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Title: A Statistical Study of Discretionary Lane-changing Decision with Heterogeneous Vehicle and Driver Characteristics
Accession Number: 01764019
Record Type: Component
Abstract: Lane-changing maneuvers on highways may cause capacity drops, create shock waves, and potentially increase collision risks. Properly managing lane-changing behavior to reduce these adverse impacts requires an understanding of their determinants. This paper investigates the determinants of lane-changing in congested traffic using a Next Generation Simulation (NGSIM) dataset. A random parameters binary logit model with heterogeneity in means and variances was estimated to fully account for unobserved heterogeneity in lane-changing behavior across vehicles. Estimation results show that average headway, the original lane of the vehicle, driver aggressiveness, and vehicle size all significantly influence lane-changing probabilities. It was further found that the effect of vehicle size varied significantly across observations, and that the mean of this variation decreased with increasing average headway and the variance increased with increasing driver aggressiveness. These empirical findings provide interesting new evidence on the determinants of lane-changing, which can be used in traffic flow models to better replicate and predict traffic flow.
Supplemental Notes: This paper was sponsored by TRB committee ACP50 Standing Committee on Traffic Flow Theory and Characteristics.
Report/Paper Numbers: TRBAM-21-02028
Language: English
Corporate Authors: Transportation Research BoardAuthors: Li, QianwenLi, XiaopengMannering, Fred LPagination: 15p
Publication Date: 2021
Conference:
Transportation Research Board 100th Annual Meeting
Location:
Washington DC, United States Media Type: Web
Features: Figures; References; Tables
TRT Terms: Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors
Source Data: Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-02028
Files: TRIS, TRB, ATRI
Created Date: Dec 23 2020 11:17AM
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