Journal of Advanced Transportation最新文献

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.

The accurate fitting of railway station track profiles is crucial for improving the quality of station-related engineering projects. However, in practice, there are still cases where straight lines are used to approximate vertical curve segments. To avoid the ‘straight-line approximation’ deviation in the gradient value of the profile’s straight segments, this paper establishes a mathematical programming model with the objective of minimising the total amount of track raising and lowering. To satisfy the model constraints, an optimal vertical curve fitting algorithm, utilising a bisector vector approach to unambiguously resolve the conjugacy issue in analytical circular curve calculations, and a genetic algorithm based on neighbourhood search, specifically designed to fine-tune initial least squares gradient estimates and avoid systematic deviations, are proposed. Finally, the effectiveness of the proposed model and algorithm is validated through a case study. With a total grade value adjustment of only 0.45‰ based on the least squares method, the proposed fitting algorithm achieves a 38.7% reduction in track raising and lowering compared to manual fitting. This demonstrates the effectiveness of the proposed method in achieving the integrated optimisation of straight-line segments and vertical curve segments within the longitudinal profile. The implementation of this algorithm through programming can significantly enhance both the efficiency and quality of railway station track profile fitting.

To identify the key causes of major road traffic accidents (resulting in three or more deaths), this study constructed an accident causation network based on association rules and complex networks using data from 173 major traffic accidents over the past decade. Initially, 62 potential risk factors were extracted from aspects such as human, vehicle, road environment, and management. Association rule algorithms were then employed to explore the coupling relationships between these risk factors, generating strong association rules. Finally, a complex network model was built based on these association rules to identify critical risk factors. Results indicate that (1) 80% of major traffic accidents are linked to poor driving behavior. Complex network analysis identified speeding, overloading, lane crossing, and failure to maintain safe following distances as primary human factors, which are closely related to vehicle, road, and environmental conditions, contributing collectively to accidents. (2) Association rule results revealed that head-on collisions are primarily related to lane crossing and occur on national and secondary grade highway; falling accidents are common on roads with inadequate infrastructure; rear-end collision often happen on expressway and national highway, especially at night (18:00–07:00) with freight vehicles; vehicle-related major accidents are usually associated with noncompliant vehicle performance, vehicle failures, overloading, and insufficient regulation. Major accidents involving inadequate road signs, markings, and safety barriers have a significant association with nighttime periods. The conclusions of this study can provide valuable insights for the prevention of major road traffic accidents.

To promote the sustainable development of urban bus transit, this paper introduces the concept of spatial sustainability for bus networks. This concept aims to achieve a balance in the spatial distribution of bus routes, optimizing both social and economic benefits. The paper presented a novel approach that utilizes multisource big data to assess the spatial sustainability of bus transit, with a specific focus on the existing transit-supportive area (TSA) method. In this study, we employed various data sources, including mobile phone signaling data, residential travel survey data, map API data, and bus credit card data. These datasets were used to identify regions where peak-hour ridership exceeded a defined threshold. Using Nanjing as a case study, we defined the bus sustainable development zone (BSDZ) by evaluating areas where peak-hour bus ridership exceeded 10 trips per grid. The study then compared the BSDZ with both the bus support zone and the bus stop service zone. In addition, we conducted a comparative analysis between the BSDZ and TSA, as well as the existing bus service areas. The findings revealed a spatial imbalance in the current bus network and highlighted areas where optimization was needed. The identified BSDZs offered valuable insights that can serve as a reference for future bus route optimization in Nanjing, contributing to a more balanced and efficient transit system.

The adoption of connected and automated vehicles (CAVs) and automated vehicles (AVs) is expected to improve overall travel efficiency in traffic networks. However, this improvement may vary across multimodal road traffic, influencing the equity of travel experiences among different income groups. This study evaluates the equity effects of AV deployment during its gradual adoption in public transit and automobiles. Five scenarios with varying penetration rates are designed to represent the transition from HVs to CAVs in public transit and automobiles. Different model parameters were calibrated to represent HVs, AVs, and CAVs, and four measurements of effectiveness were developed to compare travel experiences between high-income individuals, who primarily rely on private automobiles, and low-income individuals, who depend on public transit. A detailed case study was conducted using real-world road network and traffic flow data from Madison, Wisconsin. Simulation results reveal that while AV adoption improves overall traffic efficiency, including nonupgraded vehicles, it also exacerbates disparities in travel performance between high- and low-income groups, even with efforts to expand AV-enabled public transit.

This study investigates transportation choices with a specific focus on ride-sharing practices. The main aim of the study was to understand the current modes of transport, the primary reasons for choosing them, ride-sharing experiences, and future ride-sharing intentions within the context of Islamabad, Pakistan. The final analyses were based on 294 respondents, including 88 respondents with prior ride-sharing experience. The sample was skewed toward male participants (80.6%), reflecting national mobility patterns. Logistic regression was employed to investigate the relationship between different factors toward individuals’ intentions to use ride-sharing as a future commuting option. The results indicate that gender, previous ride-sharing experience, preferences for companionship during ride-sharing, and the primary mode of transportation for shopping emerged as significant factors influencing future ride-sharing intentions. Males are nearly three times more likely to adopt ride-sharing (Exp (β) = 2.9) than females (β = 1.07, p < 0.01). Similarly, individuals with previous ride-sharing experience (β = 0.94, p < 0.01) have a 2.6 times higher likelihood of choosing ride-sharing in the future. Moreover, respondents preferring larger groups while ride-sharing exhibit higher adoption intentions (β = 0.26, p = 0.02, Exp (β) = 1.3). In contrast, individuals primarily using motorcycles (β = −1.53, p = 0.02, Exp (β) = 0.2) or personal cars (β = −1.72, p = 0.01, Exp (β) = 0.2) for shopping are less inclined to shift toward ride-sharing. The model achieves a Nagelkerke pseudo R² of 0.23, explaining 23% of the variance in future ride-sharing intentions. This research yields valuable insights that could guide initiatives aimed at fostering ride-sharing adoption and encouraging individuals to utilize this mode of transportation.

Urban rail transit trains consume a significant amount of energy; therefore, reducing the operational energy consumption is of great importance for train energy-saving efforts. To address this issue, and to avoid the limitations of solutions constrained by operational condition combination strategies and the combination explosion of schemes with interval-by-interval position searches under unrestricted operational strategies, an operation control scheme solution method based on curve splicing is proposed. This method involves selecting curve segments and continuously splicing and recombining them to efficiently generate new running curves without being restricted by the basic energy-saving curve framework. Based on this optimization concept, a splicing strategy is developed that includes four parameter variables: the splice point location, splice point speed, splice relationship type, and control force magnitude. On this basis, a curve splicing optimization model is established, with the objective function being the minimization of the train’s operational energy consumption while meeting interval running time requirements. A two-layer iterative optimization algorithm is designed based on the simulated annealing framework. Utilizing the data of Guangzhou Metro Line 2, the optimized scheme achieves energy savings of 11.713% in the Baiyun Cultural Square–Baiyun Park interval and 9.115% in the entire downward intervals.

The Yangtze River Delta is one of the most economically dynamic urban agglomerations in China, with the Hangzhou Bay Bridge and Jiashao Bridge serving as crucial sea-crossing transportation corridors. This study proposes a novel travel time prediction framework that integrates a genetic algorithm–based section travel time calculation with a long short-term memory (GA-LSTM) neural network. The genetic algorithm enhances the segmentation of travel time across different road sections, ensuring refined input for the GA-LSTM model, which effectively captures spatiotemporal dependencies in travel patterns. Unlike conventional methods that rely on aggregated traffic data or simpler regression models, our approach leverages real-world toll data to provide highly accurate travel time predictions for different corridors and time periods. The case study on the Hangzhou Bay Bridge and Jiashao Bridge demonstrates that the proposed model significantly improves prediction accuracy compared to traditional methods. These findings offer valuable insights for optimizing traffic management, informing infrastructure planning, and enhancing the efficiency of major transportation corridors in urban agglomerations.