01764042

Component

https://doi.org/10.1177/03611981211037543

A new methodology is proposed for calibrating Wiedemann-99 vehicle following parameters for mixed traffic based on trajectory data. The existing acceleration equations of the Wiedemann model are modified to represent more realistic driving behavior. Exploratory analysis of simulation data revealed that different Wiedemann-99 model parameters can lead to similar macroscopic behavior, highlighting the importance of calibration at the microscopic level. Therefore, the proposed methodology is based on optimizing performance measures at the microscopic level (acceleration, speed, and trajectory profiles) to estimate suitable calibration parameters. The performance of the optimized parameters is validated and compared against other heuristic methods of calibration reported in the literature. It was found that parameters optimized using the proposed methodology performs better than those obtained using heuristic methods. Further, the calibration parameters and goodness of fit for the observed data are found to be sensitive to the numerical integration method used to compute the velocity and position of vehicles. The results reveal that the optimized parameter values and consequently, the thresholds that delineate closing, following, emergency braking, and opening regimes vary between two-wheelers and cars. The window (in the relative speed vs. gap plot) for the unconscious following is larger for cars, while the free flow regime is larger for two-wheelers. Under the same stimulus of relative speed and spacing, two-wheelers and cars may be in different regimes and display different acceleration responses. Thus, accurate calibration of parameters for each type of vehicle is essential for developing micro-simulation models for mixed traffic.

This paper was sponsored by TRB committee ACP80 Standing Committee on Traffic Simulation Committee.

TRBAM-21-04411

English

24p

2021

Transportation Research Board 100th Annual Meeting

Web

Figures; References; Tables

Heuristic methods; Optimization; Traffic data; Vehicle mix; Vehicle trajectories

Wiedemann Car Following Model

Data and Information Technology; Highways; Vehicles and Equipment

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-04411

TRIS, TRB, ATRI

Dec 23 2020 11:18AM