Journal of Advanced Transportation最新文献

Given the intricate interactions among the economy, land use, and transportation, it is important to assess the multifaceted impacts of the freight corridor on these systems. This study introduces a land-use transport interaction (LUTI) modeling approach to quantify the scope and magnitude of the impact of a railway corridor on the distribution of freight flows within an intermodal service network and industrial locations. The proposed model operates on two levels to analyze regional interactions between industrial locations and transportation dynamics. The upper-level model simulates the industrial interactions among regions. Meanwhile, at the lower level, interregional trade connections are converted into freight demand, which is then assigned to a transportation network. Calibration of the trip length distribution is achieved by integrating data from truck GPS, railway waybills, and ship visas to develop a connection between the intermodal service networks and economic systems. The improved model offers a LUTI modeling approach tailored for the context of intermodal transportation, considering economies of scale associated with intermodal transportation services. It can not only analyze the impact of a freight corridor on freight flow redistribution but also identify areas that may be adversely affected by such redistributions. Taking the Yangtze River Economic Belt (YREB) as a case study, study results indicate significant enhancements in the economies of scale of railway services within the middle part of the YREB due to the development of the Haoji Railway Corridor (HRC). These changes significantly influence the location utility of industrial activities, with the coal processing activities demonstrating the most sensitivity to the evolving transportation dynamics. This study offers insights into LUTI modeling approaches specifically tailored for intermodal transportation.

Intersections are vital components of urban road traffic management, frequently facing persistent congestion challenges. Existing studies rarely combine multiobjective optimization with dynamic adjustment methods. This study introduces an innovative dual-layer framework for traffic signal optimization. The first layer involves multiobjective optimization, addressing critical performance metrics such as delay, the number of stops, and fuel consumption. In the second layer, we propose a method that uses a fuzzy neural network to learn the correspondence between queue lengths and signal timings. This two-tiered approach enables real-time adjustments, achieving dynamic signal optimization. Applying this framework with real traffic flow data to a specific road intersection allows us to determine optimal signal timings dynamically. Extensive simulations using the SUMO software validate the efficacy of our approach in enhancing intersection performance. The timing strategy implemented within this framework leads to a substantial reduction in delay, ranging from 11.1% to 29.0%. The dual-layer framework presented in this study contributes valuable theoretical insights into future research initiatives in this domain.

Real-time train rescheduling for high-speed railway (HSR) is a pivotal technique in HSR transportation to efficiently recover train operation under disturbance scenarios. This paper aims to put forward an integrated resolving and rescheduling method considering network delay propagation. A tree-based conflict resolution mechanism is first established, with delicate considerations on the strategy’s adaptability under different conflict scenarios. By inputting the scheduled arrival and departure time under different conflict resolution strategies, the timetable optimizing model aims to look for an optimal solution with minimal weighted train delay and average train adjustments under necessary technical and empirical constraints solved by a combined algorithm of Pareto optimality and Nash equilibrium, where the feasible solution space is narrowed in advance by a depth-first pruning algorithm. The performance of this coordinated train rescheduling approach is validated by a typical section disturbance in a regional HSR network administrated by the Shanghai Bureau. The results show that the proposed method can well utilizes timetable buffers and organizes train avoidance. The delay propagation characteristics are also simultaneously estimated based on the indicators of cumulative delay and instantaneous delay, which are established considering the spatio-temporal difference between the scheduled and planned timetables, in order to verify the coordination between resolution strategies and train running delays.

This paper introduces a bilevel programming model for optimizing transit network departure frequency. In the upper-level model, user satisfaction is reflected by considering congestion effects in the cost function. The lower-level assignment model simulates passenger travel behavior more realistically by incorporating congestion effects. This problem is solved by a heuristic gradient descent algorithm, where an approximation of the gradient is obtained at each iteration by using sensitivity analysis for transit equilibrium problems. The effectiveness of the proposed model and algorithm is demonstrated through two test examples, one of which involves a real-world scenario comprising over 130 transit lines. Numerical results conclusively indicate that the incorporation of congestion effects in the proposed model leads to improved transit system performance and enhanced user satisfaction.

With the increasing density of the freeway network, frequent traffic incidents on road segments have a significant impact on the operational efficiency of the road network. Therefore, it has become urgent and important to study traffic route guidance strategy on the road network level. The previous traffic route guidance method primarily focused on the congestion on the road segments where incidents occurred, with insufficient attention given to the impact of congestion on the road network level. In this study, a route guidance model with limited overlap is proposed to improve freeway network reliability under traffic incidents. Specifically, in order to explore alternative paths, we conducted a study on the problem of finding k-short paths with limited overlap. The objective is to identify a set of k-paths that are both sufficiently dissimilar and as short as possible. Then, we promptly update the route guidance information using a stochastic dynamic traffic assignment model that aligns with travelers’ path choice psychology. Moreover, we use the reliability of the road network to evaluate the network performance. To illustrate the model, the Jinan freeway network is selected as an experimental study. The effectiveness of this method was validated through SUMO simulations, comparing it with alternative route guidance methods, including Yen’s algorithm, A^∗ algorithm, and ant colony algorithm. These results show that the proposed method has proven effective in mitigating traffic congestion arising from incidents and performs well in regard to the reliability of the road network under the impact of incidents.

To investigate the effects of proactive safety control systems suitable for highway interchanges and improve road traffic safety. Simulated driving experiments were conducted to test the effects of the interchange warning system (IWS) on the ramp, merging section, diverging section, and accident section. Random forest (RF) and SHapley Additive exPlanations (SHAP) are used to analyze the effects between driving behavior and driving risk change in both situations without and with IWS. The results show that (1) as driving risk increases, drivers tend to increase the frequency of braking and engage in more comprehensive saccade behaviors. Concurrently, there is an increase in acceleration and speed variation, leading to a gradual decrease in speed. (2) Compared with the SVR and XGBoost, RF can better fit the nonlinear relationship between driving risk and driver behavior characteristics with the application of IWS. (3) The IWS mainly reduces driving risk by affecting operation behavior. When the mean speed, speed standard deviation (SD), acceleration SD, and maximum braking depth are at 40 to 70 km/h, 3 to 10 km/h, 0 to 0.6 m/s², and 14 to 16, respectively, there is a significant reduction in driving risk. The application of the IWS expands the effective range of mean speed and speed SD for reducing driving risk to 40 to 100 km/h and 3 to 15 km/h, respectively.

This paper presents a Laplace transform model for an urban tunnel ventilation system. This model allows one to witness higher performance for supervisory control and data acquisition (SCADA) in terms of monitoring and control of an urban area tunnel based on measurement systems. This proposed model illustrates the ventilation control system framework as well as the emergency response system for urban area tunnels such that smoother controllability and higher security in the operation of tunnels can be envisioned. The salient contributions of this work can be stated as a novel method for modeling tunnel ventilation systems and the implementation of an emergency response plan for a futuristic intelligent transportation system. The simulation results exhibit that the proposed model outperforms the ventilation system in the high-density traffic jams and further the efficient operation of the tunnel. Likewise, comparison results and experimental results are addressed to emphasize the validation of this method and to be helpful in proving the reliability of the results obtained in this study. These results show that the ventilation control system reaches the desired CO value either in high-traffic volume conditions or in normal traffic conditions.

Road accidents are a major cause of injuries and deaths worldwide. Many accident victims lose their lives because of the late arrival of the emergency response team (ERT) at the accident site. Moreover, the ERT often lacks crucial visual information about the victims and the condition of the vehicles involved in the accident, leading to a less effective rescue operation. To address these challenges, a new Internet of Things (IoT)-based system is proposed that uses on-vehicle sensors to detect and report the accident to rescue operator without any human involvement. The sensor data are automatically transmitted to a remote server to create a visual representation of the accident vehicles (which existing systems lack), facilitating the situation-based rescue operation. The system tackles any false reporting issue and also sends alerts to the victim’s family. A mobile application has also been developed for eyewitnesses to manually report the accident. The proposed system is evaluated in a simulated environment using a remote-controlled car. The results show that the system is robust and effective, automatically generating visuals of accident vehicles to facilitate informed rescue operation. The system has the potential to aid the ERT in providing timely first aid and, thus, saving human lives.

Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall’s impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people’s mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents’ travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions.

In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation.