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Pre-peak fare discount policies have become increasingly common in urban transit services and show potential to spread morning peak-hour demand. This study analyzes the departure time choice behavior of interregional bus passengers during morning hours. A mixed logit model is applied to estimate the probability of shifting departure time as a function of fare discount rate, in-vehicle congestion, and other influential factors, accommodating heterogeneity of passenger preferences. The analysis data are obtained through a stated preference survey, which is conducted to passengers who regularly use the interregional bus in Seoul metropolitan area. Choice models are segmented by departure time periods (peak, pre-peak, and post-peak) and occupation types (fixed time worker, flexible time worker, and student). Marginal utilities of the unobserved preferences are estimated to suggest policy implications. From the experimental analysis, target passengers for shifting departure time during peak hour are suggested as flexible time workers and students. [ABSTRACT FROM AUTHOR]

This paper presents a systematic investigation of the train choice preference heterogeneity of High-Speed Railway (HSR) passengers under market segmentation to understand their train choice comprehensively. A stated preference survey was conducted for the Nanning-Guangzhou Railway and Nanning-Beihai Railway. Latent Class Analysis (LCA) was employed to identify homogeneous subgroups and segment the passenger market of each line into three segments: private travelers with a long total duration (PTLTD), business travelers (BT), and private travelers with a short total duration (PTSTD). Mixed Logit (ML) models were constructed for each subgroup sample to assess passengers' preferences in train choice. The results show that each class exhibited unique characteristics and preferences, and train fare and running time, departure date and time, and train frequency were statistically significant factors affecting train choice. This study can furnish theoretical and decisional support for HSR operators to design train operating schemes and flexible fare systems. [ABSTRACT FROM AUTHOR]

This paper presents the findings of an activity-based tour-level model that jointly investigates individuals' decisions of activity-tour participation, time allocation, and mode choice. A multiple discrete continuous extreme value (MDCEV) model is developed that explores the participation and time allocation decisions for mandatory, maintenance, and discretionary activity-tour types. Mixed logit (MXL) models are developed to evaluate the mode choice decisions of individuals for different activity-based tours. Logsum values, which represent the modal accessibility, are introduced within the MDCEV modeling framework as feedback mechanisms from mixed logit models that exclusively examine the influence of individuals' mode choice on their activity-tour engagement decisions as well as ensure the integrity of the decision modeling system. Findings of this study would assist the policymakers to develop effective strategies for better transportation investment decisions. [ABSTRACT FROM AUTHOR]

One common limitation faced in mode choice modeling is data imbalance. Mode choice models, such as logit models, may output biased estimations for alternatives with smaller shares and consequently have high prediction errors. Since accurate prediction of the less commonly used modes is important in some applications, such as predicting transit mode share in many auto-oriented American cities, it is essential to improve the prediction capability of logit models for those modes. Hence, this study applies an imbalanced learning technique and evaluates the prediction capability and interpretability of logit models under both balanced and imbalanced datasets using a case study for the City of Nashville, Tennessee. The results show that the proposed method improves the accuracy of the less commonly used modes and the mean absolute percentage error by 18% and 2%, respectively, while keeping the models interpretable. Finally, we provide some high-level guidelines for mode choice modeling with imbalanced data. [ABSTRACT FROM AUTHOR]

Lane changes are particularly frequent on urban streets, which not only impact traffic operations, but also cause negative effects on safety. This study uses vehicle trajectory data collected from Southwest Road in Dalian, China, to investigate the influential factors of discretionary lane changing in an urban road environment. Both the standard logit model and mixed logit model were fitted to the data, which evidently identified several key factors. The mixed logit model outperformed the standard logit model in terms of model fit. The results suggest that driver heterogeneity is present on the speed differentials of the subject vehicle and the leading vehicles, and the distance gap in the target lane. A sensitivity analysis was further conducted to quantify the degree of influence of the statistically significant variables. The findings support the notion and purpose of discretionary lane changing, i.e. seeking a speed advantage and/or a more satisfactory driving environment. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to determine the key factors affecting passengers' domestic airline choice in Turkey. This study utilized the data of a self-administered survey conducted to 2473 passengers in 11 different airports. The data were analyzed using multinomial and mixed logit (ML) models. The analysis results reveal that ML model is useful to explain preference heterogeneity of passengers' airline choice behavior. The estimation results also indicate that several demographic, socio-economic, and travel destination characteristics were found as the significant drivers of passengers' airline choice. The results of this study may be a valuable tool for policy-makers in the airline industry. [ABSTRACT FROM PUBLISHER]