Journal of Advanced Transportation最新文献

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.

This paper develops a method for estimating carbon-emission specific road networks, considering the presence of electric vehicles (EVs). A mixed equilibrium traffic assignment model is set up to obtain the traffic volume for each link in the network, where oil-fueled vehicles (OFVs) prioritizing travel time minimization, while EVs also consider charging station locations and battery charge state in route selection. A carbon-emission estimation method is then developed, which is calculated by three parameters of traffic volume, average speed, and the road category. A case study is carried out using two networks. It is found that the travel time of the road network has increased by 27%, because EVs tend to choose paths containing charging stations. The route selection of EVs is affected by perceived risk, safe electric quantity, and expected charging electricity. EVs can reduce carbon dioxide emissions but not energy consumption for road network. In addition, it was found that the location of charging stations has a significant impact on traffic flow. After optimizing the location of charging stations, the total travel time, total carbon emissions, and balance of charging station utilization indicators in the transportation network have all relatively decreased. Among them, the total travel time has decreased by 0.2%, the total carbon emissions have decreased by 1.85%, and the balance of charging station utilization has decreased by 0.95%. The research is helpful for determining the locations of charging piles and designing road networks, and it is also helpful for estimating the traffic flow and carbon emissions.

Dynamic traffic assignment (DTA) models are used in many transportation planning and traffic management scenario analyses today. The aim of the DTA model is to reproduce the pattern of vehicular movements. DTA models require inputs in terms of demand and capacity of the road network and are very challenging to calibrate for large urban networks. In this paper, a new network-wide calibration method for link capacities in urban networks is proposed. The method takes link flow observations for a subset of the links in the network to estimate the link capacities. The proposed method relies on partial least squares (PLS) regression and is demonstrated to be feasible and efficient in an urban road network (Stockholm, Sweden) compared to the simultaneous perturbation stochastic approximation (SPSA) method. Performance analysis of the proposed method for different amounts of link flow observations shows that it performs favorably for the cases in which only a small percentage of link flow observations is given.

This study aims to develop a machine learning-based framework for predicting the severity of highway traffic accidents by leveraging high-resolution data from Taiwan’s Electronic Toll Collection (ETC) system. Unlike traditional accident-reporting systems, the ETC infrastructure provides a uniquely comprehensive and precise dataset that captures spatiotemporal traffic patterns and environmental conditions across the national highway network. This rich dataset enabled the integration of data mining and data visualization techniques to uncover nontypical contributing factors to accident severity. Feature engineering was conducted using random forest and LASSO regression, while extreme gradient boosting and the Apriori algorithm were employed to identify key associations between accident severity and contextual variables. Based on human factor and traffic psychology theory, influential factors include poor lighting at night, adverse weather conditions, late-night hours (20:00–06:00), specific geographic regions (e.g., Yilan County), speed limits of 100 km/h, and vehicle types such as taxis and large trucks. The findings not only enhance the understanding of environmental influences on accident outcomes but also offer actionable insights for improving highway safety. Moreover, Taiwan’s ETC system serves as a model for countries seeking to integrate tolling infrastructure with traffic safety analytics.