Journal of Advanced Transportation最新文献

Due to the high incidence of pedestrian collisions at intersections and the vulnerable status of pedestrians, an increasing number of researchers are focusing on pedestrian safety. Currently, researchers tend to apply surrogate safety measures (SSMs) in pedestrian safety analysis, with Time to Collision (TTC) and Post Encroachment Time (PET) being the most widely used. However, their application is subject to certain conditions and limitations. Therefore, this study proposes a novel traffic conflict indicator called improved Time Advantage (improved TAdv), designed to identify pedestrian-vehicle conflicts without collision trajectories. A conflict model for right-turning vehicles and pedestrians at signalized intersections is developed to analyze the impact of various factors on the probability of pedestrian-vehicle conflicts. The results indicate that the improved TAdv offers advantages over traditional traffic conflict indicators. Meanwhile, the findings emphasize the spatial disparities of right-turning pedestrian-vehicle conflicts and analyze the impact of vehicle speed at different locations on the likelihood of such conflicts, offering new insights into pedestrian safety analysis.

S-curves are common and complex curves in road networks, consisting of two curves in opposite directions connected by a short segment. Drivers’ behavior on S-curves reflects their ability to navigate complex environments, which is not yet fully understood. This study examines speed patterns on five S-curves using a naturalistic driving dataset of 2676 vehicle trajectories. A hierarchical clustering method is proposed to identify typical speed patterns on S-curves, and a mixed logit model is developed to analyze the influence of curve radius, slope, and lighting conditions on drivers’ speed pattern choices. The empirical findings reveal that (1) each S-curve has dominant speed patterns, and these patterns vary across different S-curves; (2) speed patterns generally exhibit three types of shapes: S-shaped, U-shaped, and anti-S-shaped; and (3) the curve radius, the difference in radii between the two adjacent curves, the slope, and lighting conditions significantly influence drivers’ speed pattern choices. These results provide valuable insights for S-curve alignment design, speed guidance strategies, and speed planning algorithms for autonomous vehicles.

In light of the escalating global concern over road traffic safety, which claims over a million lives annually, this study endeavors to fortify the foundational structures of emergency road rescue systems (ERRSs) through the lens of advanced theoretical modeling. Recognizing the unpredictability of road accidents in temporal and spatial dimensions, we propose a novel assessment methodology leveraging the mⁿ hypercube queuing model (mⁿ HQM) to account for the stochastic nature of road accidents and the variable service rates driven by demand-side factors such as geographical location disparities. The essence of our contribution lies in the development of the approximate hypercube queuing (AHQ) algorithm, designed to address the computational complexities inherent in large-scale ERRS, making it possible to evaluate ERRS under a wide range of scenarios with improved accuracy and efficiency. Validation of the AHQ algorithm demonstrates its reliability and effectiveness in capturing the dynamics of emergency road rescue operations. Further, the application of this novel assessment method to a real-world road rescue case in the X Mountain area offers critical insights into the system’s performance. These findings underscore the potential of our approach to enhance the operational readiness and responsiveness of ERRS, thereby contributing to the reduction of casualties and losses in the aftermath of road traffic accidents.

Assessing the resilience of road networks in high-intensity urban development areas is crucial for ensuring sustainable urban growth in the face of increasing traffic demands. Underground road networks are a key solution to alleviating surface traffic congestion and optimizing urban spatial utilization. By enhancing shared mobility on the surface, these networks contribute to reducing vehicle emissions and mitigating environmental pollution. This study explores the optimal conditions for initiating underground road network construction in high-density development areas. Using a spatiotemporal consumption model, the research calculates the maximum traffic capacity of the network while considering land use characteristics to assess traffic demand. The resilience of these road networks is evaluated through structural indicators that measure their resistance to damage and overall stability. The findings indicate that when the demand-to-capacity ratio of the road network ranges from 0.865 to 0.870, the existing road capacity becomes inadequate, necessitating the construction of underground networks to alleviate surface congestion. This study provides both theoretical guidance and practical insights for the planning and development of underground road networks, along with strategies to improve surface environmental quality.

The irregular arrival patterns of container trucks at ports have a substantial impact on logistics operations’ efficiency, resulting in congestion during peak hours and unused port capacity during idle times. Implementing a truck appointment system (TAS) is vital to address this issue effectively. This paper suggests enhancing the TAS by adopting a data-driven approach using terminal gate data to understand the intricate and uncertain relationship between truck arrival patterns and port operational efficiency. Insights gained from these data are utilized to develop a distributionally robust optimization (DRO) model. This model provides an exact solution for optimizing the appointment quota plan of TASs, thereby improving port efficiency and addressing operational challenges. Compared to existing methods, this approach does not heavily rely on theoretical assumptions concerning the cooperation mechanisms among trucks, yard equipment, quayside equipment, and other facilities and fully considers the complex uncertainties in truck arrivals. Furthermore, to examine the effectiveness of the proposed model, a case study is conducted at Yan Port, China, aiming to achieve practical results. The numerical experiments comparing its performance with the conventional robust optimization (RO) model confirm the superiority of the proposed DRO model in minimizing the total truck turnaround time within the terminal and overall time expenses. This superiority stems from its integration of the respective advantages of stochastic optimization (SO) and traditional RO methods. By optimizing the appointment quota plan in this manner, it achieves a balanced distribution of truck arrivals, showcasing its significant potential to enhance port logistics efficiency.

This paper utilizes trajectory data of electric taxis in Shenzhen to first study the driving characteristics of electric taxis from different indicators such as travel/driving speed, overspeed ratio, and overspeed amplitude. A combination weighting VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) evaluation method based on Nash equilibrium is constructed to comprehensively assess the operation safety of electric taxis in various spatiotemporal scenarios. The results show that the average driving speed and overspeed amplitude of battery electric taxis is 39.14 km/h and 22.78%, which is 1.27 and 1.72 times that of fuel taxis. The average overspeed ratio, average acceleration and deceleration and total acceleration and deceleration frequency are all more than twice those of fuel taxis. The average acceleration, deceleration, and idle time ratios of battery electric taxis are 0.27, 0.24 and 0.43, respectively, with a constant speed time ratio of only 0.06. The proposed combination weighting VIKOR evaluation method outperforms other evaluation methods in comprehensively considering data discreteness and correlation. Electric taxis have the highest operational safety on arterial roads, while the safety on minor arterial roads and local roads is relatively poor, especially on weekends. In all spatiotemporal scenarios, the overall operation safety of electric taxis is lower than that of fuel taxis. The research results can provide theoretical support for the formulation of effective measures and policies to reduce dangerous driving behavior of electric taxis.

The increasing use of bicycles highlights the need for enhanced road safety measures, particularly in interactions between vehicles and cyclists on rural mixed-traffic roads. This study investigates the impact of driver age and behavior on the effectiveness of advanced driver assistance systems (ADASs) in improving cyclist safety. Utilizing a driving simulator, the study analyzed the overtaking maneuvers of 300 male participants, categorized by aggressive and passive driving styles, across three age groups: young (20–34), middle-aged (35–49), and older (50–64) drivers. Results showed that younger drivers exhibited more dynamic and erratic behaviors, with significant variations in lateral control (LC) and time to danger (TTD). Specifically, younger driver’s TTD increased by 20% on average, while older drivers maintained consistent caution with a 10% improvement in LC. Aggressive drivers showed a negligible change in behavior, whereas passive drivers demonstrated a 25% improvement in TTD and a 15% enhancement in LC when using ADAS. The findings suggest that tailored ADAS features are necessary to address the diverse responses of different driver demographics. Future ADAS development should incorporate real-world testing, consider psychological factors, and conduct longitudinal studies to optimize safety outcomes. This study provides critical insights for enhancing the design and implementation of ADAS to protect vulnerable road users such as cyclists.

Bangladesh has a significant prevalence of motorcycle usage accompanied by a correspondingly high incidence of motorcycle-related fatalities. The COVID-19 crisis has brought additional challenges to road safety in Bangladesh because of containment strategies and restrictions. The impacts of the pandemic on motorcycle-related road traffic crashes, injuries, and fatalities in Bangladesh are investigated in this study using ARIMA time series analysis. Data spanning 86 months (January 2016 to February 2023) were collected from the Accident Research Institute (ARI), which compiles newspaper-based data serving as an alternative source of information on crashes encompassing both pre-COVID (January 2016 to February 2020) and COVID-19 periods (March 2020 to February 2023). Three COVID-19 waves were demonstrated, with the first wave showing a significant decrease in crashes, injuries, and fatalities due to a government-imposed lockdown. During the second wave, crashes and fatalities approached predicted values, while injuries remained lower than anticipated. The third wave witnessed a sudden drop, followed by a sharp rise in all three variables. Box and whisker plot analysis confirmed the disparities between observed and predicted values, with observed data being lower. These results demonstrate the significant impact that COVID-19 containment strategies have had on trends in motorcycle crashes. By understanding these patterns, policymakers and road safety authorities can develop adaptive interventions to mitigate motorcycle-related incidents during pandemics or similar crises. The study provides a data-driven foundation for designing context-specific policies, adjusting law enforcement strategies, and efficiently allocating resources to enhance road safety under varying conditions.

The run-off-the-road (ROR) vehicle from the curves, as one of the most accident-prone sections of roads, has always received special attention. Centrifugal force on vehicles and human error are the two main causes of accidents in these areas, which will eventually lead to overturning or sliding of vehicles. Based on previous research, few studies have been conducted on the influence of friction factors over horizontal and vertical curves with foreslopes for ROR vehicles considering various factors such as vehicle type, speed, departure angle, and foreslope slope through the vehicle dynamics simulation. Thus, in this research, the safety of ROR vehicles on curves over the foreslope was investigated from the perspective of the vehicle dynamics simulation. Finally, by simulation outputs for each of the vehicles used (Sedan, SUV, and truck), a multiple regression modeling was presented to examine the side friction factor of horizontal and vertical curves with a foreslope. The results showed that for horizontal curves, the first third of the beginning of the curve was the most dangerous part when vehicles deviated from the curves. Also, in vertical curves, the departure angle of 15 and 25° for vehicles, and foreslopes of 1: 3 and 1: 4, had the greatest effect on the overturning points of the vehicles. In addition, trucks had fewer friction factors at all speeds in comparison with Sedans and SUVs, and consequently, they had lower skidding potential in all specified conditions. On the other hand, an increase in skidding potential was observed in all tests on steeper foreslopes, which was caused by increasing the side friction factors and decreasing the margin of safety of vehicles on these types of foreslopes. Finally, based on the multiple regression analysis, the best model was presented to predict the side friction factor for various vehicles on horizontal and vertical curves with a foreslope, and it was indicated that the obtained models had a good correlation for all the test conditions. The study’s findings can be applied to improve road safety by modifying road geometry, adjusting foreslope angles, enhancing pavement friction, and informing vehicle design and driver education programs.

Within the framework of China’s “Dual Carbon” strategy, numerous cities have articulated visions and objectives for optimizing the travel structure of transport to further urban sustainable development goals. A critical challenge lies in minimizing CO₂ emissions from road networks while meeting diverse transport demands. However, at present the mode shares are set arbitrarily and may not be realistically achievable. When government officials establish travel structure targets, they may not adequately consider the intricate balance between residents’ travel demands and low-carbon development objectives. To address this issue, this paper presents a dual-layer optimal allocation model for transport modes, which simultaneously addresses travel demand management and carbon emission control. The upper-layer model evaluates carbon emissions with the help of speed-dependent emission factors for various transport modes, and the lower-layer model leverages the logit Stochastic User Equilibrium (logitSUE) model to yield the velocities of road segments under a diverse array of travel structures. A sophisticated fusion algorithm, integrating the Dial_MSA algorithm with a genetic algorithm (GA), is developed to solve the model. The proposed model and algorithm are tested on a large-scale real network and show its robustness and scalability. The optimal travel structure derived from this study can provide a theoretical foundation and empirical support for policymakers and urban planners in setting transport infrastructure goals and strategies.