Sakda Panwai

Neural Agent Car-Following Models

This paper presents a car-following model that was developed using a neural network approach for mapping perceptions to actions. The model has a similar formulation to the desired spacing models that do not consider reaction time or attempt to explain the behavioral aspects of car following. The models performance was evaluated based on field data and compared to a number of existing car-following models. The results showed that neural network models outperformed the Gipps and psychophysical family of car-following models. A qualitative drift behavior analysis also confirmed the findings. The model was validated at the microscopic and macroscopic levels, and the results showed very close agreement between field data and model outputs. Local and asymptotic stability analysis results also demonstrated the robustness of the model under mild and severe traffic disturbances

A reactive agent-based neural network car following model

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.

Evaluation of discrete choice and neural network approaches for modelling driver compliance with traffic information

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.

Vehicle Reidentification With Self-Adaptive Time Windows for Real-Time Travel Time Estimation

This paper proposes a vehicle reidentification (VRI) system with self-adaptive time windows to estimate the mean travel time for each time period on the freeway under traffic demand and supply uncertainty. To capture the traffic dynamics in real-time application, interperiod adjusting based on the exponential smoothing technique is introduced to define an appropriate time-window constraint for the VRI system. In addition, an intraperiod adjusting technique is also employed to handle the nonpredictable traffic congestion. To further reduce the negative effect caused by the mismatches, a postprocessing technique, including thresholding and stratified sampling, is performed on the travel time data derived from the VRI system. Several representative tests are carried out to evaluate the performance of the proposed VRI against potential changes in traffic conditions, e.g., recurrent traffic congestion, freeway bottlenecks, and traffic incidents. The results show that this method can perform well under traffic demand and supply uncertainty.

Traffic Impact Assessment of Incident Management Strategies

This paper presents results from a simulation study which aimed to quantify the impacts of incident management strategies. The evaluation was based on a large-scale micro-simulation model covering an area approximately 122 square kilometres, including 43 kilometres of motorway and about 85 kilometres of surface roads on the Gold Coast, Australia. The study examined the effectiveness of selected incident management strategies including ramp metering, VMS information dissemination combined with route diversions, and variable speed limit systems. The provision of VMS information on the motorway and dynamic adjustment of signal timings on the diversion route resulted in equilibrium conditions and balanced distribution of traffic on both the normal and diversion routes when the optimal diversion rate was 30 percent. This resulted in reduction of delays by 8.8 percent, decrease in number of stops by 22 percent, and decrease in travel times by 3.3 percent. An important finding was that these benefits are only realised when the two incident management responses (VMS route diversion and dynamic traffic signal plans on surface roads) are implemented at the same time. Combined, their impact was such that they resulted in restoration of traffic conditions to the pre-incident situation. The paper also reports on a preliminary investigation into variable speed limits (VSL) as a means to reduce the negative impacts of incidents. The results showed that VSL had the potential to provide an 11 percent improvement in efficiency and also contributed to improving safety by homogenising the flow in higher speed regimes.

Comparative evaluation of power-based environmental emissions models

This paper presents findings from a comparative evaluation of two Australian power-based models of fuel consumption and environmental emissions using traffic simulation. The simulation approach provides a number of advantages for this type of work through detailed modelling of road geometry and grade, driver behaviour, vehicle performance and traffic parameters. The power-based models were interfaced to a microscopic traffic simulator and applied to each vehicle on the road network. Second-by-second fuel consumption and pollutant emissions were estimated on a section-by-section and network-wide basis. The simulation study examined a number of abstract vehicle types and gathered detailed information about their speed profiles, acceleration, deceleration, fuel consumption and pollutant emissions. The results showed that the fuel consumption outputs were similar but the emission estimates were substantially different for the two models. The paper also compared the outputs of the two models against various standard tests. The results showed that both models provided higher rates of pollutant emissions when compared with available standard test data. The paper concludes with some recommendations for further research to enhance the performance of the models

Neural Agent (Neugent) Models of Driver Behavior for Supporting ITS Simulations

This paper presents an agent-based neuro-fuzzy approach for modeling drivers’ compliance with travel advice under the influence of real-time traffic information. Fuzzy logic is combined with neural networks to capture the variability of drivers’ appraisal of the different route attributes as well as the variability in their perceptions of the various attribute levels. The accuracy of the models, in terms of predicting the categories of drivers likely to comply with traffic advice, was found to exceed 90%. A comparative evaluation with discrete choice models showed higher accuracies ranging between (91 and 96) percent compared to (50–73) percent for the binary choice models.

Impact of Driving Behaviour on Emissions and Road Network Performance

This paper presents findings from a simulation-based comparative evaluation of driving behaviours and their impacts on road safety, environmental quality and network efficiency. Driving behaviour was represented by driver speed, acceleration, lane changing and gap acceptance actions. A fourmode elemental emissions model was used to collect second-bysecond data on fuel consumption and CO2 emissions. Surrogate measures of safety, expressed in terms of the number of lane changes and severe decelerations, were used to describe the degree of safety in the simulation experiments. Aggressive drivers were found to be 35 times more likely to be involved in a crash on the motorway, and two times more likely to be involved in a crash on the urban network. The results for the motorway simulations also showed that aggressive drivers achieved only a 3.8 percent reduction in travel times (62 seconds on a 26 minute trip) at the expense of 85 percent more lane changes and 332 percent increase in fuel consumption and CO2 emissions. The reduction in travel times for urban conditions was lower at around 1.6 percent (7 seconds on a 434 second trip) at the expense of 300 percent more lane changes and 138 percent increase in fuel consumption and CO2 emissions. Sensitivity analysis of the impacts of varying proportions of drivers was also conducted. The results showed that the negative impacts of aggressive driving behaviour outweigh by a factor of three any benefits that can be obtained through reductions in travel times.