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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