%0 Report %1 aa04d2796309433f95a4258dd7ddfecf %A Kulkarni, Mihir Mandar %A Chaudhari, Ankit Anil %A Srinivasan, Karthik K. %A Chilukuri, Bhargava Rama %A Treiber, Martin %A Okhrin, Ostap %D 2024 %K topic_engineering FIS_scads physics.soc-ph %R 10.48550/arXiv.2405.10665 %T Leader-Follower Identification with Vehicle-Following Calibration for Non-Lane-Based Traffic %X Most car-following models were originally developed for lane-based traffic. Over the past two decades, efforts have been made to calibrate car-following models for non-lane-based traffic. However, traffic conditions with varying vehicle dimensions, intermittent following, and multiple leaders often occur and make subjective Leader-Follower (LF) pair identification challenging. In this study, we analyze Vehicle Following (VF) behavior in traffic with a lack of lane discipline using high-resolution microscopic trajectory data collected in Chennai, India. The paper's main contributions are threefold. Firstly, three criteria are used to identify LF pairs from the driver's perspective, taking into account the intermittent following, lack of lane discipline due to consideration of lateral separation, and the presence of in-between vehicles. Second, the psycho-physical concept of the regime in the Wiedemann-99 model is leveraged to determine the traffic-dependent "influence zone" for LF identification. Third, a joint and consistent framework is proposed for identifying LF pairs and estimating VF parameters. The proposed methodology outperforms other heuristic-based LF identification methods from the literature in terms of quantitative and qualitative performance measures. The proposed approach can enable robust and more realistic LF identification and VF parameter calibration with practical applications such as LOS analysis, capacity, and travel time estimation.

@techreport{aa04d2796309433f95a4258dd7ddfecf, abstract = {Most car-following models were originally developed for lane-based traffic. Over the past two decades, efforts have been made to calibrate car-following models for non-lane-based traffic. However, traffic conditions with varying vehicle dimensions, intermittent following, and multiple leaders often occur and make subjective Leader-Follower (LF) pair identification challenging. In this study, we analyze Vehicle Following (VF) behavior in traffic with a lack of lane discipline using high-resolution microscopic trajectory data collected in Chennai, India. The paper's main contributions are threefold. Firstly, three criteria are used to identify LF pairs from the driver's perspective, taking into account the intermittent following, lack of lane discipline due to consideration of lateral separation, and the presence of in-between vehicles. Second, the psycho-physical concept of the regime in the Wiedemann-99 model is leveraged to determine the traffic-dependent {"}influence zone{"} for LF identification. Third, a joint and consistent framework is proposed for identifying LF pairs and estimating VF parameters. The proposed methodology outperforms other heuristic-based LF identification methods from the literature in terms of quantitative and qualitative performance measures. The proposed approach can enable robust and more realistic LF identification and VF parameter calibration with practical applications such as LOS analysis, capacity, and travel time estimation.}, added-at = {2024-11-28T16:27:18.000+0100}, author = {Kulkarni, {Mihir Mandar} and Chaudhari, {Ankit Anil} and Srinivasan, {Karthik K.} and Chilukuri, {Bhargava Rama} and Treiber, Martin and Okhrin, Ostap}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/28196a4d0ce364c19c8ffbbc34893916d/scadsfct}, day = 17, doi = {10.48550/arXiv.2405.10665}, interhash = {63fc04c8514635f5365e5e477321a4d3}, intrahash = {8196a4d0ce364c19c8ffbbc34893916d}, keywords = {topic_engineering FIS_scads physics.soc-ph}, language = {English}, month = may, timestamp = {2024-11-28T17:41:01.000+0100}, title = {Leader-Follower Identification with Vehicle-Following Calibration for Non-Lane-Based Traffic}, type = {WorkingPaper}, year = 2024 }