Abstract-Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a traffic-simulation system depends highly on the quality of the traffic-flow model at its core, with the two main critical components being the car-following and lane-changing models. This paper presents findings from a comparative evaluation of car-following behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Stadten-simulation (VISSIM)]. The car-following algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a car-following experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a one-lane road. The experiment was replicated in each tool and the simulated car-following behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gipps-based models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative "drift and goal-seeking behavior" test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings.

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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, predictive and 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 information. 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 eachcommuter. 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 4-7% in network speeds; 5-8% in network delays; 7-11% in stop time per vehicle and 1-3% in network travel times.

This article evaluates dynamic driver behaviour models that can be used, in the context of intelligent transport systems (ITS), to predict drivers' compliance with traffic information. The inputs to this type of models comprise drivers' individual socio-economic characteristics and other variables that may influence their compliance behaviour. The output is a binary integer representing whether drivers comply with travel advice or not. Two approaches are available for formulating this category of classification problems: discrete choice models and artificial neural networks (ANNs). The literature on this topic clearly points to the limitations of the discrete choice approach which suffers from assumptions of perfect information about travel conditions, infinite information processing capabilities of drivers and inability to model the uncertainty in driver decision making or the vagueness in information received from ITS devices. ANNs, on the other hand, are able to deal with complex non-linear relationships, are fault tolerant in producing acceptable results under imperfect inputs and are suitable for modelling reactive behaviour which is often described using rules, linking a perceived situation with appropriate action. This study aims to evaluate the performance of these two categories of models based on a common data set of driver behaviour, collected from a field behavioural survey on a congested commuting corridor in Brisbane, Australia. This article proposes the combination of fuzzy logic and neural networks as a viable approach for overcoming the limitations of existing algorithms by modelling drivers as heterogeneous individuals. The results showed superior performance for a neuro-fuzzy model over binary choice models in terms of classifying or predicting the categories of drivers most likely to comply (or not comply) with traffic advice. The accuracy of the proposed model, in terms of classification rate, ranged between 95 and 97% compared to 50-73% for the discrete choice models.

Abstract-This paper presents a car following model which was developed using reactive agent techniques based on a neural network approach for mapping perceptions to actions. The model has a similar formulation to the desired spacing models which do not consider reaction time or attempt to explain the behavioural aspects of car following. A number of error tests were used to compare the performance of the model against a number of established car following models. The results showed that simple back-propagation neural network models outperformed the Gipps and Psychophysical family of car following models. A qualitative drift behaviour analysis also confirmed the findings. For microscopic validation, speed and position of individual vehicles computed from the model were compared to field data. Macroscopic validation involved comparison of the field data and model results for trajectories, average speed, density and volume. Model validation at the microscopic and macroscopic levels showed very close agreement between field data and model results.