Understanding choice behavior regarding travel mode is essential in forecasting travel demand. Machine learning (ML) approaches have been proposed to model mode choice behavior, and their usefulness for predicting performance has been reported. However, due to the black-box nature of ML, it is difficult to determine a suitable explanation for the relationship between the input and output variables. This paper proposes an interpretable ML approach to improve the interpretability (i.e., the degree of understanding the cause of decisions) of ML concerning travel mode choice modeling. This approach applied to national household travel survey data in Seoul. First, extreme gradient boosting (XGB) was applied to travel mode choice modeling, and the XGB outperformed the other ML models. Variable importance, variable interaction, and accumulated local effects (ALE) were measured to interpret the prediction of the best-performing XGB. The results of variable importance and interaction indicated that the correlated trip- and tour-related variables significantly influence predicting travel mode choice by the main and cross effects between them. Age and number of trips on tour were also shown to be an important variable in choosing travel mode. ALE measured the main effect of variables that have a nonlinear relation to choice probability, which cannot be observed in the conventional multinomial logit model. This information can provide interesting behavioral insights on urban mobility.

No abstract

No abstract

Lane changes are critical contributors to road traffic safety on highways. Among the safety indexes aimed at evaluating the risk associated with lane changes, the lane-change risk index (LCRI) is used to determine the collision probability of a platoon of vehicles during lane-change maneuvers. This study estimated the impact of driver behavior and vehicle type on the LCRI using individual vehicle trajectory data from the Next Generation Simulation (NGSIM) program. We define the subject vehicle (i.e., a vehicle changing lane) and its surrounding vehicles (i.e., front, rear, lead, and lag vehicles) as a platoon. Each vehicle type (i.e., truck, bus, car, and motorcycle) and driver behavior (i.e., aggressive, ordinary, and timid) were categorized for regression analysis. Driver behavior was classified through time–space deviations between each vehicle’s trajectories and expected trajectories using Newell’s car-following model. In addition, to take into account the heterogeneity among the lanes, this study used a linear mixed model, which reflected fixed and random effects. Two unique findings were that (i) we were able to quantify and analyze the complex interaction between vehicle type and driver behavior within the platoon during lane changes, and (ii) using the random parameter model, the influence of vehicle type and driver behavior in the platoon was heterogeneous, depending on the lane. The findings of this study are expected to provide detailed lane-change strategies for autonomous vehicles as well as to evaluate the causative factors of lane-change risk.

No abstract

No abstract