Journal of Advanced Transportation最新文献

The stable and efficient operation of rail transit networks (RTNs) is critical for the integrated development of metropolitan areas. However, numerous studies have indicated that RTNs are prone to large-scale cascading failures when subjected to disturbances. To address the limitations of traditional cascading failure models, this paper proposes an innovative cascading failure model for metropolitan areas RTNs, which incorporates nonlinear load fluctuations and the bounded rationality of passengers. This model aims to capture the cascading failure characteristics of RTNs with chaotic properties under 12 combination strategies. A single- and dual-parameter coupling analysis of chaotic evolution parameters and prospect theory parameters are conducted. Taking the RTN in the Chengdu metropolitan area as an example, both the static characteristics and cascading failure features of the network are analyzed. The findings reveal the following: (i) the RTN is a assortativity network and lacks small-world and scale-free properties. (ii) During network disturbances, a higher level of passenger familiarity with the network increases the likelihood of large-scale cascading failures. (iii) When passengers tend to avoid risks, stations with higher carrying capacity are more prone to failures. This study holds significant implications for ensuring the stable and reliable operation of rail transit systems within metropolitan areas.

With the emergence of Level 4 automated vehicles, it is necessary to investigate the impact of these vehicles on mode choice. Previous studies have looked at the potential benefits and drawbacks of automated vehicles, but there has been little research done on how these vehicles will impact individuals’ travel behaviors. This paper proposes a needs-based approach to study the impact of Level 4 automated vehicles on mode choice. The approach takes into consideration the travel needs of different individuals and their willingness to adopt new technologies. Through a stated preference survey in China, the data on travel preferences and the perceived safety levels of automated vehicles can be collected. Then, a model is built to simulate the adoption of Level 4 automated vehicles and estimate the mode split for different scenarios. The results indicated that private AV modes are preferred, and business and nonwork trips may be the targeted market for all AV modes. Overall, value of automation increases with income for private modes, with large variance. Furthermore, Pro-AV attitude has a positive effect on value of automation, especially for self-owned AV and AV subscription. Accordingly, the needs-based approach demonstrates a promising method to study the impact of new technologies on travel behaviors and provides insights for policy makers to promote more sustainable transportation systems.

This study proposes a cooperative lane-changing control framework for multiple vehicles in freeway ramp merging areas, aiming to achieve safe and efficient merging. Specifically, multiple connected and automated vehicles (CAVs) form triplets to participate in cooperative lane-changing. The framework consists of two stages: Longitudinal Headway Adjustment (LHA) and Lane-Changing Execution (LCE). In the LHA stage, a centralized longitudinal controller is developed based on the vehicle’s longitudinal dynamics model to optimize the longitudinal velocity of the cooperative vehicles and create suitable gaps for merging vehicles. In the LCE stage, an optimal lane-changing reference trajectory is generated using a quintic polynomial and a lateral controller is designed based on the vehicle’s lateral dynamics model. Model Predictive Control (MPC) is utilized for trajectory tracking. The simulation results obtained using MATLAB/Simulink, GPOPS, and CarSim demonstrate that the proposed control strategy can be applied to different vehicle speed control scenarios. Taking a specific velocity combination as an example, the cumulative control errors in the longitudinal and lateral directions for PV (Preceding Vehicle), SV (Subject Vehicle), and FV (Following Vehicle) are 1.4014 m, 0.5631 m, and −0.7601 m, respectively, satisfying the safety distance requirements. Compared to the Linear Quadratic Regulator (LQR) control, the proposed strategy improves control efficiency by 145.03%, 69.64%, 43.18%, and 67.61% in terms of comprehensive spacing errors, synthesized acceleration, front wheel angle, and speed fluctuation, respectively. These research findings highlight the superior performance of the proposed control strategy in terms of traffic efficiency, comfort, safety, and vehicle stability.

This paper describes a traffic signal control strategy that allows motorists who travel at a recommended speed on suburban arterial two-way roads with a common cycle time to make every traffic signal. A road-to-traveler-feedback-device (RTFD) advises motorists how fast they should travel to do this. Signalized arterial roads where vehicles that travel at the recommended speed make every traffic signal are termed Ride-the-Green-Wave (RGW) roads. Left-turn-arounds enable vehicles to turn left from two-way RGW roads to intersecting/orthogonal two-way RGW-road while allowing maximum flow at the intersection. The traffic signal control technique that enables vehicles that travel at the recommended speed to make every traffic signal has been verified using a simulation program (RGW-SIM). In addition to introducing novel traffic signal control strategies, the methods presented in this paper have implications for road network design, public transport control, connected and automated vehicles, and environmental impacts.

The identification of core stations in urban rail transit (URT) networks remains a vital issue in network structure organization analysis and an integral part of network reliability evaluation. However, the identification of critical stations with a single centrality metric has limitations and the varying interactions between stations cannot be ignored. In this paper, a novel integrated approach is proposed by using the principal component analysis and topological potential considering entropy (PCA-TPE) method. Taking the Shanghai metro (SHM) network as a case study, a Space L network model is constructed and the network topology characteristics are analyzed. Moreover, the susceptible-infected (SI) model and the network failure simulation are employed to demonstrate the effectiveness of the proposed method. The results show that the SHM network exhibits characteristics of both small-world networks and scale-free networks. According to the experiments of the SI model, the nodes obtained by the PCA-TPE method have stronger spreading influence than those derived by other methods, especially in the initial stage. The failure simulations illustrate that attacks against the nodes detected by the PCA-TPE method will lead to devastating network failures. Hence, the proposed method is effective for identifying critical nodes in URT networks, and the findings of the research can provide theoretical evidence for the development planning and emergency management of the public traffic system.

Technological advancements in the transportation sector have enabled new mobility solutions. Mobility as a Service (MaaS) is one such example that represents the integration of information technology-enabled apps with transport modes to provide door-to-door and affordable transport options to substitute private cars. Research in transportation is growing in focus on MaaS, and so are commercial MaaS products in various developed countries across the world. This study employs the systematic quantitative literature review approach to select scientific research articles on MaaS published to date and proposes a nested ecosystem framework involving actors, infrastructure, value, and customers. The ecosystem framework presented in this review provides valuable guidance to both transport sector academics and practitioners, highlighting the challenges involved in the successful deployment of MaaS schemes. In the end, this review provides future research directions to expand knowledge on MaaS to answer questions in the wake of fast-growing transport technology and global mobility patterns.

The rapid development of urban air mobility (UAM) has emphasized the need for in-flight control and passenger safety management. Recently, with the significant spread of technology in the field of computer vision, research has been conducted to manage passenger safety and security with vision-based approaches. Previous research predominantly focuses on single-task vision models, which limits their ability to comprehensively recognize various situations. In addition, conventional vision-based deep learning models require substantial computational power, potentially reducing the operational sustainability of UAMs with limited electrical resources. In this study, we propose a novel cabin surveillance framework for passenger safety and security. The proposed method achieves high accuracy by using a single model optimized for a specific task and ensures maximum computational efficiency through a scheduler that executes the appropriate models based on the situation. It can effectively perform roles such as detecting prohibited items and recognition of dangerous/abnormal behavior. Moreover, it simplifies the management of the involved models by adding new models or updating the existing ones, and it provides a sustainable system by reducing energy consumption. Through comprehensive experiments on various benchmarks, we validated the effectiveness of each model and verified the practicality of the proposed framework in terms of time complexity and resource usage through practical tests.

The rapid increase in e-commerce and the emergence of combined passenger/freight systems in urban areas have raised the question of how best to integrate public transport services into door-to-door deliveries. This paper develops a variant of the pickup and delivery problem, called the two-echelon pickup and delivery problem using public transport (2E-PDP-PT). In the 2E-PDP-PT, the transportation network is split into two echelons. Different vehicles are utilized across the first and second echelons to ensure distribution efficiency. Parcels are delivered by public transport with free capacity or via trucks between satellites in the first echelon, and logistics vehicles are operated in the second echelon. The satellites are located at the echelon borders to transfer commodities between echelons. The 2E-PDP-PT aims to minimize total delivery costs and improve public transport capacity utilization. We formulate a new mathematical model based on a space-time network and adopt an adaptive large neighborhood search (ALNS) algorithm for the 2E-PDP-PT. The effectiveness of the ALNS algorithm is validated using newly generated small-scale instances. Furthermore, we investigate large-scale instances based on the Beijing Yizhuang transportation network. The computations show that an average total delivery cost savings of 4.5% is feasible. In addition, we analyze the impact of demand distributions and compare the ALNS algorithm and the LNS algorithm. Finally, we conclude that dynamically integrating public transport into freight transport services can benefit both logistics companies and public transport operators.

Big data provide massive samples and resources for exploring the operating rules of public transportation. This article proposes a method that combines multiple data sources to identify potential bus passenger flows, aiming to address the issue of insufficient identification accuracy with a single data source. First, the spatially weighted K-means algorithm and improved DBSCAN algorithm are designed to partition traffic zones and residents’ travel flow OD is extracted based on mobile phone signaling data. Second, using bus IC card data and vehicle trajectory data, a method for identifying bus passenger boarding and alighting stops based on spatiotemporal clustering is proposed and the bus passenger flow OD for each traffic zone is calculated. By comparing the resident travel flow OD with the bus passenger flow OD, we set a threshold for the potential bus passenger demand proportion. Finally, the analysis is conducted using actual data from a city in central China. The city is divided into 43 traffic zones, with the maximum bus passenger flow proportion between zones being 14.9%, the minimum being 5.0%, and the average being 7.2%. The initial threshold for the potential bus passenger demand proportion is thus set to 7.2%, and a sensitivity analysis is conducted by gradually decreasing the threshold in increments of 0.5% to 6.7%, 6.2%, 5.7%, and 5.2%. The corresponding potential bus passenger demand OD pairs between traffic zones are identified as 419, 358, 245, 151, and 51. Urban managers should focus on the 51 pairs with relatively large potential flows to gradually optimize and balance the development of the bus network based on actual conditions. The method proposed provides important theoretical and practical support for effectively optimizing urban bus networks. However, there are limited indicators for identifying potential passenger flows; in the future, more multidimensional indicators will be taken into consideration.

Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT-enabled environments focusing on proposing an ML-based model for predicting available parking space. The study evaluates the performance of various models including K-nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and F1-score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and F1-score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT-enabled environment. On the other hand, models such as K-nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT-enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real-time scenarios, and better user satisfaction in the IoT-enabled environment.