network
consisting of 58,583 links, 20,730 nodes, 2,282 zones, 1,262,930 vehicle demand, 490 signalized
intersections, and 1,059 bus routes. A number of the project challenges involved processing data,
particularly using the available static planning data, such as origin-destination trip matrix, to generate the
data necessary for a dynamic model, such as time-dependent vehicle demand. This work focuses on
presenting and analyzing model output, which may provide unique insight into the traffic conditions on the
Sydney network. While model deployment and calibration is an ongoing process, current results are
presented here.
2. BACKGROUND
Dynamic models are increasingly being chosen by transportation agencies to access the impacts of
various policies and infrastructural developments in the urban network. Advancements in computational
efficiency in the last decade have enabled their implementation on a wider scale. While DTA is a broad
field, this work refers to a simulation-based DTA that aims to determine the conditions of dynamic user
equilibrium, based on an iterative procedure consisting of three main parts (time-dependent shortest path,
simulation based network loading to assess cost conditions, and adjustment of vehicles between paths for an
origin-destination (OD) pair at a departure time in order to reach the dynamic user equilibrium condition),
similar to that described by Chiu et al (1).
Table 1 provides an overview of various DTA implementations found in the literature, generally
developed as part of evaluating different project objectives. These DTA models were developed on study
areas ranging from a small corridor (2, 3) to much larger regional transportation networks (4, 5). To the
authors’ knowledge, the current application is among the most large-scale DTA implementations yet.
DTA models have been applied across the world. Erdoğan et al. (2015) used a simplified DTA
model to forecast the travel patterns across the state of Maryland, US (6). The model was able to represent
congestion dynamics without using detailed network and signal information. Some regional models
implemented a multi-resolution hierarchical simulation structure where link flow and delay information was
collected for a sub-area using microsimulation. These details were exchanged with mesoscopic model at an
upper level to access its impacts at a regional scale (7, 8). Although these models are data intensive, they
are able to evaluate traffic conditions on multiple scales, which may help test policy and operational
measures.
DTA models can also be integrated with activity based models (ABM), which simulate an entire
population and their trip activities, finally assigning them on the road network to access network
performance (9, 10). These studies move away from the use of trip tables and are capable of representing
the variation in departure time and mode choice based on the input scenarios.
4
TABLE 1 Overview of selected DTA projects 1
2 Author(s) Objective of Study Study Area DTA Platform Findings
Sadabadi et al.
(2015) (2)
Modelling the impact of travel time
reliability for both private vehicles
and transit
Southwest corridor in
Portland, Oregon,
Metro (USA)
DynusT
1. BRT contributes to improved ridership due to higher
reliability and VMS improved reliability on corridor by
balancing flow between arterial and freeways
Zockaie et al.
(2015) (9)
Forecast the impact of congestion
pricing schemes on different user
classes.
Chicago Regional
Network (IL, USA) DYNASMART-P
1. The paper demonstrates an application of multi-
criterion ABM-DTA model and shows that congestion
pricing could improve network performance and mode
shares
Lu et al. (2015)
(10)
Integrating fully econometric ABM
with DTA model Singapore
SimMobility mid-
term simulator
1. Preliminary results from the model provide an idea
about model’s efficiency
Erdoğan et al.
(2015) (6)
Use of analytical DTA to build state-
wide dynamic model Maryland State, USA TRANSIMS
1. Analytical DTA provided improved information on
temporal travel characteristics at a state level
2. Individual vehicles can be tracked
Binkowski and
Hicks (2013) (3)
A dynamic model to aid decision
making process during staged
construction of a major freeway
I-96 Freeway,
Detroit, MI, USA DynusT
1. Cost of delay calculated from DTA model was helpful
in decision making
2. Freeway and bridge closure and hot spot analysis were
helpful in decision making and reducing delay
Wellander et al.
(2013) (13)
To evaluate dynamic road tolling
strategies
Alaskan way viaduct,
Seattle, Washington Dynameq
The DTA model provided comparatively accurate
estimates of toll revenue and traffic system impacts
Wismans et al.
(2013) (14)
Evaluation of fuel emissions and
noise levels
A12 highway,
Amsterdam OmniTRANS
Static model forecasts highly under or over-estimated
emission levels when compared to dynamic
Duthie et al.
(2012) (4) Bottleneck analysis at a regional level
Downtown and
regional Austin; Seattle VISTA
1. Improvement in travel time due to changes in
geometry of the expressway under consideration
2. No major route switching behavior was observed in
the network
Parsons
Brinckerhoff
(2012) (5)
Regional model for policy assessment San Francisco, CA,
USA Dynameq
1. Applying a turn penalty at intersections to account for
heavy pedestrian movement during calibration
Boyles et al. (2006)
(15)
Simulation model to test congestion
pricing policies
Dallas-Fort Worth,
Texas, USA VISTA
1. Static model under-estimated the congestion levels
during simulation period
Chang and
Ziliaskopoulos
(2003) (16)
Simulation based model to evaluate
transit signal priority Chicago, Illinois, USA VISTA
1. Lack of detailed data at regional scale for developing
the model and reasonable results were obtained from the
calibrated model
5
Additional works also examined efficient methods to calibrate the large-scale DTA models to real world
data, which was one of the major challenges in the Sydney DTA project. Jafari et al. (2015) proposed
dividing the demand at a centroid in two parts and distributing them across nearby nodes and one linked
to the periphery (11). The bi-level strategy increased the share of traffic on local streets, thus lowering the
root mean square error (a common calibration metric) of the model. Shabanian and Hadi (2014) used past
data from loop detectors to estimate the capacity at bottleneck locations. The study found the evaluated
capacities to be significantly different from the Highway Capacity Manual (HCM) at some locations (12).
This work adds to the existing literature on DTA by discussing an implementation on Sydney’s
greater metropolitan area, which is the first of its kind in Australia. This project makes use of the DTA
platform VISTA, which has been explored in other works and thus, the detailed properties of the model
are not discussed here; interested readers can refer (19, 20) for more details.
3. MODEL OVERVIEW: LOCATION AND DATA This section discusses the characteristics of the study area for the Sydney DTA model, a number of tasks
that were necessary for preparing the input data, and provides a summary of the model data itself. Figure
1 provides a project overview, with tasks divided into four primary steps: collecting the data, processing
the data, implementing the data processes and the model, and finally model calibration. A full description
of the data processing is beyond the scope of this work, so this section focuses on providing an overview
of the information relevant to the final model presentation.
FIGURE 1 Sydney DTA project framework.
3.1 Study Area
The DTA project presented in this work is focused on the city of Sydney and surrounding suburbs, which
is at the center of the Greater Metropolitan Area (GMA) (shown in Figure 2, where the areas shown in tan
on the right figure were included in the final model). By population, Sydney is the 8th largest city in the
southern hemisphere and the largest in Australia. The geographical urban area of Sydney is 1,687 km2,
divided into 658 suburbs and thirty-eight local government areas (councils), and the urban structure
follows a pattern of “urban sprawl”. The road network is dominated by major corridors, the need to cross
the Sydney Harbour between the north and south of the city, and about ten important roads/motorways.
Duell et al 6
According to the Household Travel Survey (HTS), there are approximately 2.67 million private
vehicles in Sydney and approximately 16.7 million trips on a weekday, of which 69% are by private
vehicles (17). The congestion level ranks Sydney the 21st most congested city in TomTom 2014
Congestion Index (18). In terms of static models, Sydney has the Strategic Travel Model (STM3) (22)
and the Roads Network Model (RNM) (23), both of which were sources of data for this work. Other past
studies have been project-based, generally on the microscopic scale and therefore, could not capture
network wide phenomenon, particularly the impact of route choice (which can have a significant effect
due to the corridor structure of the road network).
FIGURE 2 Sydney greater metropolitan area (GMA).
3.2 Input Data for the Model
The first task the team faced for developing a DTA model for Sydney was preparing the input data. There
are six main categories of input data that the team needed to acquire, process, and implement, including:
the network characteristics, the travel demand, the departure time profile for the AM peak, transit, traffic
signals, and the calibration data. Table 1 shows a summary, sources for the different datasets, and details
of the data that comprised the Sydney DTA model. Additional details are described below.
A significant amount of data is required to represent the road network, and the team primarily
relied on pre-existing static models. The main sources of input data utilized for the Sydney DTA model
were the two static traffic assignment models mentioned previously, the STM3 that was developed by the
developed by the Bureau of Transport Statistics (BTS) and the RNM that was developed by the Roads
and Maritime Services (RMS). The STM3 is primarily a travel demand model and included more details
regarding transit, disaggregated travel zones and data that impacted mode choice, while the RNM had
slightly more detailed data regarding the road network (lanes, capacity and speed). Figure 3 includes a
visualization of the road network. At this point, the Sydney DTA model is not integrated with a travel
demand model.
Duell et al 7
The team also considered various secondary sources for obtaining additional network details such
as transit, demand departure time information, and signals inventory data. One of the many challenges the
team faced (common to all large-scale applications) was handling the size of the datasets, as indicated in
Figure 2. We explored the use of software tools like ArcGIS, and scripts in PostgreSQL, python and bash,
to visualize output, synthesize data and minimize the computational time. All input data needed to be
processed (for quality control) and then implemented in the DTA platform.
TABLE 2 Overview of data for DTA Model of Sydney
Data
Category Source of data Data description
Greater
metropolitan
area details
Sydney area
details
Network
geometry and
characteristics
1) STM3
2) RNM
Links 63,420 42,628
Nodes 25,690 18,454
Zones 2,759 1,177 (aggregated)
Travel demand STM3 O-D matrix for 2 hour
AM peak 1.67M
1,257,961 vehicles
Departure time Household travel
survey (2009-2013)
Spatially dependent
departure time
profiles for 15 minute
intervals
N/A
Transit (buses)
1) STM3
2) Parramatta road
reconfiguration
project
3) General transit
feed specification
(GTFS) for NSW
Bus routes 1,239 1,059
Bus frequency N/A Varies with route
Dwell time N/A 25 seconds
Bus stop location 28,000 15,353
Signals
1) SCATS (Sydney
coordinated
adaptive traffic
system)
2) STM3
Signal location
information N/A 490 signals
Phase timing N/A Varies with signal
location
Calibration
BTS 1 hour volume counts
(7-8 AM, 8-9 AM) N/A 545 points*
RMS 1 hour volume counts
(7-8 AM, 8-9 AM) N/A
322 points in 160
locations
RMS Average speed N/A
Inbound/outbound
along 7 major
corridors
Finally, the team made two primary model refinements in order to address data shortcomings and
computation time. First, due to insufficient data, we decided to focus only on the Sydney city area and
surrounding suburbs, eliminating the larger areas to the north, south, and east, as shown by the greyed
lines in Figure 2. Second, the team elected to aggregate travel zones for the following reasons. The
Duell et al 8
original STM3 dataset featured 3,301 travel zones, which resulted in almost 60% of OD pairs with a
demand less than one. Unlike static modelling, DTA requires a discrete vehicle demand and OD pairs
with a demand less than one introduces a probabilistic process that may later cause errors. Thus, the team
aggregated nearby travel zones on the basis of sharing borders, where zones with a statistically high
amount of origin or destination demand were not aggregated and the remaining zones were combined
manually. This helped to reduce computation time. Additional details about the model data are contained
in Duell et al (19).
4. MODEL OVERVIEW: IMPLEMENTATION AND CALIBRATION
For any large-scale DTA application, implementing the model, analyzing the results, and calibrating the
model are each challenging tasks. This section discusses results and calibration of the Sydney model,
discusses the advantages and disadvantages of our approach, and tries to provide the model with real-
world context.
4.1 Results
After refining the focus of the model to the Sydney city area (as shown in Figure 2) and aggregating
travel zones, the DTA model included 42,628 links, 18,454 nodes 1,131 travel zones, 14,919 centroid
connectors, two hour AM peak demand of 1,262,930 vehicles, and 490 signals. The results presented here
do not include transit data. Due to the availability of new calibration data, the large-scope of the network,
and the changes between models runs, the Sydney DTA model is being continuously updated and refined;
presented here is the most up-to-date results at the time of this writing, as well as ongoing improvements.
4.2 Calibration
Ultimately, the purpose of a DTA model is to represent the real network within a sufficiently small
margin of error such that the model can be used to evaluate the impacts of future scenarios, such as
infrastructure projects. Thus, a calibration process in which the model output is compared with real-life
data is vital to ensure that the model will make reliable predictions. The calibration of the Sydney DTA
model involved close examination of model output from multiple perspectives in order to identify
appropriate adjustments to the model data.
For the Sydney DTA model, the calibration process was an iterative procedure that consisted of
running the model, comparing the output with calibration data, identifying problem areas and comparing
the model data with the real network (e.g., the number of lanes on a link with information from Google
Maps), identifying appropriate changes, implementing the changes and re-running the model. Note that
this calibration procedure was not unique to this project, although the large-scale and network structure
introduce complexities not faced in previous DTA implementation.
Appropriate changes to model data were primarily adjustments to link characteristics such as link
capacity, number of lanes, and the speed. In some cases, adjustments to the signals or the network
geometry were identified as viable calibration changes. These changes are necessary because this is a
large-scale model where the input data wasn’t tailored for the morning peak travel period. Lane
configurations in many locations differ during the morning period. Additionally, the data was for a static
model, whereas the data needs (particularly speed and capacity) and interpretation for a dynamic model
are different.
The primary calibration metric used in the Sydney application was link traffic volumes. We
acquired two waves of traffic count data that ultimately resulted in 322 calibration points at 160 locations
throughout the network from 7-8 AM, 8-9 AM, and 7-9 AM based on weekday averages for the year 2013
(the locations are indicated in Figure 4). While at first, the team focused on comparing model output
based on the two hour counts, ultimately this resulted in the model under-predicting delay due to the “cold
start” of the DTA model (i.e., the model begins with an empty network). Thus, the counts from 8-9 AM
(the second hour of the model) were the primary focus.
Duell et al 9
(a)
(b)
FIGURE 3 GEH results for the MADAM model for (a) inbound links and (b) outbound links
Duell et al 10
This work used the GEH statistic to quantify model output, which is a weighted measure of the
absolute and relative difference between the real data prediction and the model output at a certain
location. The GEH statistic is relatively common in traffic models, usually based on microsimulation
projects. A GEH less than ten is generally considered an acceptable match to data. However, this project
is on a much larger scale, and there are several significant reasons why a larger bound of error is
acceptable, or even expected. The first relates to the travel demand. This project used an origin-
destination matrix from the STM3, which included car and truck trips and was uncalibrated. On the other
hand, the traffic counts were a yearly weekday average which may not match the OD matrix or account
for expected variations in travel. Additionally, the STM3 model included a large number of centroid
connectors, which was important to account for the walking distance in the mode choice estimation of
their travel demand model. However, it may result in some error for a DTA model as it may not be
realistic for vehicles to enter the network at all of those points. Thus, the team decided to consider a GEH
of 10-25 within the error margins.
Figure 3 shows the results for the GEH statistic on the Sydney network for the 8-9 AM counts,
where Figure 3(a) shows the links that are inbound to the city centre, while Figure 3(b) shows the links
that are outbound from the city centre. The green points indicate a GEH less than 10, the orange points
indicate a GEH between 10 – 25, and the red points indicate a GEH greater than 25. About 80% of the
counts were less than 25 and about 48% were less than 10.
However, focusing solely on the traffic counts may not provide a holistic view of network
performance. For example, when the team focused on the two-hour traffic counts, it was easy to miss the
fact that the network was under-estimating delay. While quantifying the model calibration is essential, it
is also important to view the network performance from additional perspectives, including the network-
wide aggregation measures such as total system travel time or average vehicle travel time, disaggregated
into zones, such as the average delay for a zone destination, at the corridor and link level.
Table 3 summarizes the performance metrics that the team considered in the Sydney DTA
project, why the performance metric was important in the project, and the state of that metric during the
current point of calibration. These are similar to the metrics described by Sloboden et al (28). As stated
previously, the primary metric was the GEH statistic for all calibration points in the network. However, it
was important to consider additional aspects of model performance to ensure model realism. While it was
more straightforward to consider model output on a network-wide scale, more disaggregate measures by
zone, corridor, or link were more informative of model performance, but also more difficult to calculate,
measure, and visualize.
TABLE 3 Overview of performance metrics considered in the Sydney DTA project
Scale Examples Why it matters Sydney network
Network
(aggregate)
Total system travel time
-Common measure of performance
(mainly in static models)
-Used for project ranking
~280,000 hours
Relative gap (defined in (1)) Measure of model convergence 8% - 15%
-Average vehicle travel time
-Standard deviation of vehicle
travel time
Confirm model realism Figure 4
-Average vehicle delay
-Standard deviation of vehicle delay Confirm model realism
5 minutes average
delay
Zone (origin and
destination)
-Average travel time/delay for
origin or destination
-Disaggregated over varying time
intervals
Spatial analysis to confirm model
realism Figure 5
Duell et al 11
-Total demand
-Aggregate demand for varying
departure intervals
Visual confirmation of model
realism *
Number of paths (for each OD pair) Network property relating to
dynamic user equilibrium condition
7,769,316 total
routes
Corridor Average speed (for varying time
intervals)
-Important to Sydney due to
network structure
-Confirms model realism
*
Volumes at points along corridor
Identify sets of parallel routes
where the distribution of vehicles
needs to be adjusted
*
Link
Volume Primary calibration metric *
Delay Identify bottlenecks *
Volume-to-capacity ratio Indication to inform calibration *
Point (link) GEH statistic Primary quantification of model
performance and calibration
82% (of 322
points) < 25
(Figure 3)
Vehicle Delay
-Measure of network performance
-Check for outliers or extreme
behavior in model output
Figure 6
*not included in the presentation of results for the current work but considered in the calibration process
Figure 4 compares a high-level measure of network performance, vehicle average travel times
and standard deviation of travel times (denoted STD), for two network scenarios. This figure shows how
the travel time of vehicles departing during different time intervals differs between calibration runs and
additionally reflects a property of the model output, which is how the model changes due to calibration
measures. The difference between Case I and Case II is their point during the calibration process. Case II
includes some additional calibration changes at specific links.
FIGURE 4 Comparing the average travel times for two versions of the Sydney network
10
12
14
16
18
20
22
24
26
Veh
icle
Av
era
ge
Tra
vel
Tim
e (m
inu
tes)
Time Interval of Travel
Case I Average
Case I STD
Case II Average
Case II STD
Duell et al 12
Next, we examine network output from the persepective of each zone. Figure 5 shows the average
delay in minutes for each zone. The average is calculated based on each vehicle destined for that zone for
the entire departure time period (which is the two hour peak). The delay is represented by the colors
indicated in the corner of Figure 5, where the one zone that is dark red experiences an average delay of
over an hour. This may be due to the congestion around the airport in the south of Sydney, which is a
destination with a high number of trips. Most zones in the inner city experience between 5-30 minutes of
delay.
FIGURE 5 Average delay (minutes) for each destination zone
Finally, we demonstrate the delay experienced for each vehicle. The vehicle delay is the vehicle
path travel time minus the free flow travel time of the path. Figure 6 shows the frequency distribution for
delay (in minutes) for all of the vehicles in the Sydney DTA project. Most vehicles experienced less than
ten minutes of delay, while a few vehicles experienced more than two hours of delay. This indicates that
the majority of delay in the model takes place in relatively few locations, likely along major corridors. In
the future, the team will seek additional data to be able to measure whether this is a realistic result.
Duell et al 13
FIGURE 6 Frequency distribution of vehicle delay for all travel demand
4.3 Sydney DTA project challenges
This section provides a brief discussion of the major challenges the team faced during the calibration of
the Sydney DTA model. The calibration process on such a large-scale network (and for most traffic
models) required a significant amount of manual exploration and expertise of the Sydney road network,
for example, recognizing parallel corridors which may not have the correct distribution of flow.
As expected, the computational time on the large-scale network presented a challenge. We
addressed this both by adjusting the model to reduce computations and through the hardware we used,
which could speed up computations. However, these tricks can be challenging because they require an
unusual combination of programming expertise and thorough understanding of the model itself.
Other challenges included the model’s under-estimation of delay during the first hour due to the
cold start of the demand (i.e., no demand in the network at time zero), which is a recognized problem in
DTA models (26). In the future, this may be addressed by using a warm start method, potentially based on
data from the Household Travel Survey or as proposed by Levin et al (24). Additionally, the large number
of centroid connectors from the static model in combination with the zone aggregation resulted in a loss
of some traffic on local roads. This was not an unexpected result based on previous literature on zone
aggregation (11, 27) and issues encountered during previous DTA model deployments (5). While major
destinations were unaffected (because they weren’t aggregated), depending on the exact location of a
traffic count, this sometimes resulted in errors that were difficult to identify.
In the future, this project will aim to improve realism by performing more in-depth corridor
analysis by ensuring that travel time and speed along major commuter routes are within freely available
estimations (25). Additionally, a warm start for the demand may improve model prediction of delay.
Finally, additional data sources along the major motorways or turning movements at major intersections
may also help direct and refine the calibration process.
1,097,832
82,751 33,512 17,195 9,732 4,974 2,827 1,881 1,480 1,006 941 337 2,889
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000N
um
ber
of
Veh
icle
s
Minutes Delay Per Vehicle
Duell et al 14
5. CONCLUSION AND FUTURE DIRECTIONS
This paper describes the experiences of building the first large scale DTA model in Australia, applied to
the Sydney metropolitan area. The project acquired and prepared numerous data sources including the
Sydney Strategic Travel Model (STM3), the Roads Network Model (RMN), the household travel survey,
the Sydney GTFS data, Sydney SCATS signals data, traffic count data from permanent stations acquired
from the RMS journey information division, and travel time and speed estimations along major corridors.
The team implemented the model and devised various techniques to address computation time. Currently,
the run time is about 48 hours to evaluate updates in the model. Ultimately, the goal of the calibration
process will be to match corridor speed estimations within 20% and of the 322 calibration points to have
an 8-9 AM GEH statistic less than 25, with at least 60% of locations being less than 10. Currently, 83% of
calibration points are less than 25, with 42% being less than 10.
A calibrated DTA model presents numerous opportunities for future extension, particularly in
regard to applications such as environmental impact evaluations. Of course, traffic assignment serves as
an important component of a four-step transport planning model, so it would be interesting to incorporate
the travel demand aspects and see if predictions change versus the static case. More detailed transit data
or even transit assignment could be included. Measures to address the computational challenges will also
be necessary. Finally, in order to evaluate the effects of reliability, the team intends to extend the
deterministic DTA model to account for volatility in day-to-day traffic flows.
ACKNOWLEDGMENTS
The authors gratefully acknowledge project funding from Transport for New South Wales. In addition,
significant assistance and support in the form of time and data came from the Bureau of Transport
Statistics (BTS) and the Roads and Maritime Services (RMS). In particular, the authors would like to
thank Christopher Zito from RMS, Malcolm Bradley and Matthew Jones from BTS, and James Sloan
from UNSW.
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