Journal of Advanced Transportation最新文献

Owing to the increasing demand for improving the utilization rate of shared bikes, identifying their key management areas is necessary for shared-bike companies to effectively allocate resources and formulate efficient management strategies. However, traditional methods often assist management decisions by analyzing the spatial distribution characteristics of each influencing factor of shared-bike usage and fail to quantitatively consider the comprehensive impact of influencing factors on the management area. Therefore, a new method for identifying key management areas for shared bikes using geographical weighting and knowledge graph centrality is proposed. In this study, a multiscale geographically weighted Poisson regression (MGWPR) model was initially used to explore the influencing factors of shared-bike usage and their degrees of influence. The regression model results were linked to geographical statistical units to construct a knowledge graph of factors affecting shared-bike usage in each district, and the weighted degree centrality classification results of nodes in each district were used to assist in identifying their key management areas. The results showed that the proposed method could quantitatively measure the comprehensive impact of different factors in each district on the utilization rate of shared bikes and could effectively identify their key management areas, thereby assisting enterprises in formulating appropriate shared-bike management strategies and improving the efficiency of shared-bike management decision-making.

With the development of a sustainable economy, higher requirements are put forward for logistics enterprises, which not only need to meet the requirements of profit growth but also to meet the need of sustainable development. A vehicle routing problem (VRP) optimization model considering carbon emissions and multifuel-type vehicles (VRP-CEMF) is proposed to solve the problems of air pollution and high transportation cost in the current logistics distribution. An improved genetic algorithm (IGA) is designed to solve the VRP-CEMF. The impact of carbon emissions and multifuel-type vehicles on the logistics distribution path is explored by a real example simulation. The results show that the logistics distribution path optimization considering carbon emissions and multifuel-type vehicles including hybrid electric vehicles and hydrogen-fueled vehicles can significantly reduce carbon emissions on the premise of ensuring the lowest total cost. Furthermore, the impact of carbon emissions, hydrogen fuel price, and customer demand on the logistics distribution path is discussed by sensitivity analysis. The research results of this paper provide an effective reference for enterprises to control carbon emissions in the process of logistics distribution and promote the green transformation of logistics.

Vehicle interactions in weaving sections are relatively frequent and complex, posing significant challenges to traffic congestion management and safety. Trajectory data-driven driving behavior analysis can effectively reveal differences in driving behaviors. Therefore, in accordance with the research requirements, this study selected the CitySim dataset as the foundation after comparison and utilized intelligent algorithms to extract 1349 lane-changing samples from a specific weaving section within the dataset for analyzing the lane-changing behavior characteristics of vehicles in weaving areas. After analyzing the sample data using traffic flow theory and statistical theory, the following results were obtained: the speeds increase upon entering and decrease upon exiting weaving zones, while headway distances consistently grow. Vehicles in the inner lanes exhibit smoother transitions and higher speeds, while lane-changing speeds range from 10 to 55 km/h (median: 29 km/h) and durations vary from 2 to 18 s (median: 8.5 s). Statistical analyses highlight significant behavioral differences based on lane and direction. Vehicles on entrance ramps demonstrate higher speeds, longer durations, and larger headways than those on exit ramps. Furthermore, right-lane changes are associated with lower speeds and shorter durations compared with left-lane changes. Based on these findings, the study proposes targeted traffic management strategies, including ramp flow control and optimized road markings, to enhance safety and efficiency in weaving areas. This research provides actionable insights for traffic management and road design in the expressway weaving areas.

In modern urban logistics and schedule systems, road congestion stands out as a primary contributor to heightened energy consumption in new energy logistics vehicles. Addressing this issue, this study establishes a scheduling method for new energy logistics vehicles comprising several key components: Using the vehicle–road–cloud collaborative technology, the number of vehicles on the road is obtained, and the road congestion coefficient is calculated by combining the speed-flow model, and then the nonlinear energy consumption model for new energy logistics vehicles is studied. Additionally, a VRC-GVRP model is developed considering multiple constraints with the aim of minimizing total energy consumption. To solve this model, an initial solution is constructed using an energy-saving algorithm, while exploring a Cauchy variational strategy and a parallel local search to propose an improved adaptive large neighborhood search (ALNS) algorithm. An illustrative analysis is conducted within an industrial park, based on the real-time traffic information aggregated to the cloud control platform, and the scheduling problem of new energy logistics vehicles is solved. The experimental results indicate that the enhanced ALNS algorithm exhibits rapid convergence and yields high-quality solutions. Compared to the situation without vehicle–road–cloud collaboration technology, despite the increase in the total distance traveled by new energy logistics vehicles, the proposed method effectively reduces total drive time and total energy consumption. As the congestion factor increases, the percentage of reduction in total time and total energy consumption becomes higher and higher, indicating that this method is of great significance for improving the work efficiency of new energy logistics vehicles and achieving energy conservation and emission reduction.

Signal-free crosswalks are a high incidence area for pedestrian–autonomous vehicles (AV) conflicts, but there is no comprehensive and reasonable solution for AVs to safely and efficiently navigate through these conflict scenarios. To address this problem, this study proposes a responsibility-sensitive safety (RSS) model specifically for pedestrian–AV conflicts in signal-free crosswalks. The model is based on the principles and contents of existing RSS models and proposes a safe AV access strategy for hazardous scenarios. The effectiveness of the strategy is verified by an integrated SUMO simulation taking into account the vehicle motion state, driving conservatism, and safety. The results show that the proposed automatic driving access strategy based on the improved RSS model effectively improves the driving stability and safety of the AV through the signal-free crosswalk. This study provides a solution to the pedestrian–AV conflict in signal-free crosswalks on road sections, which can provide a reference for the further promotion and application of the RSS model in the field of autonomous driving.

With urbanization, public transportation resources are becoming increasingly strained. As a key complement to urban transit systems, shared bikes offer distinct advantages in solving the ‘last-mile’ issue for urban commuters. However, one pressing challenge in integrating shared bikes with public transportation is the uneven spatiotemporal distribution. Using Lanzhou City as a case study, this paper provides a detailed analysis of the spatiotemporal characteristics of shared bike and public transport connections. Through the mining of cycling data and analysis of travel demands, a random forest regression (RFR) model is employed to identify factors influencing shared bike usage. The results reveal that variables such as age, population density, and cycling distance significantly impact the efficiency of shared bike connections. Based on these findings, several improvement strategies are proposed, including optimizing the allocation and distribution of shared bikes, addressing the specific needs of various age groups, enhancing cycling safety, and improving bike maintenance. By implementing these strategies, the integration of shared bikes with public transport can be enhanced, increasing shared bike usage and improving the overall efficiency of urban commuting, while promoting green travel and sustainable urban development.

This research develops the BUS-CTM, a novel mathematical simulation model that adapts the cell transmission model (CTM) to analyze spatiotemporal passenger flow dynamics in urban bus networks. The framework discretizes bus routes into interconnected cells bounded by adjacent stops, enabling simultaneous tracking of passenger density evolution and bus traffic interactions through a unified state-space representation. By integrating real-time data streams—including GPS trajectories, automatic passenger counters (APCs) records, and VISSIM-simulated traffic dynamics—the model captures critical nonlinearities in boarding/alighting processes and network-wide congestion propagation at shared stops. Numerical experiments on Gainesville’s RTS network demonstrate the model’s accuracy in predicting passenger distributions, achieving a 4% mean absolute percentage error (MAPE) during peak hours (6:30–9:45 a.m.) and successfully identifying bottlenecks where densities exceed 85% of capacity. The BUS-CTM advances prior CTM adaptations through three key innovations: (1) integration of mixed-traffic capacity reduction effects to account for bus-induced roadway bottlenecks, (2) modular parameterization for transferability across diverse transit systems, and (3) real-time applicability via embedded calibration protocols for door throughput (C_door = 1.2 pax/s) and fare efficiency (γ = 0.8–1.0). These contributions provide transit agencies with a computationally efficient tool for optimizing service frequency, mitigating crowding, and improving network resilience.

Smart tourism is gaining prominence in the tourism industry by improving efficiency and enhancing tourists’ satisfaction. It often involves the use of advanced digital technologies. Although integrating these technologies in transportation can yield significant benefits, but further exploration of tourist’s perceptions on digital transformation is necessary. In this research, a two-step cluster analysis was employed to segment travelers into different groups and to identify their preferences for the use of key digital technologies in transportation hubs. We surveyed 180 passengers to understand their preferences for transportation service digitization. The analysis revealed three segments of traveler preferences: (i) manual—travelers who prefer assistance from customer service officers, (ii) automated—travelers who prefer using technology-based self-service facilities, and (iii) mobile—travelers who prefer more personalized, fully digitized services. By understanding the diverse preferences of different traveler segments, tourism providers can tailor their digitization efforts to better meet customers’ needs.

Autonomous modular buses (AMBs) constitute a novel form of public transportation, enabling real-time adjustments of module configurations and facilitating passenger exchanges in transit. This approach resolves unpleasant transfer experiences and offers a potential solution to traffic congestion. However, while most existing research concentrates on logistical operations, the technical implementation of AMBs remains underexplored. This paper fills this gap by proposing key perception technologies for the docking process of AMBs, which presents a suite of sensors and segments the docking process into four stages. A late fusion-based perception network, featuring event-driven and periodic modules, is introduced to optimize perception by integrating multisource data. Plus, we suggest a “mutual view and coview” strategy to enhance perception accuracy in the unique scenario of docking. Experimental results demonstrate that our method achieves a substantial reduction of errors in x and y axes, as well as the heading angle compared with other state-of-the-art perception methods. Our research lays the groundwork for advancements in the precise docking of AMBs, offering promising tactics for other intelligent vehicle applications.

The spatial and temporal rules governing passenger flow in urban rail transit (URT) stations are complex, and simulation modeling and analysis of passenger flow distribution in stations are very important in regard to scientifically organizing and controlling passenger flow and improving passenger travel efficiency. With a focus on the multilevel three-dimensional spatial structure of URT stations and the composition of multiclass passenger flow lines, the travel process and microbehavior of passengers are analyzed here. The goal-driven behavior of passenger flow groups in the free area and the interaction between them are considered, and a static–dynamic field hybrid model describing the differences in speed between passengers, their walking, and avoidance behavior and a queue field model of queuing behavior are constructed. A selection behavior model for facility nodes such as gates, interlayer facilities, and waiting areas is constructed to represent heterogeneous passenger flow to multiservice channels. A passenger flow simulation method framework for URT stations that takes into account heterogeneous passenger flow, the 3D spatial structure, and multipotential energy field is also established. The effectiveness of the proposed model and method is verified via simulation of Changsha Metro Shumuling Station, and it is found that the proportion of escalators selected as interlayer facilities is significantly higher than that for stairs. After a train leaves the station, the passenger flow density on both sides of the platform reaches more than 1.5 person/m², significantly higher than that in the central area of the platform. The average passing times for passengers at the exit gate and the ascending escalator are 16–18 and 13–14 s, respectively. The average queue length and passing times for passengers are higher than those at the entrance gate and the descending escalator. These results can provide support for decisions on the actual operation of URT stations.