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

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

Evaluation of microscopic car-following behaviour

Microscopic traffic simulation tools are increasingly being applied to deal with dynamic and operational traffic problems and ITS applications. Each tool has its own formulation of microscopic traffic behaviour and this has an impact on the accuracy of the model in replicating traffic behaviour on the road. This paper describes the car-following models implemented in a number of commercial traffic simulation tools. The paper then describes the application of these simulators to an experimental data set that was collected from a European study. The field experiment recorded the speed and relative distance of the follower and leader vehicles on a one-lane road using especially equipped vehicles. The data from the study was used to test the car following behaviour of a number of microscopic traffic simulators and the initial findings are reported in this paper

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

Abstract not available

Modelling dynamic driver behaviour in response to travel information

Abstract not available