Journal of Advanced Transportation最新文献

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.

Spatial correlation is a critical factor in establishing accurate traffic accident analysis models, with the choice of measurement method significantly influencing the results. Despite the central role of roads as the primary conduit for traffic flow and a direct exposure variable in accidents, their impact on spatial correlation in accident analysis has not been fully explored. This study introduces an innovative spatial correlation matrix, termed the road matrix, which incorporates shared road lengths between grids to enhance accident prediction accuracy. The model examines the relationship between traffic accidents and various predictor variables, including land use, road networks, and public transportation facilities. Compared to traditional spatial correlation methods such as the rook and queen matrices, the road matrix provides a more precise characterization of spatial dependencies and significantly improves accident frequency estimation. Notably, the application of the road matrix within a conditional autoregressive (CAR) model uncovers additional significant contributors to traffic accidents, such as the number of interchanges and the length of nonexpress arterial roads. These findings offer new insights and practical recommendations for urban planning and traffic safety management. The study provides a valuable reference for future research on traffic accident frequencies and offers guidance for the design of more effective traffic safety measures.

Autonomous valet parking has drawn wide attention these years. The k-stacks layout, known for its ability to increase parking capacity by stacking vehicles more compactly, is of great practicality among all possible layout patterns. Although this layout can increase the capacity of a parking lot, it generates relocations, which let vehicles move additional distances and influence the lot’s peak hour service ability. For the sake of optimizing them all simultaneously, we propose a simulation-based multiple-objective optimization (SMOO) and use NSGA II to solve the problem, obtaining candidate solutions. Then, a nondominated sorting based on cumulative advantages (NSCA) method is put forward to select the most robust solution from all candidates, considering different demand scenarios. K-stacks parking lots optimized by the SMOO can provide 36%–59% more parking spaces than a traditional parking lot while keeping other evaluations fine. In addition, we specify high-demand and low-demand scenarios and discuss the impact of different aspect ratios. It is recommended to use k-stacks layouts when a lot’s length is close to its width.

This paper introduces an action replacement module for reinforcement learning (RL)–based ramp metering to address the issue of ramp queue spillback during the training process. Ramp queue spillback leads to significant impacts on the traffic efficiency of adjacent road networks, making it a critical concern in ramp control. Existing RL approaches often employ ramp states as reward functions to encourage agents to learn strategies that avoid queue overflow. However, due to the trial-and-error nature of RL, these methods frequently generate actions that cause queue spillback during training, posing challenges for real-time online training in real-world applications. To overcome this limitation, the proposed action replacement module utilizes the store-and-forward model to estimate a lower bound for ramp metering rates. By identifying and replacing actions that fail to meet this constraint, the strategy effectively prevents queue spillback. In addition, penalties are imposed on replaced actions to guide the agent in learning effective and practical control policies. The proposed method is evaluated in both single-ramp and multiramp scenarios. Experimental results demonstrate that the agent can learn the queue spillback prevention strategies, and nearly eliminate ramp queue spillback without compromising control performance.

The hit-and-run caused a delay in medical assistance to the victim and posed a significant threat to the safety of drivers in road tunnels. This study investigates the potential factors contributing to drivers’ hit-and-run violations in river-crossing tunnels. This paper built three models (the logit model, the random parameter logit model, and the random parameter logit model with heterogeneity in means) based on a dataset consisting of crashes reported in thirteen river-crossing tunnels in Shanghai, China. Potential contributors from five aspects (offending drivers, vehicle conditions, tunnel characteristics, environmental conditions, and crash information) were explored. Results showed that the random parameter logit model with heterogeneity in means produced the highest fitting accuracy among the three models. Eight important variables (nighttime, single-vehicle, multi-vehicle, two-wheeled vehicle, passenger car, heavy goods vehicle, rear-end, and short tunnel) were found to affect hit-and-run violations significantly. The research has highlighted that nighttime and short tunnel increase the likelihood of hit-and-run and other variables are the opposite. The results of this study could provide useful information for the development of interventions to improve the level of safety in tunnels and reduce the rate of hit-and-run offenses.