Modeling the impact of lane-changing’s anticipation on car-following behavior

Chen, Kequan; Knoop, Victor L.; Liu, Pan; Li, Zhibin; Wang, Yu-Xuan

doi:10.1016/j.trc.2023.104110

Cited by 15 publications

(1 citation statement)

References 40 publications

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“…Existing studies often decompose continuous vehicle driving actions into two types of sub-behaviors, following and lane changing, and construct relatively independent modeling systems for them [4][5][6][7][8][9][10][11]. The behavioral assumption of this modeling approach is that since the time a driver spends performing a lane change during continuous driving is shorter than the time they spend in a following state, it is assumed that the following behavior is driven by the decision to follow continuously within a certain spatial and temporal range, whereas the lane-changing behavior is derived from the driver's behavioral decision and the time spent on this decision is very short or even negligible.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

Zhang,

Qu,

Zhang

et al. 2024

Sustainability

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To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver’s multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver’s capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers’ decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems.

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