Parallel and distributed simulation of wireless vehicular ad hoc networks
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
VISSIM: a multi-parameter sensitivity analysis
Proceedings of the 38th conference on Winter simulation
MoVES: A framework for parallel and distributed simulation of wireless vehicular ad hoc networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Transactions on Intelligent Transportation Systems
A Markov model for headway/spacing distribution of road traffic
IEEE Transactions on Intelligent Transportation Systems
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Society of Mind cognitive agent architecture applied to drivers adapting in a traffic context
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
| Hi-index | 0.00 |
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.