Fuquan Pan

Undersea tunnels play a crucial role in modern transportation systems by facilitating undersea connectivity and supporting urban mobility. However, the entrance sections of these tunnels pose significant traffic safety challenges, primarily because drivers have limited ability to adjust speed in response to sudden environmental and geometric changes, leading to increased accident risks. Although variable speed limit signs (VSLSs) and visual speed reduction markings (VSRMs) have been proposed to address this issue, existing regulations lack detailed guidance on their coordinated layout. To fill this gap, this study systematically investigates the synergistic effects of combining VSLSs and VSRMs at undersea tunnel entrances. Using UC‐win/Road and SketchUp to construct simulation scenarios and employing the Blue Tiger driving simulator to collect behavioral data, 12 coordinated layout schemes were designed, involving different types of deceleration markings (transverse, longitudinal, and fishbone shaped) and various spacing intervals. The comparative analysis of data under each scheme enabled a quantitative evaluation of their effects on speed management and operational stability. The results indicate that fishbone‐shaped VSRMs with a 40‐m coordination distance achieve the optimal deceleration effect and can be recommended as an optimized layout scheme for improving traffic safety at undersea tunnel entrances.

To accurately forecast the passenger flows of existing stations in urban rail transit and provide a scientific basis for subway operation management departments, a passenger flow prediction model optimized by an unbiased grey Markov chain was constructed. Using 18 months of average daily passenger flow data from Shilaoren Bathing Beach Station on Qingdao Metro Line 2, a grey prediction model was established and tested. The data were classified into six states according to their relative errors for Markov state modeling, and the classification results were validated. A state transition probability matrix and the corresponding k-step transition probabilities were then established. The unbiased grey Markov chain prediction model was used to predict the passenger flow data of existing stations in the next 3 months. By comparing the prediction results with the actual passenger flow data, it was found that the prediction error after optimization by the unbiased grey Markov model was controlled within 10%, which corresponded well with the actual characteristics of urban rail transit passenger flows. This model is suitable for predicting the passenger flows of existing urban rail transit stations and can provide an effective reference for managing subway operations and ensuring safety.

The study explores the relationship between subway passenger satisfaction and passenger travel behaviour characteristics from the perspective of subway passengers. This study takes Qingdao as an example and designs a questionnaire that includes the basic personal information and travel behaviour characteristics of passengers and evaluates their subway satisfaction. A total of 6340 valid questionnaires were obtained through the combination of online surveys and on-site random surveys. By using the fuzzy synthetic evaluation, the overall score of passenger satisfaction with the Qingdao subway is determined. According to the relationship between passenger satisfaction and travel behaviour characteristics, the chi-square test is used to select the correlation variables group. The results show that there is no significant correlation among the satisfaction of subway passengers, the main means of transportation and the availability of private cars; the frequency of taking the subway is related to the satisfaction of subway passengers; and the purpose of travelling and the reasons for choosing the subway are significantly related to the satisfaction of subway passengers. Finally, based on the analysis of the differences in satisfaction under different conditions, some suggestions were proposed to improve the satisfaction of subway passengers.

Owing to undersea‐tunnel constraints concentrating off‐ramp maneuvers in confined zones, this study optimizes graded lane‐changing strategies to mitigate collision risks. Using the Jiaozhou‐Bay Undersea Tunnel case, we propose an innovative exit diversion area graded lane‐changing strategy comprising Transition Section I, Transition Section II, gradient section, and auxiliary lane. Six schemes were simulated via UC‐WinRoad, with driver physiological stress quantified through Tobii eye‐tracking as a novel application of pupil dynamics. Four indicators—lane‐change position, lane‐change rate, pupil diameter, and speed change—were weighted by the integrated analytic hierarchy process and entropy weight method (AHP–EWM) methodology and evaluated via the set pair analysis with the technique for order preference by similarity to ideal solution (SPA‐TOPSIS) theory model. Optimal Scheme E (290‐m transition I, 210‐m transition II, 120‐m gradient, and 140‐m auxiliary lane) achieved γ = 0.968, significantly reducing pupil fluctuation by 32% compared with the shortest design (Scheme A) while ensuring smoothest speed control. This demonstrates effective conflict distribution in high‐risk undersea environments, providing universally applicable design benchmarks for tunnel safety enhancement.

Efficient proactive emergency vehicle planning is crucial for effective emergency response management systems. This paper develops a flexible deep reinforcement learning framework to address uncertainties in dynamic emergency requests and complex traffic conditions. The Markov decision process model is proposed to optimize ambulance dispatch and reallocation, focusing on minimizing average response time, reducing delays from traffic congestion and patient severity misclassification, and enhancing general fairness. To tackle the challenges of the unbounded MDP state space, we employ a least-squares-based approximate policy iteration model for the upper bound and a linear programming model for the lower bound. We use Chinese emergency medical services system data for computational experiments and select average response time, fraction of delay, risk level, and Gini coefficient to evaluate the performance of our model. The numerical results demonstrate our optimal flexible policy outperforms the first-in-first-served rule, improving both efficiency and equity in medical decision-making.

The influence of passenger gender, age, educational background, and other personal characteristics on satisfaction with an urban rail transit was studied. In total, 6340 valid questionnaires were completed, and basic data about the passengers were statistically analyzed. Based on AHP and the fuzzy comprehensive evaluation method, 94.594 percent of passengers reported overall satisfaction with the Qingdao rail transit; the data for subgroups based on gender, age, and other aspects were also calculated. An independent samples t-test and one-way analysis of variance were used to analyze the correlations between passenger satisfaction and the following parameters: gender, age, education, occupation, income, ride frequency, and private car availability. The results show that women attach more importance to the caring they feel in the process of travel than men, and no significant difference exists in travel satisfaction between passengers with private cars and those without private cars (p > 0.05). Older passengers report more satisfaction than younger passengers. Additionally, for passengers with high education and high income, satisfaction is lower in terms of safety, convenience, and comfort and caring. There are also significant differences in the safety, convenience, comfort, and caring experienced across different occupational groups. These research results provide a theoretical basis for understanding how passengers with different backgrounds perceive the operational services of an urban rail transit with regard to service defects, the weaknesses in the operation process, and passenger satisfaction.

Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19.