Considering that, in reality, passengers usually hold preferred time windows when waiting for a bus at a station, this study develops a mixed integer programming model for the customized bus routing problem (CBRP) with full spatial–temporal constraints based on one of our previous studies. Specifically, bus routing and passenger assignment are simultaneously optimized with better vehicle capacity utilization and more realistic considerations of partial service, characteristics of customized bus service, and a range of operational constraints. To solve the formulated model, an exact algorithm (i.e. the branch-and-cut algorithm) and two heuristics (i.e. the genetic and tabu search algorithms) are numerically compared through an illustrative example, and subsequently, a case study in Beijing is conducted to assess the proposed approach. A comparison with the practical customized bus system shows that the proposed approach realizes effective vehicle usage on several routes.

Mobility patterns at region level can provide more macroscopic and intuitive knowledge on how people gather in or depart from the region. However, the analysis and prediction of regional mobility patterns have yet to be effectively addressed. In light of this, using smart card data (SCD) and points of interest (POI) data, a multi-step methodology which integrates the inner-restricted fuzzy C-means clustering, nonnegative tensor factorization and artificial neural network are proposed and implemented in this paper. It overcomes the difficulties in region division, pattern extraction, and prediction. The bus SCD and POI data in Beijing city are utilized for proving the usefulness of the methodology. The regional mobility patterns of bus travellers in Beijing city are extracted from the third-order tensors involving 1110 regions, 34 time slots, and 7 days of the week. The analyzed results show that the proposed methodology has a good performance on predicting the regional mobility patterns based on the regional properties. Furthermore, by considering both of the regional boarding and alighting patterns, the predictions of the regional aggregation pattern can also be achieved. These research achievements can not only provide a deep insight on the human mobility patterns at region level, but also support the evidence-based and forward-looking urban planning and intelligent transportation management.

Taxi is an indispensable mode in the urban public transportation. Although many studies have explored the travel patterns of taxi trips, few have combined taxi and subway to reveal their intermodal relationship. To bridge the gap, this study utilized taxi’s trajectory data to investigate its relationship with subway. Considering the multifaceted relationship between taxi and subway in operation, taxi trips are categorized into three types, namely, subway-competing, subway-extending, and subway-complementing taxi trips. The characteristics of each type of taxi trips reflect the specialties and their interactions with subway. The origin/destination distributions of taxi and subway trips are compared and analyzed. Furthermore, the supply and demand of taxi within the buffer zone of each subway station are analyzed to reflect the difficulty of hailing taxis. The negative binomial regression models are used to explore the relationship between taxi trips and subway ridership. The results show that there is a significantly positive correlation between taxi trips and subway ridership.

The emerging customized bus system based on modular autonomous electric vehicles (MAEVs) shows tremendous potential to improve the mobility, accessibility and environmental friendliness of a public transport system. However, the existing studies in this area almost focus on human-driven vehicles which face some striking limitations (e.g., restricted crew scheduling and fixed vehicle capacity) and can weaken the overall benefits. This paper proposes a two-phase optimization procedure to fully unleash the potential of MAEVs by leveraging the strengths of MAEVs, including automatic allocation and charging of modules. In the first phase, a mixed integer programming model is established in the space-time-state framework to jointly optimize the MAEV routing and charging, passenger-to-vehicle assignment and vehicle capacity management for reserved passengers. A Lagrangian relaxation algorithm is developed to solve the model efficiently. In the second phase, three dispatching strategies are designed and optimized by a dynamic dispatching procedure to properly adapt the operation of MAEVs to emerging travel demands. A case study conducted on a major urban area of Beijing, China, demonstrates the high efficiency of the MAEV adoption in terms of resource utilization and environmental friendliness across a range of travel demand distributions, vehicle supply and module capacity scenarios.

Objective: The aim of this study is to examine the effect of traffic density on drivers’ lane change and overtaking maneuvers. The differences between drivers’ left and right lane changing/overtaking maneuvers were also investigated. Method: A driving simulator experiment was conducted and 24 participants took part in this experiment. Based on the driving simulation data, lane change frequency, time duration, average speed, and acceleration were extracted as key variables of lane change maneuvers; overtaking frequency, overtaking duration, initial overtaking distance and headway, instantaneous speed, and acceleration before overtaking were analyzed as the key overtaking variables. One-way repeated measures analysis of variance (ANOVA), the Friedman test, and the Wilcoxon signed rank test were adopted for hypothesis tests with a significance level of .05. Further pairwise comparisons were performed with a Bonferroni correction for multiple comparisons. Results: Some significant differences in lane change and overtake behaviors were observed among different traffic densities: (1) both lane change and overtaking frequencies significantly increase with traffic density; (2) the average lane change acceleration and instantaneous overtaking acceleration significantly increase with traffic density; (3) as the traffic density increases, the initial overtaking distance and headway decrease. As for the effect of the directions of maneuvers, the (1) time duration of lane change and overtaking from the right side was significantly shorter than that from the left side; (2) right initial overtaking distance/headway was smaller than that of the left side; (3) right instantaneous overtaking acceleration was significantly higher than the left instantaneous acceleration. Conclusions: The results showed that as traffic density increases, drivers’ lane changing and overtaking intentions are enhanced. Both initial overtaking distance and headway decrease with traffic density, which might influence road safety. In addition, drivers do not show a preference on the directions of lane change or overtaking according to frequency. However, drivers tend to be more decisive and reckless when overtaking on the right because of a smaller distance/headway before overtaking, higher instantaneous acceleration, and a more restricted field of view compared with overtaking on the left.