Journal of Advanced Transportation最新文献

In recent years, severe traffic congestion in urban areas has become a growing concern for many cities in China. While beltway expressways have been built to alleviate urban congestion by diverting traffic away from city centers, existing preferential access schemes have led to overuse of certain beltway links and a decline in service levels. This research is motivated by the need to develop an improved scheme that balances urban traffic relief and beltway service level maintenance. To benefit both the government and the beltway expressway company, we propose a tradable credit scheme (TCS) to shift some of the traffic burden from urban roads to the beltway expressway while maintaining its current service level. In order to determine an appropriate TCS, a biobjective bilevel programming model is introduced. The upper-level model seeks to maximize the revenue of the beltway expressway company while minimizing the system travel time as specified by the traffic management department. The lower-level model describes user travel behavior under given TCS and capacity constraints of beltway expressway links, represented as the generalized capacitated traffic assignment problem. Additionally, an NSGA-II framework is designed to discover Pareto-optimal TCSs. Within this framework, a gradient projection method is utilized within the augmented Lagrangian multiplier algorithm to solve the lower-level problem and evaluate the fitness of the TCS. Numerical experiments demonstrate that implementing well-designed tradable credit schemes can effectively alleviate urban traffic congestion, reducing system travel time by up to 1.6%, while simultaneously increasing the beltway company’s revenue by up to 300% compared to the base case without requiring government subsidies. The sensitivity analysis results indicate the insufficiency of the existing preferential access or toll-free schemes on the beltway, highlighting the greater effectiveness of the integrated strategy proposed in this study.

Even though the Demand-Responsive Transport (DRT) service plays an important role to provide more convenience to the disabled people, they still have a challenge to be solved in terms of waiting time. Especially, the biggest problem is that waiting times for using the DRT service are variant across space and time because users cannot predict when they can get the service and/or how long they have to wait for service. This study developed a model for predicting the waiting time of Demand-Responsive Transport for Disabled (DRTD) with irregular spatiotemporal characteristics in real time. The primary purpose of the model developed was to monitor the level of service (LOS) to improve the satisfaction of users who use mobility services for the disabled and efficiently manage service providers. The model was estimated using an Adaptive Neuro-Fuzzy Inference System (ANFIS), which is known to have an excellent predictive performance by combining the advantages of both artificial neural networks and fuzzy inference systems. Four variables, including the number of calls (or requests), the number of vacant vehicles, Medical Infrastructure Concentration Index (MICI), and Disabled Population Concentration Index (DPCI), were used as input variables for the ANFIS-based model. Despite using cross-sectional data, the accuracy of the predicted model was found to be excellent (90%) and showed good and even prediction performance without bias by LOS categories The proposed method is expected to become a monitoring tool to manage mobility services and improve user convenience by notifying users how long they have to wait to use mobility services in real time.

Drivers’ fault emergency response behavior can easily lead to crashes, resulting in significant economic and property losses. Exploring the causes of improper emergency response behavior is crucial for regulating driver behavior and preventing crash. Therefore, based on crashes data between commercial motor vehicles (CMVs) and noncommercial motor vehicles (NCMVs) that occurred in China from 2014 to 2018, this study established a binary logistic regression model. It systematically analyzed the key factors influencing drivers’ fault emergency response behaviors in terms of individuals, vehicles, road conditions, environment, and corporate management. Additionally, it compared the differences in the influencing factors of fault emergency response behaviors between drivers of CMV and NCMV. The results indicate that the model fits well. The presence of faulty emergency response behavior in drivers is significantly correlated with five factors: age, gender, fatigue driving, speeding, and weather conditions. Moreover, these factors have different impacts on CMV drivers and NCMV drivers. Fatigue driving and speeding have a more significant impact on CMV drivers, while other factors are more pronounced for NCMV drivers. This study can provide valuable insights for the development of measures aimed at reducing the severity of CMV-NCMV crashes.

The study of pedestrian catchment areas for metro stations serves as the foundation for addressing issues such as-passenger flow forecasting, land use planning around stations, and transportation link facility planning. This study aims to investigate how various factors related to pedestrians, the environment, and the characteristics of metro stations affect the areas where pedestrians are attracted to. The goal is to gain insights into design strategies and policy interventions that can enhance the appeal of metro stations. The present study considers the metro stations in the Chengdu area as the primary research objects. A framework for examining the connection between the built environment and the pedestrian catchment area of a subway station through the use of a hybrid gradient decision tree approach has been used. The study examines the pedestrian catchment areas of these stations and confirms the presence of a nonlinear relationship between pedestrian catchment areas and variables at these stations. There exists a negative correlation, within specific thresholds, between the number of companies, bus stops, and road network density with the catchment area of passengers. Concurrently, there is a positive correlation between the distances to the city center and the catchment area. Furthermore, this framework, which accounts for spatial heterogeneity, showed significant goodness-of-fit and predictive capability, aspects that were overlooked in earlier research. As a result, these research findings can serve as a key foundation for advancing the theoretical framework and statistical proof to facilitate land planning and development near metro stations, predicting passenger flow, and designing essential connecting amenities in the vicinity of such facilities.

Connected and autonomous vehicles (CAVs) are considered a hot area of research in the field of intelligent transportation systems. However, over the past few years, cybersecurity threats have posed significant challenges to such systems, given the ever-evolving automotive industry. Thus, there is a growing need to design resilient control strategies to address the issue of cyberattacks. This article proposes the design of a distributed multiagent expert control scheme for cyberattack-resilient control of CAVs. The study implemented an event-triggered consensus-based attack detection scheme capable of distinguishing between replay (RA), denial-of-service (DoS), and false data injection (FDI) attacks. The attacks in this study occur randomly, are bounded and time-varying, and can overlap with each other. It was demonstrated that by considering an estimator error bound ε for the attacked signal reconstruction, the SMC controller in feedback with a vehicle remains stable in the sense of Lyapunov. Conditions were then provided that guarantee global asymptotic stability for a minimum dwell-time constraint $$, and the platoon was shown to be string stable for the minimum distance between vehicles, denoted as $$. Finally, the performance of the control strategy was evaluated using multiple performance indices, considering platoons of varying sizes.

Rainstorm and the induced flood disaster often cause serious damage to transportation system, resulting in the loss of life and property. To improve the resilience of transportation system, this study proposes a framework for assessing the impacts of rainstorm and flood disaster on road network based on GPS data. The change ratio of traffic flow and congestion index are used to evaluate the disrupted and detour road sections caused by rainstorm. A method based on the change of traffic flow compared to the normal circumstances is proposed to identify the flood-damaged road sections. A case study in Beijing verifies the feasibility and practicality of the proposed framework, which can effectively assess the impacts of rainstorm from multiple spatial and temporal dimensions, identify the flood-damaged road sections, and supplement existing research. Research findings can provide scientific basis for the emergency management departments to accurately identify the roads affected by rainstorm and improve the resilience of transportation system.

Although urban freight transportation is crucial to address societal demands, it also has a major negative impact on the environment, the economy, and society. Then, the growing interest in promoting more sustainable and liveable cities is pushing to point out more in depth the role of urban goods movements within the planning process, as well as city administrators to implement new sustainable city logistics actions/policies/measures. Therefore, after a brief overview, the study presents more advanced techniques (models and methodologies) to help in the assessment of planning scenario. This paper concentrates on the importance of urban freight transport and logistics. Then, technical and logistics actions are outlined, and future last-mile delivery challenges are discussed. The main objective is to support urban planners, on a strategic and tactical scale, in obtaining an overview of urban freight transport systems that point out the challenges for implementing sustainable city logistics scenarios. It is also of interest to technicians because they can identify the most suitable methodologies, as well as the features that they need for selecting and assessing ex ante the effects of city logistics measures. This work is useful for researchers in various sectors because it allows them to formalise and then to solve the problems for simulating the complex system of urban goods movements where there are different actors with own interests that are conflicting.

Maritime collisions pose significant risks, prompting the need for robust risk assessment models to enhance safety measures. This study endeavors to develop a comprehensive ship collision risk model reflecting the intricate marine traffic environment in South Korea. Through a survey of experienced maritime personnel and a random forest model analysis, an evaluation model integrating internal and external factors was devised. Internal factors were determined through conjoint analysis, emphasizing encounter relationships, separation distance, and vessel speed. External risk factors were established using a random forest model based on historical collision data. The model’s efficacy was then applied to and validated in the vicinity of Busan Port, a region with complex marine traffic. The resulting risk map highlighted high-risk areas, offering valuable insights for risk management and policy formulation. This model provides a foundational framework for maritime safety policy decisions, representing a significant contribution to collision risk assessment methodologies.

Internet of Vehicles can improve driving and riding experience, provide information needs, reduce environmental pollution, and improve transportation efficiency, thereby promoting the rapid development and application of intelligent transportation. Especially through the advantages of the rapidity of information exchange and the flexibility of real-time data processing, an autonomous vehicle can provide barrier-free, safe, and sustainable transportation. At present, the rapid progress of the Internet of Things has promoted the continuous development of a fully autonomous vehicle. However, the autonomous vehicle network uses wireless communication technology, and the openness of its communication channel makes the communication process vulnerable to various security attacks. Therefore, this paper proposes a practical and efficient secure communication in the autonomous vehicle. This scheme utilizes elliptic curve cryptography, which can protect the security of multiparty communication between vehicle, cloud server, and user with lower computation and communication overhead. This paper provides security verification for the scheme by using Scyther. An informal security analysis shows that this scheme can resist multiple attacks. Through a comparative analysis and performance evaluation of the schemes, we found that the scheme improves security while maintaining efficiency.

Bike-sharing has a significant impact on commuters’ rational planning of their travel times, which can lead to an advance or delay in the peak passenger flow of the subway system during the morning peak. To explore the impact of bike-sharing on subway commuters’ choices of departure times, we developed a departure time choice model considering the effect of bike-sharing. This model considers both constant and linear marginal-activity utility and compares it with traditional departure time choice models. Research indicates that within the timeframe that ensures on-time arrival at work, models not accounting for bike-sharing services underestimate both the departure rate and the total number of commuters compared to actual figures. Specifically, under the constant marginal-activity utility, about 6.76% of commuters actually choose to depart earlier, while under the linear marginal-activity utility, this figure is 6.91%. Conversely, during the departure timeframes that lead to late arrival at work, the traditional model overestimates both the departure rate and total number of commuters. Finally, through case analysis, we further revealed the dynamic relationship between commuter departure rates, commuting fatigue, and number of bike-sharing and calculated the actual commuting costs for different proportions of bike-sharing. The results indicate that when the number of bike-sharing reaches 30% of the commuting demand, it can maximally reduce the commuting costs for commuters by approximately 23.32%. These findings offer a crucial basis for optimizing management strategies for morning peak subway commuting.