TY - JOUR
T1 - Segment-then-refine
T2 - A general calibration framework incorporating intra-driver heterogeneity into Car-Following Models
AU - Wang, Chengming
AU - Jia, Dongyao
AU - Zheng, Zuduo
AU - Ngoduy, Dong
AU - Wang, Wei
AU - Wang, Shangbo
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/7
Y1 - 2025/7
N2 - Physics-based Car-Following Models (CFM) are fundamental to microscopic traffic flow research owing to their simplistic parametric structures, offering valuable insights into longitudinal driving behaviors among human drivers. However, the standard parameter calibration settings are limited in both calibration accuracy and generalization capability. Firstly, significant influential factors, such as intra-driver heterogeneity, are not effectively captured by calibrated CFMs, resulting in substantial calibration errors. Secondly, the estimated CFM parameters often fail to generalize to unseen driving scenarios. These problems cast doubt on the reliability of such CFMs. To address these challenges, this paper introduces a general segment-then-refine calibration framework designed to improve the calibration accuracy and generalization capability of CFMs. The segmentation stage incorporates intra-driver heterogeneity into CFMs by optimizing trajectory segments, each representing distinct driving behavioral pattern and their corresponding CFM parameters, thereby improving calibration accuracy. In the subsequent refinement stage, the CFM parameters of each trajectory segment are refined to enhance generalization capability using a feature-sharing approach based on an auto-regressive neural network (ARNN) model. The effectiveness of this framework is validated using simulation data to assess segmentation accuracy and real-world traffic data to evaluate trajectory simulation accuracy. Experimental results demonstrate that this framework outperforms standard calibration settings in both calibration accuracy and generalization capability, and is more effective in capturing distinct driving styles.
AB - Physics-based Car-Following Models (CFM) are fundamental to microscopic traffic flow research owing to their simplistic parametric structures, offering valuable insights into longitudinal driving behaviors among human drivers. However, the standard parameter calibration settings are limited in both calibration accuracy and generalization capability. Firstly, significant influential factors, such as intra-driver heterogeneity, are not effectively captured by calibrated CFMs, resulting in substantial calibration errors. Secondly, the estimated CFM parameters often fail to generalize to unseen driving scenarios. These problems cast doubt on the reliability of such CFMs. To address these challenges, this paper introduces a general segment-then-refine calibration framework designed to improve the calibration accuracy and generalization capability of CFMs. The segmentation stage incorporates intra-driver heterogeneity into CFMs by optimizing trajectory segments, each representing distinct driving behavioral pattern and their corresponding CFM parameters, thereby improving calibration accuracy. In the subsequent refinement stage, the CFM parameters of each trajectory segment are refined to enhance generalization capability using a feature-sharing approach based on an auto-regressive neural network (ARNN) model. The effectiveness of this framework is validated using simulation data to assess segmentation accuracy and real-world traffic data to evaluate trajectory simulation accuracy. Experimental results demonstrate that this framework outperforms standard calibration settings in both calibration accuracy and generalization capability, and is more effective in capturing distinct driving styles.
KW - Car-following models
KW - Deep learning
KW - Driving style identification
KW - Optimization algorithms
KW - Parameter calibration
UR - http://www.scopus.com/inward/record.url?scp=105004369552&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2025.105144
DO - 10.1016/j.trc.2025.105144
M3 - Article
AN - SCOPUS:105004369552
SN - 0968-090X
VL - 176
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
M1 - 105144
ER -