CLACD: A complete LAne-Changing decision modeling framework for the connected and traditional environments

Abstract

This study develops a Complete LAne-Changing Decision (CLACD) modeling framework that explains both the mandatory and discretionary lane-changing behaviors, incorporates both the successful and failed lane-changing attempts, and describes lane-changing behaviors in both the traditional and connected environments. The connected environment provides real-time and valuable information about surrounding vehicles and downstream situations (e.g., congestion ahead) that drivers cannot foresee. Such information can be particularly useful for complex driving maneuvers like discretionary lane-changing (DLC), which requires information about surrounding traffic speeds and subsequent gaps available on the adjacent lanes. DLC modeling efforts in the connected environment appear to be nascent, primarily because of: (a) the novelty of the connected environment (and the consequent scarcity of the relevant data), and (b) the lack of a behaviorally sound DLC model even for the traditional environment (that is, without driver-aid messages). Moreover, the existing models omit failed DLC attempts by motorists that are likely to create traffic disruptions and have a significant impact on traffic safety. To overcome these challenges, this study develops a DLC model (i.e., CLACD model) for the traditional environment, which is extended for the connected environment. An integrated approach is employed to model DLC behavior by combining the target lane selection using the utility theory approach and the gap acceptance behavior using a game theory approach. To collect high-quality vehicle trajectory data in the connected environment, the CARRS-Q advanced driving simulator was used. The CLACD models (for both the traditional and connected environments) are calibrated using NGSIM and the driving simulator data, and the CLACD models’ performances were rigorously assessed using various performance indicators. Results reveal that the CLACD models can effectively capture the observed DLC decisions with high accuracy. The performance of CLACD models is compared with the performance of two alternative models described in the literature, revealing that the CLACD models outperform the alternatives. A sensitivity analysis suggests a consistent trend of the CLACD models, which agrees with past research. Furthermore, the CLACD model shows a realistic prediction of traffic flow patterns compared with the utility theory model. By incorporating failed DLC attempts into the CLACD models, the performance and realism of CLACD models have been significantly improved.