Modeling passenger route choices is crucial for analyzing and predicting public transportation demand. One of the most popular methods is to use probabilistic route choice (PRC) models (also known as discrete choice models in general), which have broad applications in transportation, economics, politics, and other fields. However, its performance varies depending on the characteristics of the origin–destination (OD) trip data and should be examined carefully. This paper proposes a framework for validating the PRC model on its application to urban rail transit (URT) networks containing small‐scale OD trip data. The concept of small‐scale data is defined at first for each OD pair considering the desired confidence level and the variance of route choices. Then, a travel time range (TTR)‐based method is put forward to deduce passengers’ actual route choices as a benchmark for verifying PRC models. The difference and regularity analysis between the actual route choices and the model predictions are also performed with a twofold comparison. A case study on the Nanchang metro in China shows that the actual daily passenger volumes on routes of small‐scale OD pairs diverge remarkably from the estimations of the PRC model. The PRC model’s performance is further discussed when the small‐scale OD trip data accumulate to a larger scale over multiple days (e.g., several months). This study reveals the inherent limitation of PRC models in estimating the travel behaviors of passengers in a small‐scale population. Several practical implications are discussed to improve the route choice model and passenger flow analysis.

Passenger pushing behavior during emergency evacuations on roll‐on/roll‐off (Ro‐Ro) passenger ships is a critical yet overlooked factor in evacuation modeling. This study investigates the impact of pushing behavior on evacuation dynamics by employing an improved social force model (SFM) that integrates pushing forces and the ship’s inclination angle. Four evacuation scenarios are simulated to evaluate the impacts of pushing behavior and falling incidents. Results show that (1) moderate pushing can slightly shorten evacuation time without significantly increasing the risk of falling; (2) excessive pushing induces localized congestion, elevates the probability of falls, and ultimately prolongs evacuation time—under severe pushing conditions, total evacuation time increased by 45.4% compared with the no‐pushing baseline; and (3) ship inclination significantly affects passenger stability, particularly near exit bottlenecks and in narrow passages. The findings enhance the realism of evacuation simulations and provide practical insights for optimizing crowd management strategies on Ro‐Ro passenger ships.

Urban expressways serve as the main arteries of urban transportation. Congestion and traffic disruptions on expressways can easily lead to the paralysis of the entire regional transportation system. Accurately understanding the patterns of congestion formation and influencing factors on expressways is beneficial for improving traffic efficiency and reducing travel costs. This study takes typical sections of expressway diverging and exit sections as examples, introducing traffic data provided by navigation systems to explore the potential influencing factors and formation processes of urban expressway traffic congestion. This study explores the effects of operating conditions, control facilities, road properties, weather, and other factors on the smoothness of exit sections based on navigation and field survey data. The traffic congestion index is used as an indicator of congestion degree to evaluate the smoothness of typical area of urban expressways exit sections and the overall safety of urban roads. A structural equation model is used to construct a traffic congestion impact model. The results show that traffic facilities (β = 0.462, p < 0.001), road conditions (β = 0.177, p < 0.001), road location (β = 0.129, p < 0.001), spatiotemporal characteristics (time of day: β = 0.295, p < 0.001; day of week: β = −0.105, p < 0.001), environment (β = 0.021, p < 0.001), and driving behavior (β = 0.326, p < 0.001) have a significant impact on traffic congestion. And driving behavior can be used as an intermediate variable to affect the relationship between transportation facilities, road conditions, road location, spatiotemporal characteristics, environment, and traffic congestion. The research contributes to a precise understanding of the formation patterns and influencing factors of urban expressway traffic congestion, laying the groundwork for the adoption of targeted traffic management measures to improve traffic flow efficiency and reduce accident occurrences.

Automated warehousing and distribution has been an innovation approach to reduce costs and increase efficiency in logistics industry. Taking the intelligent demonstration warehouse in a commercial logistics park in Shandong, China as the background, this paper constructs a platform resource scheduling model under the Automatic Guided Vehicle (AGV) sharing mode to solve the problems of platform allocation and equipment scheduling, and solves it using the simulated annealing algorithm. This paper designs First‐Come, First‐Served (FCFS) rule, platform resource scheduling rules when AGVs are used separately, and platform resource scheduling rules when AGVs are shared, outputting platform operation scheduling schemes. Meanwhile, different numbers of AGVs are scheduled under the AGV sharing mode to validate the model and algorithm. The results show that the platform resource scheduling model proposed in this paper improves the platform utilization rate by 4.4% compared to the traditional FCFS rule, and the latest departure event is advanced by 95 min. The AGV sharing mode can complete vehicle loading tasks in a shorter time and with faster operational efficiency.

This study investigates the integration and prioritization of connected vehicle infrastructure services (CVISs) in mixed autonomy traffic systems using a fuzzy–analytic hierarchy process (Fuzzy–AHP). The study aims to enhance operational efficiency in environments where autonomous vehicles (AVs) and human‐driven vehicles (HVs) coexist. By evaluating 92 existing services, the research selects and prioritizes 17 critical services that address safety and efficiency challenges. The methodology involves a Fuzzy–AHP analysis to assess service importance and a modified–importance–performance analysis (M–IPA) to categorize services as either specialized or common based on their utility for AVs and HVs. The findings highlight the pivotal roles of emergency management, traffic operation, and pedestrian detection services in improving traffic safety and flow. This study contributes to the theoretical and practical understanding of CVIS implementation, offering a framework for policymakers and engineers to optimize infrastructure in mixed autonomy traffic scenarios.

Infrastructure‐to‐infrastructure (I2I) communication enables the exchange of traffic data between intersections, which brings a new challenge to urban traffic control. This paper proposes a novel deep reinforcement learning (DRL) framework for urban traffic signal control within the vehicle‐to‐everything (V2X) ecosystem. The framework incorporates a joint‐state representation integrating traffic data from both I2I and vehicle‐to‐infrastructure (V2I) communications, while considering communication range effects. The reward function is designed to optimize both local intersection conditions and global network performance, facilitating adaptive signal coordination. A simulation case study in Huzhou, China, evaluates the proposed model against conventional pretimed, actuated, nonjoint‐state DRL, and joint‐state reinforcement learning (RL) models. Results demonstrate superior performance of the joint‐state DRL model in episode rewards, average speed, and average time loss, particularly during peak traffic periods. To address data limitations, Monte Carlo cross‐validation (MCCV) is employed, further validating the model’s robustness. Results show consistent performance advantages in average speed and time loss across peak and off‐peak periods, with slight variations compared to joint‐state RL models in certain intervals. The impacts of the communication range are also discussed with the proposed model. Pearson correlation analysis reveals a strong positive correlation between the communication range and convergence time across all traffic periods. Meanwhile, correlations between the communication range and reward, average speed, and average time loss vary by traffic period. Findings highlight the transformative potential of integrating DRL with V2X communication technologies for enhancing traffic signal control in complex urban environments. The proposed model offers a flexible, adaptive approach to traffic management, optimizing flow while maintaining safety standards, with implications for future smart city developments.

Real‐time safety evaluation of urban signalized intersections is a prerequisite for proactive traffic safety management. Due to its independence from historical data, the traffic conflict technique has gained increasing popularity as a tool for real‐time safety evaluation in transportation systems. However, different types of conflicts (e.g., rear‐end and side‐impact conflicts) may lead to differences in safety evaluation, which has led previous studies to typically analyze crash risk based on various conflict types separately. Therefore, this study develops the hierarchical Bayesian extreme value theory (block maxima (BM)) model based on different conflict types to form a novel real‐time safety evaluation approach. The proposed model is applied to the real‐time safety evaluation of five signalized intersections in Harbin, China. The results show that (1) the proposed BM model exhibits good prediction performance when there is sufficient observation duration and a sufficient number of samples; (2) the proposed model is superior to other baseline models developed based on only one conflict type in terms of prediction accuracy. The empirical findings of this study establish innovative frameworks and theoretical foundations for advancing proactive safety protocols and autonomous mobility systems.

With the development of a sustainable economy, higher requirements are put forward for logistics enterprises, which not only need to meet the requirements of profit growth but also to meet the need of sustainable development. A vehicle routing problem (VRP) optimization model considering carbon emissions and multifuel‐type vehicles (VRP‐CEMF) is proposed to solve the problems of air pollution and high transportation cost in the current logistics distribution. An improved genetic algorithm (IGA) is designed to solve the VRP‐CEMF. The impact of carbon emissions and multifuel‐type vehicles on the logistics distribution path is explored by a real example simulation. The results show that the logistics distribution path optimization considering carbon emissions and multifuel‐type vehicles including hybrid electric vehicles and hydrogen‐fueled vehicles can significantly reduce carbon emissions on the premise of ensuring the lowest total cost. Furthermore, the impact of carbon emissions, hydrogen fuel price, and customer demand on the logistics distribution path is discussed by sensitivity analysis. The research results of this paper provide an effective reference for enterprises to control carbon emissions in the process of logistics distribution and promote the green transformation of logistics.

To identify the key causes of major road traffic accidents (resulting in three or more deaths), this study constructed an accident causation network based on association rules and complex networks using data from 173 major traffic accidents over the past decade. Initially, 62 potential risk factors were extracted from aspects such as human, vehicle, road environment, and management. Association rule algorithms were then employed to explore the coupling relationships between these risk factors, generating strong association rules. Finally, a complex network model was built based on these association rules to identify critical risk factors. Results indicate that (1) 80% of major traffic accidents are linked to poor driving behavior. Complex network analysis identified speeding, overloading, lane crossing, and failure to maintain safe following distances as primary human factors, which are closely related to vehicle, road, and environmental conditions, contributing collectively to accidents. (2) Association rule results revealed that head‐on collisions are primarily related to lane crossing and occur on national and secondary grade highway; falling accidents are common on roads with inadequate infrastructure; rear‐end collision often happen on expressway and national highway, especially at night (18:00–07:00) with freight vehicles; vehicle‐related major accidents are usually associated with noncompliant vehicle performance, vehicle failures, overloading, and insufficient regulation. Major accidents involving inadequate road signs, markings, and safety barriers have a significant association with nighttime periods. The conclusions of this study can provide valuable insights for the prevention of major road traffic accidents.

Rainy weather significantly affects traffic efficiency and safety on freeways. This study investigates a safe speed limit model for freeways under rainy conditions, considering both human‐driven vehicles (HVs) and automatic vehicles (AVs). The relationship between safe speed limits and rainfall intensity is quantified. The intelligent driver model (IDM) was selected as the basis for this study due to its complexity and applicability. By analyzing the effects of rainfall on model parameters, an improved IDM was proposed, optimizing the expected speed. Simulation results demonstrate that the proposed speed limit method effectively reduces collision rates, with AVs showing superior traffic efficiency, stability, and safety compared to HVs.