Exploring the stimulative effect on following drivers in a consecutive lane-change using microscopic vehicle trajectory data

TL;DR

This paper analyzes how consecutive lane-changing behaviors influence following drivers, revealing a stimulative effect that varies among drivers and developing models to predict improper lane changes using microscopic vehicle trajectory data.

Contribution

It introduces a comprehensive analysis of consecutive lane-changing effects, incorporating driver heterogeneity and developing utility prediction models for detecting improper lane changes.

Findings

Consecutive lane-changing negatively impacts following vehicles.

A stimulative effect exists and varies due to driver psychological differences.

A utility prediction model effectively detects improper lane-changing decisions.

Abstract

Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of multiple vehicles and the stimulative effect on following drivers in a consecutive lane-changing scenario. The microscopic trajectory data from the dataset are used for driving behavior analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario, and not only distance- and speed-related factors but also driving behaviors are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver psychological heterogeneity in the consecutive lane-changing situation.Furthermore, a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision. Results indicate that (1) the consecutive lane-changing behaviors have a significant negative effect on the following lane-changing vehicles after lane-change; (2) the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers; and (3) the utility prediction model can be used to detect an improper lane-changing decision.