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Nonlinear Dyn (2007) 49:493509
DOI 10.1007/s11071-006-9111-3
O R I G I N A L A R T I C L E
Modelling drivers compliance and route choice behaviour
in response to travel informationHussein Dia Sakda Panwai
Received: 3 November 2005 / Accepted: 12 March 2006 / Published online: 12 January 2007C Springer Science+Business Media B.V. 2007
Abstract This paper addresses commuters route
choice behaviour in response to traveller information
systems. The data used in this study was obtained
from a field behavioural survey of drivers that was
conducted on a congested commuting corridor in
Brisbane, Australia. Agent-based neural network
models (Neugents) were used to analyse the impacts
of socio-economic, context and information variables
on individual behaviour and propensity to change
route and adjust travel patterns. The results from these
models clearly indicate that prescriptive, predictiveand quantitative real-time delay information provided
for both the usual and best alternate routes are most
effective in influencing commuters to change their
routes. The Neugent behavioural models describing
drivers dynamic route choice decision making
were also implemented within a microscopic traffic
simulation tool to evaluate the corridor-wide impacts
of providing drivers with real-time traffic informa-
tion. The simulation results support the notions that
commuters decisions to divert to alternate routes are
influenced by their socio-economic characteristics;the degree of familiarity with network conditions and
the expectation of an improvement in travel time that
exceeds a certain delay threshold associated with each
H. Dia S. PanwaiSchool of Engineering, Hawken Engineering Building,Staff House Road, The University of Queensland,Brisbane, Queensland 4072, Australiae-mails: [email protected]; [email protected]
commuter. An evaluation of the benefits of the Neugent
model over static route choice algorithms which do not
consider dynamic driver behaviour and compliance
with travel advice showed improvements of 47% in
network speeds; 58% in network delays; 711% in
stop time per vehicle and 13% in network travel times.
Keywords Behavioural route choice models.
Artificial neural networks.Microscopic traffic
simulation.Advanced traveller information systems
Introduction
Advanced traveller information systems (ATIS) are in-
creasingly being recognised as a potential strategy for
influencing driver behaviour on route choice, trip mak-
ing, time of departure and mode choices. The provi-
sion of real-time travel information allows travellers to
make informed travel decisions and has the potential
to improve network efficiency, reduce congestion and
enhance environmental quality. The successful imple-mentation of these systems, however, will depend to
a large extent on understanding how motorists adjust
their travel behaviour in response to the information
received.
This paper presents findings based on a travel be-
haviour survey of commuters on a congested commut-
ing corridor in Brisbane. The survey results provided
a useful insight into the factors influencing driver be-
haviour and route choice decisions under the presence
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494 Nonlinear Dyn (2007) 49:493509
of real-time information. A number of agent-based
neural network dynamic driver behaviour models that
were developed to predict drivers compliance and
route choice decisions under the influence of variable
message sign (VMS) information are also presented.
The applicability and feasibility of the approach was
demonstrated by interfacing the neural network driverbehavioural models to a microscopic traffic simula-
tor to evaluate the impacts of providing travel in-
formation on driver compliance rates and network
performance.
Information requirements
The quality of the information provided by ATIS is
critical to the credibility of these systems and needs
to be perceived as accurate and reliable. The effect
of information on travellers decisions depends on its
content, format or presentation style, and nature (i.e.
static, dynamic or predictive). This information can be
either qualitative, quantitative or predictive delay infor-
mation. Prescriptive information advises travellers to
make a specific choice (i.e. take an alternative route).
Descriptive information can be either qualitative or
quantitative and provides information on travel times,
incident locations and expected delays. A study con-
ducted by Polydoropoulou et al. [15] found that trav-
ellers propensity to take alternative routes increased
with prescriptive information and the most significant
increase occurred when quantitative, real-time or pre-
dictive information on both the usual route and the
alternative routes were provided. This is consistent
with the study by Khattak et al. [7] which concluded
that with accurate quantitative delay information, com-
muters may overcome their behavioural inertia when
faced with unexpected delays.
Travel behaviour framework
For the purpose of this study, it is assumed that a
travel behaviour framework similar to that proposed
by Khattak et al. [7] applies. This framework takes into
account that individuals attempt to find the best alter-
native route using the limited information available to
them. Various aspects of travel information influence
travellers decisions. The processing of information de-
pends on its content or meaning, format or presentation
style, its nature and type [18].
An example of a dynamic driver behaviour mod-
elling framework is shown in Fig. 1 [1]. Drivers set out
with goals to travel between an origin and destination
within a given period of time while incurring the low-
est possible cost. They acquire information about the
performance of the road system through direct obser-
vation or by having access to electronic information
systems. Drivers process and interpret the information
in light of their current knowledge and in accordance
with their ability to combine and process a variety of
information concerning road conditions. The interpre-
tation translates into perceptions of travel times and
Goals
Decision Rules Information Acquisition
Processing Capacity
Computational Ability
acquire info
including memory
Combining info
Actual Decisions
Reviewing
using info / prediction
Review
decision rules
Review
decisions
Choose travelpattern
Fig. 1 Driver decision process [1]
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delays. Perceptions, restrictions and individual charac-
teristics also form preferences for certain alternatives
(modes, routes and departure times). These preferences
will also depend on the previously acquired knowl-
edge, stored in the memory, and on certain thresholds
whereby motorists only switchfrom their current path if
the improvement in travel time exceeds some thresholdlevel. These preferences result in observable choices
that have outcomes (e.g. arrival time at work). If the
outcome is satisfactory, then the same choice is likely
to be repeated on the following trip forming a commute
pattern [1]. The repetition of a choice in the commute
context also depends on the future or anticipated out-
comes. These outcomes also provide feedback to the
memory in the form of knowledge updates. In unex-
pected delay situations, the anticipated outcomes are
often unsatisfactory triggering review of preferences
and changes in normal travel patterns on a real-timeand day-to-day basis [1].
As was mentioned earlier, various aspects of travel
information influence drivers decisions. The process-
ing of information depends on its content or meaning,
nature, type (whether it is quantitative or qualitative)
and presentation style. In addition, drivers are more
likely to rely on relevant and accurate information. For
example, under incident congestion, the perception of
delay and the quality of real-time information are crit-
ically important in influencing travel behaviour. Ob-
viously, the success of driver behavioural models willdepend to a large extent on capturing the different pa-
rameters mentioned above. Most previous research on
driver behaviour modelling has focused on modelling
travel response decisions based on data from travel sur-
veys or travel simulators [8]. These behavioural mod-
els mostly used drivers socio-economic characteris-
tics and attributes of usual travel patterns as explana-
tory variables. The work reported here extends previous
research efforts and will be based on real data compris-
ing the complete set of drivers choices as determined
from a driver behavioural survey.
Behavioural survey of drivers on a congested
commuting corridor
Behavioural surveys of drivers during congested traf-
fic conditions are best suited to developing behavioural
models under the influence of travel information. Prop-
erly designed surveys that capture the interactions in the
travel behaviour model would allow for the investiga-
tion of the influence of (a) unexpected and expected
congestion, (b) the various types and quality of in-
formation received about congestion and (c) drivers
experiences with congestion and related information
on the whole spectrum of pre-trip and en route deci-
sions. In particular, these behavioural surveys allowfor the relationship between a drivers response to in-
formation to be modelled in combination with actual
behaviour.
Data collection
User response to travel information is typically mod-
elled using data collected from a behavioural survey of
congestion [7]. For this study in Australia, mail-back
questionnaires were distributed to peak-periodautomo-
bile commuters travelling along a traffic commuting
corridor in Brisbane. The survey comprised questions
in the following five categories:
(1) Personal information. A drivers age, occupation and
gender may influence certain travel behaviours.
(2) Normal travel patterns. These include day-to-day be-
haviour such as work schedule, route choice and re-
sponse to recurring congestion.
(3) Pre-trip response to unexpected congestion informa-
tion.The knowledge that road conditions are abnor-
mal may influence drivers decisions regarding de-parture time and route choice before setting out on
their journeys.
(4) En route response to unexpected congestion informa-
tion. When drivers learn of abnormal road conditions
while driving, they may change certain travel deci-
sions.
(5) Willingness to change driving patterns. Given some
incentive (e.g. reduction in travel time), drivers may
be willing to leave early or take an alternative
route.
Two questionnaire forms were distributed: the first
form included questions from Categories 1, 2, 3 and 5
(for pre-trip information) whereas the second form in-
cludedall questionsfromCategories 1,2, 4 and 5 (for en
route information). Users responses to ATIS are based
on (a) revealed preference (RP) data in which the actual
behavioural response to expected/unexpected delay is
reported and (b) stated preference (SP) data in which a
drivers behaviour under a hypothetical ATIS scenario
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Table 1 Vehicle use patterns
Frequency (%)
Vehicle use Never Occasionally 1 day 2 days 3 days 4 days 5 + days
Alone by car 6.4 6.4 2.9 2.3 4.7 5.8 71.3
In a car-pool 80.7 7.0 1.8 1.2 0.0 2.3 7.0
Using park n ride facilities 91.2 4.1 0.6 1.2 1.2 0.0 1.8Using public transport 80.1 13.5 2.3 1.2 1.8 0.0 1.2
Other 94.7 2.3 0.0 0.6 1.2 0.0 1.2
Table 2 Stated commuting patterns
Response
Commuting pattern Strongly disagree Disagree Agree Strongly agree Cant say
Willing to discover new routes to get somewhere 8.8 27.1 41.8 20.6 1.8
Willing to take unfamiliar routes to avoid traffic delays 5.3 18.2 44.7 29.4 2.4
is reported. Each questionnaire comprised 40 questions
and took about 20 min to complete.
A total of 490 questionnaires were distributed to
drivers on a traffic commuting corridor in Brisbane
during the morning and afternoon peak periods.
South-west Brisbane was selected as the study corridor
because it met a number of criteria including the
availability of alternate routes to the CBD area, the
availability of public transport and real-time traffic
information and variable message signs providing
information about traffic delays to the CBD. The mail-
back questionnaire had a response rate of 34% (167
questionnaires) comprising a total of 82 pre-tip and 85
en route questionnaires. This response rate compares
favourably with the results obtained from overseas
research [7] in which substantial financial incentives
were offered to respondents. The format and presenta-
tion of the questionnaire is believed to be a key factor
in achieving this response rate, considering it was
anticipated that it would take 20 min to complete eachquestionnaire.
Overview of selected survey results
The main objective of the behavioural survey was to
determine the factors that influence route change; the
frequency of route change and traffic information pref-
erences by respondents. Some selected results relevant
to the dynamics of commuter route choice behaviour
are summarised below. A more detailed discussion of
survey results can be found in Dia et al. [2].
Vehicle use and commuting patterns
Table 1 summarises the respondents vehicle use pat-
terns. These results clearly indicate a high level of car
dependency amongst the respondents travelling on thechosen corridor with 71% choosing to drive to work in
single occupancy vehicles five or more times a week.
About 80% of respondents never used car-pools;
91% never used park n ride facilities; 80% never used
public transport and 95% never used other alternative
modes of transport. These results suggest a tendency
towards habit formation or mode choice commitment
amongst the respondents. Further exploration of the re-
spondents commuting patterns (Table 2) revealed that
62%of respondents were willing to discover new routes
and 74% were willing to take unfamiliar routes to avoidtraffic delays. These results are significant as they in-
dicate the potential of traveller information systems to
influence drivers route choice decisions.
Socio-economic attributes of respondents
Socio-economic and travel attributes of respondents
have an important effect on travel behaviour. As part
of this survey, a rich source of individual data has been
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Table 3 Characteristics of travel behaviour surveys for Brisbane, San Francisco and Chicago
Description Brisbane, Australia San Francisco, USA Chicago, USA
Sample size 171 3238 700
Gender (%)
Male 56.1 64.9 54.3
Female 43.9 35.1 45.7
Age (%)
Less than 19 0.6 0.2 0
2029 23.5 11.4 23.6
3039 22.4 31.1 33
4049 28.8 33.7 29.7
5064 22.4 21.4 12.5
Above 65 2.4 2.2 1.2
Education (%)
High school or less 15.9 4.3 4.6
Vocational/technical school 20.0 1.3 19.4
Undergraduate degree 33.5 61.3 36.1
Post-graduate degree 30.6 33.1 39.9
Annual personal income (%)Under $20,000 4.9 3.1 4.3
$20,00040,000 25.8 18.3 33.1
$40,00060,000 27.0 23 26.3
$60,00080,000 15.3 14.2 14
$80,000100,000 11.0 14.2 6.6
Above $100,000 16.0 27.2 15.7
Travel time (min)
On usual route 31 40.6 43
On best alternative route 33 44.8 53.4
Average duration of residence in the area (years) 8.3 6.7 5.3
collected which will be important in modelling the fac-tors that affect trip change behaviour,willingness to pay
for ATIS services and compliance with travel informa-
tion and route directives. Table 3 presents a summary
of selected socio-economic attributes of respondents.
The average respondent is middle-aged and has resided
in the area for 8.3 years. The respondents are divided
fairly equally between males and females with 56%
of respondents being males. The average annual in-
come is $62 000 and 64% of respondents have either
undergraduate or postgraduate qualifications. The pri-
mary occupations of this sample are professional, cler-ical/service, executive and managerial/administration.
As suggested by these results, upper-income groups
and well-educated individuals were over-represented
in this survey when compared to census demographic
profiles of the Brisbane population. However, this
problem has also been experienced in other similar
studies conducted in the United States as presented
by Polydoropoulou and Khattak [16] and shown in
Table 3.
Respondents preferences for trafficinformation sources
Travellers on this corridor received information from
a variety of sources. Table 4 lists the current sources
of traffic information as indicated by the respondents.
Most respondents indicated that their primary source
of traffic information was radio traffic reports (74%)
and their own observation (64%). About 23% indicated
that they relied on variable message signs (VMS) as a
source of traffic information on their usual route.
These results clearly indicate that the implemen-tation of strategically located and credible VMS has
the potential to influence drivers route choice deci-
sions. Other traffic information sources such as the In-
ternet and in-vehicle navigation systems were rarely
used by respondents. This may be attributed to the lim-
ited market penetration rates of in-vehicle navigation
systems and the lack of Internet-based traffic informa-
tion systems in Brisbane at the time the survey was
conducted.
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Table 4 Current traffic information sources
Information source Frequencya (%)
Radio traffic reports 74
Own observation 64
Electronic message signs 23
Conversations with other people 10
Mobile phone 4
Printed matter 2
Television 2
Home/office telephone 1
In-vehicle navigation system 1
Internet 1
Other 0
aMultiple responses allowed
Table 5 Respondents frequency of route change in theprevious month
No. of times respondentschanged route in
previous month No. of respondents Percentage
None 37 25.5
1 14 9.7
2 25 17.2
3 19 13.1
4 17 11.7
5 17 11.7
6+ 16 11.0
Frequency of route change and causesof unexpected congestion
The frequency of route change was examined by asking
the respondents to provide information on the number
of times they changed routes in the past month, in re-
sponse to the presence of congestion on their usual
routes. The results are displayed in Table 5.
On average respondents took an alternative route
3.6 times in a month (about 20 working days). How-
ever, it is believed that the proportion of drivers com-
plying with travel information can be increased by de-signing effective and credible traveller information sys-
tems. This is discussed in more detail in the next sec-
tions dealing with the content of the messages and the
type of information provided.The causes of unexpected
congestion experienced by respondents is presented in
Table 6.
Construction and roadworks were reported as the
major causes of unexpected congestion on both the pre-
trip and en route questionnaires. Accidents were also
Table 6 Causes of unexpected congestion
Cause of congestion Pre-trip (%) En-route (%)
Construction/roadworks 58.6 42.2
Accident 44.8 40.6
Disabled vehicle 6.9 10.9
Dont know 6.9 21.9
Bad weather 3.4 7.8
Other 0.0 0.0
a significant source of unexpected congestion. During
the time of the distribution of this survey, major main-
tenance and construction works were being conducted
on Coronation Drive and adjoining networks possibly
accounting for the severity of unexpected congestion
caused by roadworks. This result confirms the need
for advanced freeway and arterial incident detection
systems capable of detecting incidents in the shortestpossible time and making this information available to
the travelling public through advanced traveller infor-
mation systems.
En route responses to hypothetical ATIS messages
The effect of providing alternative forms of en route
information was evaluated. Respondents were asked
to state whether they would change travel decisions if
they were alerted of delays. Each of the five different
information types being tested were then presented to
respondents.
Qualitative information
In this situation the device only offered a simple mes-
sage: Unexpected Congestion on your usual route
(Fig. 2), where your usual route is the road that re-
spondents indicated they normally used for their travel.
While this is simple information, it represents the type
of information that is commonly available to travellersvia radio or VMS.
Fig. 2 Qualitative information message
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Fig. 3 (a) Quantitative information messageusual route. (b)Quantitative informationmessageusual and best alternateroutes
Fig. 4 Predictive information message
Quantitative information
For this scenario, respondents were provided with the
same message as before, but the device also displayed
the expected delays on the usual route and the traveltime on the best alternate route (as specified by respon-
dents), as shown in Fig. 3.
Predictive information
For this scenario, respondents were asked how they
would modify their travel choices if thedevice provided
them with the delays at the present time, and accurately
predicted the expected delays in 15 and 30 min into the
future, as shown in Fig. 4.
Prescriptive information
This scenario explored the response to recommenda-
tions offered by the ATIS which suggested taking the
route which the respondents indicated was their best
alternative route to their destination (Fig. 5).
Respondents were asked to indicate their prefer-
ences when presented with hypothetical ATIS informa-
tion by choosing from a set of finite responses which
Fig. 5 Prescriptive information message
included definitely take my usual route; probably
take an alternative route; definitely take best alterna-
tive route; probably take best alternative route and
cant say. A summary of respondents choices is pre-
sented in Table 7. These results provide one of the most
significant findings from the ATIS experiment. They
clearly indicate that prescriptive, predictive and quan-
titative real-time delay information provided for both
the usual and best alternate routes are most effective ininfluencing respondents to change their routes. There-
fore, detailed route choice decision models were devel-
oped and investigated for each of these ATIS scenarios
as discussed next.
Model structure and development tools
Driver behaviour models that can be used to dynami-
cally estimate or predict the degree of drivers compli-
ance with traffic information can be thought of as a clas-sification problem. The inputs to the model comprise
drivers individual socio-economic characteristics and
other variables that may influence their route choice
behaviour; and the output a binary integer (1,0) repre-
senting whether drivers comply with travel advice or
not, respectively. Two approaches are available for de-
veloping such models: discrete choice models and arti-
ficial neural network techniques. Detailed information
about the theoretical foundations of these techniques
is beyond the scope of this paper. However, a compre-
hensive treatment of discrete choice models and theirapplications can be found in [4] and a detailed review
of artificial neural networks and their applications in
the transportation field can be found in [3]. Hawas [5]
provides a state-of-the-art review of route choice mod-
els where he discusses the strengths and weaknesses
of the two approaches and presents recent advances in
model formulations aimed at addressing their limita-
tions. The review clearly points to the limitation of the
discrete choice approach in modelling the vagueness
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Table 7 En-route stated preferences for unexpected congestion
Qualitative Quantitative Quantitative RT Predictive Prescriptive
delay real-time delay delay on best real-time delay best alternate
Attributes information information alternate route information route
Definitely take my usual route 12.0 10.3 8.0 9.3 6.3
Probably take my usual route 28.9 29.5 12.0 16.0 13.9
Definitely take my best alternate route 32.5 29.5 41.3 41.3 53.2
Probably take my best alternate route 25.3 24.4 33.3 29.3 22.8
Cant say 2.3 7.7 5.3 5.3 3.8
(or fuzziness) of driver behaviour. The author cites a
number of studies where fuzzy logic was effectively
used to capture the variability of drivers appraisal of
the different route attributes as well as the variabil-
ity in their perceptions to the various attribute levels.
In particular, the use of neuro-fuzzy systems, where
fuzzy logic is coupled with the neural networks train-ing capability to replicate complex system behaviour,
was highlighted as a technique with potential promise.
The neural network training basically uses examples
of real-life behaviour to calibrate the fuzzy sets param-
eters and knowledge base with the aim of replicating
drivers route choice behaviour. Evidence of the poten-
tial for using neural network models for route choice
can be found in a number of studies which conducted
comparative evaluations of the discrete choice and neu-
ral network approaches [6, 12]. These studies showed
that neural networks can provide comparable if notbetter approximations to route choice problems. For
this study, neural network techniques were selected as
the modelling paradigm to predict drivers compliance
with travel information.
Neural network route choice modelling framework
The development of neural network models involves
a number of steps which include data collection, se-
lection of input variables, assignment of desired out-
put states, creation of training and validation data sets,selection of neural network architecture (e.g. number
of nodes in the hidden layer, number of hidden lay-
ers, objective function, learning algorithms and node
transfer functions, etc.), training strategy and selection
of key performance indicators to be used for evaluat-
ing the performance of the model. The neural network
route choice models presented in this paper will only
address drivers en route responses to traffic delay in-
formation. The treatment of drivers compliance with
Table 8 Data sets for model development
Data set ATIS message types Sample size
1 Qualitative delay information 80
2 Quantitative real-time delay
information 74
3 Quantitative real-time delay on best
alternative route 714 Predictive delay information 70
5 Prescriptive best alternative route 71
pre-trip travel information is the subject of a separate
study and will not be reported here.
Data for model development
The five data sets that were used for model development
are shown in Table 8 below. Each data set comprisesrespondents replies to each of the ATIS message types
described before (Table 7 and Figs. 25).
Selection and coding of input and output variables
As was mentioned before, drivers route choice deci-
sions are related to their socio economic characteris-
tics and travel habits. The inputs selected for this study
comprised income, education level, age, gender, years
at residence (a surrogate for familiarity with road net-
work conditions) and work schedule flexibility. A num-ber of studies [5] also found that these inputs have di-
rect impact on drivers responses to travel advice. Trav-
ellers responses to each of these inputs were coded ac-
cording to the categories shown in Table 9. Work sched-
ule flexibility, age and awareness or familiarity with
road network conditions are considered factors that im-
pact drivers aggressiveness and hence may influence
their decisions on route choices when provided with
real-time traffic information [9]. Drivers willingness
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Table 9 Variables used for model development
Input data sets
Input variable Category description Categorised value Normalised value
Work schedule flexibility Flexible 1 0.00
Fixed 2 0.50
Variable 3 1.00Age Under 18 years 1 0.00
Between 18 and 29 years 2 0.20
Between 30 and 39 years 3 0.40
Between 40 and 49 years 4 0.60
Between 50 and 64 years 5 0.80
Over 65 years 6 1.00
Gender Male 1 0.00
Female 2 1.00
Income level Under 20 thousand $AUD 1 0.00
Between AUD $20,000 and AUD $40,000 2 0.20
Between AUD $40,000 and AUD $60,000 3 0.40
Between AUD $60,000 and AUD $80,000 4 0.60Between AUD $80,000 and AUD $100,000 5 0.80
More than AUD $100,000 6 1.00
Education level High school or less 1 0.00
Vocational or technical school 2 0.33
Undergraduate degree 3 0.66
Post graduate degree 4 1.00
Years at residence Under 5 years 1 0.00
Between 5 and 10 years 2 0.25
Between 10 and 15 years 3 0.50
Between 15 and 20 years 4 0.75
More than 20 years 5 1.00
to pay for travel information using ATIS technologies
and services is clearly a function of their income and
has also been found to be related to age and gender [17].
Having determined the relevant inputs, the neural
network architecture can be formulated as shown in
Fig. 6 where the outputof the model represents whether
a driver complies or not with the travel information.
Drivers compliance
Drivers compliance was captured in the behaviouralfield survey through the respondents answers to the
different ATIS scenarios. For example, drivers are said
to comply with travel information if their response to
the specific ATIS scenario was definitely take my best
alternate route. Similarly, they are said to ignore the
travel advice if their responses were definitely take
my usual route. For the purpose of model develop-
ment, these two response or model output categories
were coded as 1.0 and 0.0, respectively. Respondents
also had two other options to choose from if they were
undecided on which route to choose. If the respon-
dent selected either probably take my usual route or
probably take my best alternate route, then these two
response categories will need to be coded as values be-
tween (0, 0.5) and (0.5, 1.0), respectively. A number
of experiments were conducted where different values,
e.g. (0.33, 0.67), (0.3, 0.7) and (0.4, 0.6) were coded for
these categories but no differences were found in terms
of the neural network model performance. The four-
point semantic scale of drivers route choice selectedin this study is shown in Table 10.
Selection of neural network architecture
Four neural network architectures are typically used for
classification problems [10]: Fuzzy ARTMAP, Back
Propagation (Logicon Projection Network), Reinforce-
ment Network and Radial Basis Function Network
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Fig. 6 Basic structure of neural network agent-based (Neugent) route choice model
(RBFN). A brief description of each of these archi-
tectures is provided below.
(1) Fuzzy ARTMAPis a general-purpose classification
network. It has two fuzzy ART networks (ARTa
and ARTb) which are connected by a subsystem
referred to as a match tracking system which in-
creases the vigilance of the Fuzzy ART network
until a match is made or a new category is formed.
(2) Back-Propagation is a general-purpose network
paradigm. Back-prop calculates an error between
desired and actual outputs and propagates the error
information back to each node in the network. The
back-propagated error drives the learning at each
node.
(3) Radial Basis Function Networks (RBFN) are
general-purpose networks which can be used for
a variety of problems including classification, sys-
tem modelling and prediction. In general, an RBFN
Table 10 Coding of route choice preferences
Strength of Choice transformation
preference scale
Definitely take my usual route 0.00
Probably take my usual route 0.33
Probably take my best alternate route 0.67
Definitely take my best alternate route 1.00
is any network which makes use of radially sym-
metric and radially bounded transfer functions in
its hidden (pattern) layer.
(4) Reinforcement Networksrefer to learning schemes
which iterate through a number of steps until per-
formance no longer improves. A set of weights
is selected in some methodical or semi-random
way depending on previously selected and saved
weights. Then, the performance of the network is
assessed by running the training data through the
network and evaluating an objective function. If the
weights show an improved performance, they are
saved, replacing some previously saved weights.
Performance measures
One of the main indicators to evaluate the performance
of a neural network classifier is the Classification Rate.
This indicator provides a measure of the correctly clas-sified inputs and is best depicted using the classification
rate matrix shown in Fig. 7.
The columns of the matrix represent the actual or
desired results whereas the rows represent the neural
network estimates or outputs. The body of the Classifi-
cation Rate matrix represents the intersection between
desired results and actual network predictions. A value
of 1.0 for any given cell means that all desired out-
puts for that category were correctly predicted by the
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Fig. 7 Classification ratematrix
network. Similarly, a value of 0.0 in a cell means that
none of the desired outputs were predicted to be a mem-
berof that category. A perfect classifierwould comprise
a value of 1.0 for the correctly classified states and zero
for the Type I and Type II errors. Classification rates ob-
tained during the training or model development stageprovide a measure of the calibration results while clas-
sification rates obtained during testing provide an indi-
cation of the generalisation ability and validity of the
model.
Learning rules and transfer functions
The learning rule represents the mathematical algo-
rithm used to determine the increment or decrement
by which weights of a processing element (PE) change
during the learning phase. Examples of such algo-rithms include Maxprop, Delta Prop and Genetic Al-
gorithms [11]. A transfer function is typically a non-
linear function that transforms the weighted sum of the
effective inputs to a potential output value [11]. The se-
lection of learning rules and transfer functions depends
on the individual ANN architecture and are typically
arrived at through comprehensive testing. The learn-
ing rules, transfer functions and other parameters that
were selected and tested in this study are shown in
Table 11.
A large number of experiments were run to de-termine the best architecture and set of parameters
for the route choice compliance problem. These in-
cluded five different Fuzzy ARTMAP architectures
(each with a different mapping layer); 10 different
back-propagation and Radial Basis architectures and
12 Reinforcementnetwork models. In total,185 models
with different architectures were developed and tested.
Different experiments were run using the categorised
and normalised data. The best performance models are
shown in Tables 12 and 13 for the categorised and nor-
malised input data, respectively.
The results in Tables 12 and 13 show the superior
performance of the Fuzzy ARTMAP and RBFN archi-
tectures over other neural network architectures. The
results also show an improved classification rate whendata is categorised. These results suggest that predic-
tion errors between the ARTMAP and RBFN models
are negligible and that adoption of either model is ac-
ceptable for each of the five ATIS scenarios. Closer
inspection of the model formulations, however, re-
vealed that the RBFN model had fewer parameters
to calibrate than the Fuzzy ARTMAP model and was
hence selected as the architecture for implementation
in the traffic simulator. In the remainder of this docu-
ment, this model will be referred to as the Neugent
model.
Interface to microscopic simulation
To demonstrate the feasibility of the approach, the Neu-
gent driver behaviour model was interfaced to a traf-
fic simulation tool. The traffic simulator was used to
govern individual vehicular movements on the traffic-
commuting corridor using car following, lane chang-
ing and gap acceptance behaviour. Given the static
characteristics of the commuting corridor network, itslink capacities, connections and speeds, the simulator
takes a time-dependent loading pattern and handles the
movement of individual vehicles on links as well as
the transfer between links. The routing of individual
vehicles under the influence of real-time information
is then provided by the driver behaviour model devel-
oped in this study. This model specifies how a particu-
lar drivervehicleunit (DVU) with given goals, char-
acteristics, preferences and knowledge of prevailing
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Table 11 Neural network architectures
Model Model Learning Transfer Mapping
architecture description rule function layer (F2)
Fuzzy ARTMAP network Baseline vigilance = 0.0 (between 01.0) N.A. N.A. 50
Recode rate = 0.5 (between 0.21.0) 100
Choice parameter= 0.1 150
Error tolerance = 0.01 200
250
Back-propagation network LCoef (Learning rate) 0.3 for 1st hidden,
0.25 for 2nd hidden, 0.15 for output Delta rule TanH N.A.
Momentum = 0.4 Nom-Cum Linear
Trans. Pt. = 10,000 Ext DBD Sigmoid
LCoef Ratio = 0.5 QuickProp DNNA
F offset = 0.1 MaxProp Sine
DBD
Radial basis function networks LCoef (Learning rate) 0.3 for 250 Proto,
0.25 for hidden, 0.15 for output Delta rule TanH N.A.
Momentum = 0.4 Nom-Cum Linear
Trans. Pt. = 10000 Ext DBD SigmoidLCoef Ratio = 0.5 QuickProp DNNA
Max. Trans. = 2000 MaxProp Sine
DBD
Reinforcement networks LCoef (Learning rate) 0.3 for 250 Proto,
0.25 for hidden 0.15 for output Genetic TanH N.A.
Momentum = 0.4 DRS Linear
Trans. Pt. = 10000 Sigmoid
LCoef Ratio = 0.5 Exp
Max. Trans. = 2000 Perceptron
Sine
conditions, selects the next link under the influence oftraffic information.
The commercially available microscopic traffic sim-
ulation model AIMSUN [19] was selected for model
testing. The selection was based on a comparative eval-
uation of the car following behaviour in a number of
simulation tools which showed that the Gipps model
implemented in AIMSUN performed better than the
family of psychophysical car following models imple-
mented in PARAMICS and VISSIM [13]. This sim-
ulation software also has a number of unique fea-
tures which allow the user to customise many fea-tures of the underlying simulation model through an
application programming interface (API). This feature
is critical for this study, as it will allow for interfac-
ing the driver behaviour model with the traffic simu-
lation tool. The traffic simulation will follow a deter-
ministic, fixed time-step approach. At each time step
(e.g. 1 s), vehicles will be moved at the prevailing lo-
cal speed on the same link or transferred to another
link. The latter is determined by the driver behaviour
model according to user preferences, perceptions, path-switching thresholds and other factors identified and
discussed in this study. Travel times are then calcu-
lated from each node based on the current link speeds
and the simulation continues until all DVUs reach their
destinations.
For each of the ATIS scenarios presented in Ta-
ble 7, the Neugent route choice model was coded
and interfaced to a hypothetical AIMSUN network to
evaluate the impacts of providing drivers with travel
information.
A hypothetical case study
The Neugent model developed in this study was tested
on a simple hypothetical network (Fig. 8) in which
three routes between zones A and D are clearly marked
(main route ABCD and alternative routes AEC
D and AEFD). Intersections A, B, C and D are all
signalised.
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Table 12 Best performance models using categorised input data
ATIS scenario ANN architecture Learning rule Transfer function Classification rate (CR)
Qualitative delay information Fuzzy ARTMAP (150a) N.A. N.A. 0.9636b
Back-Prop MaxProp Sine 0.5919
RBFN Delta rule Sine 0.9636b
Reinforcement Genetic Perceptron 0.7237
Quantitative real-time delay information Fuzzy ARTMAP (100a) N.A. N.A. 0.9661b
Back-Prop MaxProp Sine 0.7311
RBFN DBD Sine 0.9412c
Reinforcement Genetic Sine 0.7802
Quantitative real-time delay on best Fuzzy ARTMAP (250a) N.A. N.A. 0.9545c
alternative route
Back-Prop MaxProp TanH 0.6162
RBFN Delta Rule Sine 0.9589b
Reinforcement DRS Perceptron 0.7537
Predictive delay information Fuzzy ARTMAP (50a) N.A. N.A. 0.9615b
Back-Prop Delta rule TanH 0.5844
RBFN Delta rule Sine 0.9252c
Reinforcement Genetic Sine 0.7436Prescriptive best alternative route Fuzzy ARTMAP (250a) N.A. N.A. 0.9732b
Back-Prop DBD Linear 0.6375
RBFN Delta rule TanH 0.9488c
Reinforcement Genetic Linear 0.7815
aNumber of mapping layers (F2 units) for the Fuzzy ARTMAP networkbBest model based on classification ratecSecond best model based on classification rate
Fig. 8 A hypotheticalnetwork with three
alternative routes
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Table 13 Best performance models using normalised input data
ATIS scenario ANN architecture Learning rule Transfer function Classification rate (CR)
Qualitative delay information Fuzzy ARTMAP (250a) N.A. N.A. 0.9663b
Back-Prop MaxProp Sine 0.5950
RBFN Delta rule Sine 0.9636c
Reinforcement Genetic Perceptron 0.7205
Quantitative real-time delay information Fuzzy ARTMAP (200a) N.A. N.A. 0.9661b
Back-Prop MaxProp Sine 0.7395
RBFN DBD Sine 0.9412c
Reinforcement Genetic Sine 0.7548
Quantitative real-time delay on best Fuzzy ARTMAP (150a) N.A. N.A. 0.9589b
alternative route
Back-Prop Nom-Cum TanH 0.7711c
RBFN Nom-Cum TanH 0.7711c
Reinforcement DRS Perceptron 0.6902
Predictive delay information Fuzzy ARTMAP (100a) N.A. N.A. 0.9615b
Back-Prop Delta rule TanH 0.5919
RBFN Nom-Cum TanH 0.8889c
Reinforcement Genetic Sine 0.8889Prescriptive best alternative route Fuzzy ARTMAP (200a) N.A. N.A. 0.9732b
Back-Prop DBD Linear 0.5667
RBFN Delta rule TanH 0.8667c
Reinforcement Genetic Linear 0.7929
aA total number of F2 units for Fuzzy ARTMAPbBest model based on classification ratecSecond best model based on classification rate
The main route commonly travelled by drivers
is route ABCD. Drivers also have the option of
diverting through routes AECD or AEFD incase of congestion on the main route. Familiar drivers
are assumed more likely to choose route AEFD
to avoid the delays caused by the traffic signals on the
main route. A variable message sign (VMS) is also sim-
ulated in this experiment and was strategically located
at a control point before node A to provide drivers with
information about delays along the main and alternative
routes. DVUs approaching the control point will assess
their environments (using the driver behaviour mod-
els) and decide whether they are going to comply with
the information provided on the VMS. Drivers whohave in-vehicle devices can access real-time traffic in-
formation through the device while driving. Drivers
responses and compliance with traffic information (for
both VMS and in-vehicle device) is provided by the
Neugent driver behavioural model developed in this
study. The following factors affecting drivers compli-
ance were considered in this case study: driver aggres-
sion, driver awareness or familiarity with the network,
VMS credibility and delay tolerancethresholds for both
familiar and unfamiliar drivers. An interface which ac-
tivates route guidance within the model via variable
message signs (VMS) or in-vehicle devices was alsodeveloped for this experiment (Fig. 9).
The plug-in allows manipulation of the ITS infor-
mation strategies influencing driver compliance via the
graphical user interface (GUI). Under non-congested
conditions, the VMS would be blank, indicating that
conditions are free-flowing. As congestion builds on
the main route and reaches a threshold (determined
by the minimum delay and minimum travel time),
the VMS is activated and displays delay informa-
tion to drivers. Each driver is assigned a set of val-
ues (from a distribution function) representing age,gender, aggressiveness, awareness, acceptable delay
threshold and familiarity with network conditions as
determined from the behavioural survey. The plug-
in further assumes that drivers are classified into one
of the following three categories: informed drivers
(e.g. drivers who subscribe to en route traffic infor-
mation services); familiar drivers (e.g. drivers who
have lived in the designated area for an extended pe-
riod of time and are familiar with traffic conditions on
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Table 14 Additional benefits from implementing dynamic driver compliance (Neugent) models
Percentage improvements over static C-Logit
Network Network Stop time Network travel
ATIS scenario speed delay per vehicle time
Qualitative delay information 4.12 5.43 6.86 1.74
Quantitative real-time delay information 4.12 6.09 8.28 0.73Quantitative real-time delay on best alternative route 7.06a 6.90 8.12 3.10
Predictive delay information 4.71 6.90 8.52 3.67a
Prescriptive best alternative route 5.29 8.22a 10.57a 2.09
aBest model result
Fig. 9 GUI linking the neugent model and the traffic simulator
the network); and unfamiliar drivers (e.g. tourists or
drivers not familiar with the road network). The last
two types of drivers are assumed to have no access to
in-vehicle route guidance and that the only information
they receive would be from VMS on the side of the
road. It was further assumed that the three driver cat-egories were equally represented. The simulation was
run for 1 h including a start-up period of 5 min. Default
car following and lane changing parameters were used
to govern vehicular movement between origins and
destinations.
Simulation results
The aim of the simulation experiments was to demon-
strate the additional benefits that can be obtained using
models of driver compliance with traffic informationcompared to what can be obtained using default route
choice models. The base case scenario was therefore
based on the simulators default route choice model
(C-Logit) which does not take into account drivers
compliance with information. The comparative evalu-
ation results are reported in Table 14.
These aggregate results show that quantitative real-
time delay; predictive real-time delay and prescrip-
tive information on best alternative route provide the
best overall benefits in terms of network speed, delay
and network travel times. In other words, these ATISstrategies or scenarios are best used to influence driver
behaviour and impact their compliance rates. These
findings are also supported by a number of other stud-
ies in the literature [14]. Furthermore, the evaluation
results which compared the benefits of the Neugent
model over C-Logit clearly show improvements of 4
7% in network speeds, 58% in network delays, 711%
in stop time per vehicle and 13% in network travel
time.
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Conclusions
The results reported in this paper provide a useful in-
sight into commuters needs and preferences for traffic
information in the Brisbane metropolitan area. Unlike
other studies which relied on generalised driver char-
acteristics, this study was based on a field behaviouralsurvey of drivers which was conducted on a congested
real-world commuting corridor. A substantial amount
of information regarding driver preferences and be-
haviour were collected and a number of neural network
agent-based dynamic driver behaviour models were de-
veloped to determine the factors affecting driver com-
pliance and response to a variety of traffic information
sources. These models clearly indicated that prescrip-
tive, predictive and quantitative real-time delay infor-
mation provided for both the usual and best alternate
routes were most effective in influencing respondentsto change their routes. The behavioural models describ-
ing drivers route choice decisions were interfaced to
a microscopic traffic simulation tool which was cali-
brated to simulate the movement of individual vehicles
between their origins and destinations. The simula-
tion results supported the notions that commuters de-
cisions to divert to alternate routes are influenced by
their socio-economic characteristics; the degree of fa-
miliarity with network conditions and the expectation
of an improvement in travel time that exceeds a certain
delay threshold associated with each individual. Theimplementation of the Neugent models developed in
this study have been shown to produce benefits over
the C-Logit route choice algorithm with improvements
of 47% in network speeds, 58% in network delays,
711% in stop time per vehicle and 13% in network
travel times.
Acknowledgements The authors would like to thank theanony-mous reviewers for their valuable comments and constructivefeedback on the paper. The authors also acknowledge the as-
sistance provided by David Lockie, Katherine Johnston, DanielHarney and Andrew Boyle with the behavioural survey, data col-lection and pre-processing.
Disclaimer
This paper presents the opinion of the authors based on
their research results and does not necessarily represent
the views or policy of the ITS Research Laboratory
at the University of Queensland. This paper does not
constitute a recommendation or standard, and it is for
general information only and should not be relied upon
situationally or circumstantially. Neither the authors
nor the University are responsible for the use which
might be made of these results.
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