Comparative evaluation of microscopic car-following behavior

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 Statiten-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

A Reactive Agent-based Neural Network Car Following Model

This paper presented a novel approach to develop car following models using reactive agent techniques for mapping perceptions to actions. The results showed that the model outperformed the Gipps and Psychophysical family of car following models. The standing of this work is highlighted by its acceptance and publication in the proceedings of the International IEEE Conference on Intelligent Transportation Systems (ITS), which is now recognised as the premier international conference on ITS. The paper acceptance rate to this conference was 67 percent. The standing of this paper is also evidenced by its listing in international databases like Ei Inspec and IEEE Xplore. The paper is also listed in Google Scholar. Dr Dia co-authored this paper with his PhD student Sakda Panwai

Calibrating Car-Following Models using Trajectory Data: Methodological Study

The car-following behavior of individual drivers in real city traffic is studied on the basis of (publicly available) trajectory datasets recorded by a vehicle equipped with an radar sensor. By means of a nonlinear optimization procedure based on a genetic algorithm, we calibrate the Intelligent Driver Model and the Velocity Difference Model by minimizing the deviations between the observed driving dynamics and the simulated trajectory when following the same leading vehicle. The reliability and robustness of the nonlinear fits are assessed by applying different optimization criteria, i.e., different measures for the deviations between two trajectories. The obtained errors are in the range between~11% and~29% which is consistent with typical error ranges obtained in previous studies. In addition, we found that the calibrated parameter values of the Velocity Difference Model strongly depend on the optimization criterion, while the Intelligent Driver Model is more robust in this respect. By applying an explicit delay to the model input, we investigated the influence of a reaction time. Remarkably, we found a negligible influence of the reaction time indicating that drivers compensate for their reaction time by anticipation. Furthermore, the parameter sets calibrated to a certain trajectory are applied to the other trajectories allowing for model validation. The results indicate that ``intra-driver variability'' rather than ``inter-driver variability'' accounts for a large part of the calibration errors. The results are used to suggest some criteria towards a benchmarking of car-following models

MODELLING DRIVER BEHAVIOUR UNDER THE INFLUENCE OF TRAFFIC INFORMATION

This thesis presents the development of a new generation of dynamic behaviour models that can be used to describe drivers route choice decisions under the influence of traffic information. The new models can be incorporated in traffic simulation tools to help improve their accuracy in evaluating Intelligent Transport Systems (ITS) applications that aim to influence driver behaviour through the use of traffic information and advice. The motivation for this research stemmed from a recognition of the limitations of existing route choice models which were found to lack the necessary features to allow for modelling the impacts of ITS and in particular modelling the behaviour of heterogeneous drivers and dealing with the vagueness inherent in driver decision making and the information received from ITS devices and the road environment. Existing models of drivers route choice assume that commuters have full knowledge about traffic and road conditions. However, commuters in the real world are highly heterogeneous and have imperfect or incomplete knowledge about traffic information. This research aims to address these limitations by using a relatively new software paradigm (based on agent technologies) which can be used to model drivers as heterogeneous individuals using artificial intelligence techniques based on neural networks and fuzzy logic. The models will be described using individual characteristics, preferences on route choice and attributes based on field surveys. The thesis first explores the potential for using binary choice and artificial neural network (ANN) models for describing driver route choice and compliance with traffic information. The performance evaluation results showed superior performance for the ANN models over the 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 ANN models was about 96 percent compared to 61 percent for the discrete choice models. Therefore, the ANN modelling approach was adopted and the resulting model was referred to interchangeably as the Neural or Neural Agent or Neugent Driver Behaviour Model. One of the main limitations of the Neugent model, however, was its inability to interpret the ANN results and derive behavioural rules. This was addressed in this thesis by developing a fuzzy-neural approach where the fuzzy logic provided a mechanism for representing precise and imprecise knowledge while the neural networks provided the learning capability by using examples of real-life behaviour. Given a set of training data, the neural network determines all the fuzzy rules relating input and output patterns. The fuzzy-neural method was shown to be a suitable approach for modelling route choice behaviour and resulted in the development of models that can be interpreted in terms of ifthen rules. The main contribution of this thesis was the development of the resulting models which were referred to interchangeably as Fuzzy-Neural or Fuzzy-Neural Agent or Fuzzy-Neugent driver behaviour models. The Fuzzy-Neugent models comprised a series of three components or separate models: a drivers compliance with traffic information model (to determine if a driver complies with or ignores the travel advice); a drivers delay tolerance threshold model (to determine how each driver responds to delays of different durations) and a driver route utility model (to determine how a driver selects between the alternative routes based on the drivers preferences, familiarity with road conditions etc). The route choice behaviour covered four categories: route attributes (e.g. travel time, travel distance, presence of tolls etc), personal characteristic (e.g. age, gender, education, familiarity with road conditions, income, work-related flexibility), trip characteristics (e.g. work or leisure trip), and road environment conditions (e.g. travel during peak hours under normal and incident conditions). To enable the development of the three constituent models, it was necessary to design a new survey instrument to collect additional information from driver behaviour surveys. The thesis presents the development of a web-based driver behaviour survey which produced a response rate of about 40 percent. Analysis of the reliability and validity of the survey showed a Cronbach alpha value in the range 0.7 to 0.74, indicating that the survey was reliable. The main advantage of the work reported in this thesis is that the survey sample size for a particular region does not need to be very large. This is due to the fuzzy logic formulations which basically allow for a much larger database to be generated from the sample collected in the field through the use of different membership function shapes which were developed in this thesis. This allows for generating data sets that reflect changes in driver characteristics and the environment without the need for collecting large amounts of new survey data. This guarantees that a large number of representative data sets are available for training the Fuzzy-Neugent models. The development of the Fuzzy-Neugent models involved testing a number of model architectures to predict route choice decisions including their calibration, refinement and validation based on a comparison between the models outputs and the web survey data. Classification rate (CR), which is a measure of the models ability to correctly classify field observations, was used as the performance measure (a perfect classifier has a CR of 100 percent). The best performing models produced a CR in the range 88 to 93 percent for the drivers compliance models; a CR of 82 percent for the drivers delay tolerance threshold models; and a CR of 98 percent for the drivers route utility models. Model validation was undertaken using 72 different survey templates which described different route attributes between two alternative routes. Each respondent was randomly assigned a template and asked to indicate their most favourable route for the trip. The respondents socioeconomic and route attribute preferences (which they provided in the survey) were then used to estimate the route utility by using the Fuzzy-Neugent models. The actual real-life decisions were then compared against the predicted choices from the models. The models aggregated results were excellent and showed a classification rate of 94-95 percent. The validation of the models was also aimed at learning how individuals think and make route choice decisions. A classification rate of 75-76 percent was achieved for the disaggregate model validation. These results indicate a very good degree of accuracy given the complexity of driver behaviour at the individual level. Finally, the thesis presented an application of the Fuzzy-Neugent driver behaviour models. The models were interfaced to the traffic simulator AIMSUN NG and used to evaluate the impacts of an ITS application on incident management. A large number of incidents were simulated with varying durations and severity. Drivers were simulated to receive traffic information advice about the incidents and the Fuzzy-Neugent models were implemented to describe the behaviour of drivers under the influence of the traffic advice. At the aggregate level, the Fuzzy-Neugent models were found to provide a 3 percent improvement in network speeds, 2-6 percent reduction in environmental emissions and more than 1 percent improvement in network travel time. Although some of these benefits appear small on the surface, their impacts in financial terms were found to be substantial. At the disaggregate level, the results showed that drivers who accepted the traffic information gained substantial benefits which included savings of 1934 percent in travel time, 43 percent saving in fuel consumption and around 58 percent reduction in environmental emissions. Furthermore, it was found that around 1725 percent of drivers accepted the recommended information; and 1217 percent complied because the delays exceeded their delay tolerance thresholds. These results clearly demonstrate the benefits of the Fuzzy-Neugent driver behaviour models in providing additional information about the impacts of ITS applications. This information would not have been readily available from current or existing models. The results also show that these new models have a very substantial practical role to play. Any road authority wishing to implement the models for a new region would simply complete a basic survey of driver behaviour (e.g. a web-based survey) and then use the model formulations presented in this thesis to generate the large databases required for training the Fuzzy-Neugent models. The simplicity and feasibility of this approach have been successfully demonstrated in this thesis and shown to produce substantial benefits

Intelligent mobility for smart cities: Driver behaviour models for assessment of sustainable transport

The convergence of physical and digital worlds is creating unprecedented opportunities to enhance the travel experience for millions of people every day. A key to the success of these systems is a good understanding of driver behaviour under the influence of travel information. This paper presents the application of a new generation of driver behaviour models, based on neural agent (neugent) techniques, to describe drivers' decisions and compliance with travel information. The new models enhance the capabilities of existing simulation tools in modelling the behaviour of heterogeneous drivers and dealing with the vagueness inherent in driver decision making and the information received from sensors and the road environment. This paper also describes the traffic simulation and practical applications of the new models and how they serve to assess the impacts of smart and sustainable transport interventions

Microscopic, Macroscopic and Stability Analysis of Reactive Agent-based Car Following Models

Abstract not available

Modelling drivers' compliance and route choice behaviour in response to travel information

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 each 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 4-7% in network speeds; 5-8% in network delays; 7-11% in stop time per vehicle and 1-3% in network travel times

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 model's 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