Calibrating the Wiedemann 99 Car-Following Model for Bicycle Traffic

Andreas Keler,Klaus Bogenberger,Heather Kaths

doi:10.3390/su13063487

Abstract

Car-following models are used in microscopic simulation tools to calculate the longitudinal acceleration of a vehicle based on the speed and position of a leading vehicle in the same lane. Bicycle traffic is usually included in microscopic traffic simulations by adjusting and calibrating behavior models developed for motor vehicle traffic. However, very little work has been carried out to examine the following behavior of bicyclists, calibrate following models to fit this observed behavior, and determine the validity of these calibrated models. In this paper, microscopic trajectory data collected in a bicycle simulator study are used to estimate the following parameters of the psycho-physical Wiedemann 99 car-following model implemented in PTV Vissim. The Wiedemann 99 model is selected due to the larger number of assessable parameters and the greater possibility to calibrate the model to fit observed behavior. The calibrated model is validated using the indicator average queue dissipation time at a traffic light on the facilities ranging in width between 1.5 m to 2.5 m. Results show that the parameter set derived from the microscopic trajectory data creates more realistic simulated bicycle traffic than a suggested parameter set. However, it was not possible to achieve the large variation in average queue dissipation times that was observed in the field with either of the tested parameter sets.

Highlights

Bicycle traffic is becoming increasingly prevalent in many urban areas as citizens, planners, and politicians realize the advantages of utilitarian bicycling
It was not possible to achieve the large variation in average queue dissipation times that was observed in the field with either of the tested parameter sets
These effects cannot be accurately accounted for using microscopic traffic simulation tools unless the behavior models driving the simulation realistically replicate the movement of bicyclists and the flow of bicycle traffic

Summary

Introduction

Bicycle traffic is becoming increasingly prevalent in many urban areas as citizens, planners, and politicians realize the advantages of utilitarian bicycling. 15% in 2017 [1] Such an increase has a direct impact on the volume of bicycle traffic, and as a result, an effect on the overall traffic flow on an urban road network. These effects cannot be accurately accounted for using microscopic traffic simulation tools unless the behavior models driving the simulation realistically replicate the movement of bicyclists and the flow of bicycle traffic. Novel bicycle infrastructure designs such at specific roundabout types require intensive analytical and simulation approaches for understanding the complex relationships of present traffic flows [4]

Methods

Results

Discussion

Conclusion