%0 Journal Article %1 Anil_Chaudhari2022-ry %A Anil Chaudhari, Ankit %A Srinivasan, Karthik K %A Rama Chilukuri, Bhargava %A Treiber, Martin %A Okhrin, Ostap %D 2022 %I SAGE Publications %J Transp. Res. Rec. %K imported %N 1 %P 718--735 %T Calibrating model parameters to trajectory data of mixed vehicular traffic %V 2676 %X We propose a new methodology for calibrating Wiedemann-99 vehicle-following parameters for mixed traffic (different conventional vehicle classes) 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 could 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. Further, the goodness of fit for the observed data is sensitive to the numerical integration method used to compute vehicles' velocity and position. We found that the calibrated parameters using the proposed methodology perform better than other approaches for calibrating mixed traffic. The results reveal that the calibrated 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 versus gap plot) for the unconscious following is larger for cars while the free-flow regime is more extensive for two-wheelers. Moreover, under the same relative speed and gap stimulus, two-wheelers and cars may be in different regimes and display different acceleration responses. Thus, accurate calibration of each vehicle's parameters is essential for developing micro-simulation models for mixed traffic. The calibration analysis results of strict and overlapping staggered car following signify an impact of staggered car following compared with strict car following which demands separate calibration for strict and staggered following.

@article{Anil_Chaudhari2022-ry, abstract = {We propose a new methodology for calibrating Wiedemann-99 vehicle-following parameters for mixed traffic (different conventional vehicle classes) 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 could 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. Further, the goodness of fit for the observed data is sensitive to the numerical integration method used to compute vehicles' velocity and position. We found that the calibrated parameters using the proposed methodology perform better than other approaches for calibrating mixed traffic. The results reveal that the calibrated 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 versus gap plot) for the unconscious following is larger for cars while the free-flow regime is more extensive for two-wheelers. Moreover, under the same relative speed and gap stimulus, two-wheelers and cars may be in different regimes and display different acceleration responses. Thus, accurate calibration of each vehicle's parameters is essential for developing micro-simulation models for mixed traffic. The calibration analysis results of strict and overlapping staggered car following signify an impact of staggered car following compared with strict car following which demands separate calibration for strict and staggered following.}, added-at = {2024-10-02T10:38:17.000+0200}, author = {Anil Chaudhari, Ankit and Srinivasan, Karthik K and Rama Chilukuri, Bhargava and Treiber, Martin and Okhrin, Ostap}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/23cce73ea40bf77fd690f183e8f9720cd/scadsfct}, interhash = {1256fda46a7fdc745136f2a7009253c0}, intrahash = {3cce73ea40bf77fd690f183e8f9720cd}, journal = {Transp. Res. Rec.}, keywords = {imported}, language = {en}, month = jan, number = 1, pages = {718--735}, publisher = {SAGE Publications}, timestamp = {2024-10-02T10:38:17.000+0200}, title = {Calibrating model parameters to trajectory data of mixed vehicular traffic}, volume = 2676, year = 2022 }