Accurate and dynamic estimation of Origin-Destination (OD) passenger flow is essential for rail transit agencies to efficiently monitor network status and promptly adjust transportation organization strategies effectively. However, in large-scale urban rail transit networks, the OD matrix typically exhibits both high dimensionality and sparsity, which renders accurate short-term estimation of the global OD matrix via deep learning-based methods extremely challenging. This paper establishes a matrix-form state space model for OD estimation by considering the dynamic conservation relationship of passenger flow. To avoid the high dimensionality and sparsity of data, an improved particle filter (IPF) algorithm, incorporating historical data augmentation and importance pre-sampling, is proposed to construct a dynamic short-term OD estimation model that accounts for both real-time and historical trends. The model is implemented with the automated fare collection (AFC) data from Nanjing rail transit, China, encompassing 159 stations. Results indicate that the IPF-based dynamic OD passenger flow estimation model outperforms benchmark models, including the historical value estimation method (HIS) and the Kalman filter (KF), as evaluated by three error indicators: root means square error (RMSE), weighted absolute percentage error (WAPE), quantile absolute error (QAE). The model demonstrates excellent prediction accuracy and operational performance, making it a practical and effective model for estimating dynamic OD passenger flow in large-scale urban rail transit systems.

In the present scenario of the ‘one-tapin-one-tapout’ operation mode in urban rail transportation, determining passenger travel trajectories is challenging, thereby decreasing the effective counting of passengers on trains. In order to address this issue, a method for calculating passenger capacity on trains was developed by analyzing passenger travel information extracted from AFC data and train timetables. First, this research estimated the components of journey time to infer and reconstruct the passenger's specific spatiotemporal travel trajectory. The passenger-train matching model was then used to assign passengers to specific trains and calculate the passenger load of different trains at each station. At last, it took Suzhou Metro Line 3 as an example and analyzed the spatiotemporal distribution of urban rail transit passenger flow on a single line based on the proposed passenger-train model.

To address the collaborative issue in large-scale urban rail transit (URT) network operations, this paper proposes an adaptive real-time control framework based on the Soft Actor-Critic (SAC) deep reinforcement learning (DRL) method, featuring flexible train scheduling capabilities. First, by analyzing dynamic passenger travel behavior (e.g., entering/exiting stations, transferring) and train operation events (e.g., dispatching, interstation running, station dwelling), the control problem is modeled as a Markov Decision Process (MDP) and an efficient URT simulation environment is constructed. Then, considering constraints such as train capacity and dispatch intervals, a train scheduling model is developed to minimize both passenger costs and operational costs. Subsequently, the real-time state of the URT system is represented by the overall number of passengers present at every platform, and train dispatch intervals on all lines are used as decision variables. A solving algorithm based on the SAC framework is developed. Finally, experimental results on a large-scale URT network comprising 10 lines demonstrate the effectiveness of the proposed framework, showing superior performance compared to other reinforcement learning algorithms and traditional heuristic optimization algorithms. The proposed approach achieves a 1.63% reduction in average passenger waiting time, equivalent to 2.09 seconds, while utilizing 49 fewer trains, representing a 2.97% decrease, compared to the second-best TD3 algorithm.

Due to the dynamic changes in timetables, passenger demand, and passenger composition, the distribution of passengers within a metro system becomes quite complex. Many studies divide a day into intervals to account for the dynamics of travel time. However, the intervals used in these studies are insufficient to capture the gradual and fine-grained changes in passenger travel patterns. This study proposes an adaptive dynamic route inference model (ADRIM) that overcomes these limitations. In the ADRIM, we introduce a constrained Expectation Maximization algorithm (CEM) by confining the parameters of the mixture log-normal distribution model (MLND) within confidence intervals, thereby reducing anomalous estimations. We use the concept of Hidden Markov Models (HMMs) to achieve a parameter-adaptive characterization for the dynamics of route choice and travel time distributions for MLND through an iterative process. For a Nanjing metro case study, the proposed model exhibits superior performance in fitting the actual distribution of travel times and accurately captures the dynamic trends in route travel times. Besides, it is revealed that the maximum difference in expected travel times among multiple valid routes for the same origin-destination (OD) pair primarily falls within the interval [5 min, 15 min], and the distribution range of the maximum ratio is mainly between [1.1, 1.6]. The high consistency in passenger route choice proportions observed for two consecutive weeks, along with an analysis of route choice patterns under dynamic conditions, serves as strong evidence supporting the reliability and practical utility of the dynamic route inference model in understanding and managing metro passenger flows.

This study proposes an urban rail transit network resilience assessment method based on dynamic passenger flow, which quantifies the overall system performance from the structural and functional dimensions. At the structural level, the relative size of the largest pass subgraph is introduced to measure the network integrity, and the average node degree is used to evaluate the network connectivity; At the functional level, the passenger travel efficiency ratio is used to measure the operation efficiency of the supply side, and the proportion of unaffected passengers is used to evaluate the service support capability of the demand side. The weight of each index is determined by entropy weight method, and then the comprehensive performance evaluation model of rail transit system is constructed. Taking Nanjing Metro as an example, the empirical study shows that the performance change trend reflected by the introduction of dynamic passenger flow is significantly different from the evaluation results based on structural topology only, and the decline and recovery process of network performance after disturbance is closer to the actual operation. This study provides a theoretical basis for quantifying the resilience of rail transit network, and provides a reference for improving the system resilience and formulating optimization strategies.

In the context of Ultra-Reliable Low Latency Communication (URLLC) scenarios, 5G incorporates numerous enhancements, with link adaptation (LA) being one of them. In the pursuit of reliability, a measurement-prediction-decision approach can be considered to enhance the accuracy of Modulation and Coding Scheme (MCS) decisions during LA, specifically by forecasting interference. In this paper, a two-stage uplink interference prediction algorithm is proposed. In the first stage, complex uplink interference values are decomposed to extract inherent patterns. In the second stage, leveraging the prior knowledge provided by the first stage, which enhances the algorithm's robustness and accuracy, inference is made. The experimental results demonstrate that the proposed interference prediction algorithm not only exhibits a significant improvement in accuracy but also contributes to a substantial enhancement in the performance of the communication system.

Objective To address the compatible access issue of other types of citizen card and ticket in ticketing system during urban rail transit operation, a unified planning on the compatible access of citizen cards within Suzhou pan-urban area is required to form a comprehensive solution for the compatible access. Method Based on the current citizen card accessibility situation in Suzhou urban rail transit ticketing system, the access service of Suzhou citizen cards is sorted, and the requirements and work content of citizen cards compatible access are analyzed. The implementation difficulties and reasonable solution ideas for citizen card accessibility are analyzed from aspects including preferential policies, technical standards, ticket card structure, and three compatible access solutions are proposed based on transaction settlement methods: indirect access, direct access, and mixed access. Solutions are compared and selected regarding economy, security, and convenience, and suggestions for the specific implementation of compatible access is proposed from two aspects of standards formulation and parameterization configuration. Result & Conclusion The research and solution comparison results show that the citizen card compatible access in ticketing system involves many difficulties, requiring coordination and resolution by multiple parties; the mixed access solution takes into account both the renovation workload and the rights and interests of each accessing party, displaying strong flexibility and better applicability; developing compatible access standards for citizen cards can effectively reduce the difficulties and cost of access.

The paper proposes an urban rail transit equipment maintenance mode decision method (EMMDM). The framework takes the reliability-centered maintenance (RCM) into consideration to make up for the problem that over-maintenance and under-maintenance of urban rail transit equipment for the decision-making model. To improve the application process of the RCM, the analysis of the composition and critical equipment of urban rail transit equipment is of vital importance. Failure rate, failure detectability, and failure consequence are selected as the equipment risk evaluation indexes. The cycle of optimal maintenance mode is given by the maintenance mode choice model based on the failure risk. Taking automatic ticket gate machine (AGM) and environmental monitoring equipment as examples, the results show cost savings of 8% and 2%, respectively, supporting the optimization of maintenance mode decision.

考虑到地铁自动检票机组成复杂且故障形式多样,对其进行故障分析并以二参数威布尔分布为基础,提出一种基于混合威布尔分布的设备可靠性评估模型.为提高模型拟合精度,基于误差平方和最小思想构建非线性最小二乘参数优化估计模型并使用粒子群算法(PSO)进行最优参数求解.以南京地铁油坊桥车站自动检票机实际故障数据为例,进行实例验证.结果表明,基于PSO算法的混合威布尔分布可靠性评估模型优于传统单威布尔分布,其均方根误差、平均绝对百分比误差、皮尔逊相关系数均为最优.

This paper presents a ticket clearing method based on estimating passenger route selection probabilities. The method assumes that the number of trains waited by passengers at the origin and transfer stations follows a polynomial distribution. By applying maximum likelihood estimation, the probability of waiting for trains at the origin and transfer stations can be obtained, allowing the inference of the probability of routes to be selected. Synthetic data are used to validate the model, showing an accuracy of 90% and indicating its ability to effectively match the actual probability of the route being selected. By using this method, passenger flow on each line can be further estimated, providing better insight into the distribution of passenger flow within the network. The proposed model allows a more detailed analysis of network passenger flow, facilitating more accurate ticket clearing.

This paper proposes a smart security check system of urban rail transit based on the current demand for developing security checks considering the inefficient efficiency, mismatched mode, and poor performance of security checks. This paper analyzes the crucial technologies, which include differentiated security check mode, process and facility optimization, and security and ticket check integration. A simulation model of Tianruncheng Station of Nanjing Metro is established to verify the effectiveness of the key technologies. The proposed method provides a theoretical basis for developing a smart security check system.

With the expansion of metro network, the large passenger flow in peak hours has brought great challenges to metro operation. While upgrading the security inspection, the traffic efficiency of passengers in metro stations has also been affected. Considering public security and security inspection efficiency, using the method of passenger credit rating to establish a differentiated security inspection mode has become the development direction of intelligent security inspection in the future. It is necessary to study the passenger credit evaluation system due to a lack of research on metro passenger credit scoring. However, previous research focused on static credit evaluation rather than dynamic credit evaluation. This paper introduces motivation factor, time weight and night safety reduction coefficient to conduct dynamic passenger credit evaluation.

Urban rail transit is in the stage of rapid development in China. Many cities planned and are building new rail transit lines. The access to new urban rail transit lines will affect the distribution of passenger flow and passenger travel time in the existing urban rail network. This paper takes Nanjing Metro Line 4, which opened in 2017, as an example to study the influence of new line access operation on passenger flow distribution, and passenger travel time of the urban rail system. Firstly, the influence of new line access on passenger flow distribution and passenger flow in short-term operation is analyzed. Then, the influence of new line access network on passenger travel time and its reliability after short-term and long-term operation reaches the stable stage is analyzed, and the rule is summarized. The research results show that access to the new line will enhance the total passenger flow of the subway system and ease the congestion of crowded stations. It can also improve the travel time reliability to CBD during peak hours, reduce the travel time reliability to CBD during non-peak hours, and reach a stable stage two years after the new line accesses the network.

There are few studies on the full-cycle operation status evaluation of mechanical and electrical equipment in urban rail transit, but the status evaluation is the prerequisite for intelligent operation and maintenance. This paper proposes a state assessment method for urban rail platform screen door (PSD) system based on machine learning and improved D-S evidence theory. First, an open identification framework for health assessment is structured by K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), and then the status category data is used for decision-making and final fusion through the improved D-S evidence theory, to obtain the health status of the PSD system. At the same time, the degradation state of the PSD system is mined with the help of Multilayer Perceptron (MLP). Finally, the mined equipment degradation state is combined with the equipment health state to output the equipment status evaluation results.

Building a new integration mode between security check and ticketing is a technical development trend of automatic fare collection (AFC) in the intelligent metro transportation systems, while the key link to achieve this integration is to establish a scientific passenger credit system. Referring to the payment credit indicator system in the field of commercial and business, this paper analyzes the influencing factors of passenger security credit from the aspects of passengers’ natural information, family situation, economic condition, and behavior performance based on the theory of behavioral science to establish the metro security credit indicator system. The analytic hierarchy process (AHP) method is applied to allocate indicator weights, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is utilized to rank passenger credits. The proposed framework is helpful to provide method guidance for passenger credit management of metro.

Lane line detection is one of the important tasks of the environment perception system of autonomous vehicles, which must be very time sensitive and robust. To this end, this paper proposes a lane line detection implementation method based on the OpenCV platform, which can be applied to smart cars in specific places, mainly including image preprocessing and lane line detection and fitting. Applying morphological operations to the image preprocessing stage can effectively fill in the wear information of lane lines, and the least squares method is used to adjust lane lines after Hough transformation. The results show that the proposed method can improve the operation speed without affecting the accuracy of the algorithm, and has certain practicality.

针对轨道交通客流通行需求和安检能力不足形成的"量力"矛盾,提出一种城市轨道交通差异化安检方法,采集乘客实名认证信息并存入识别数据库,按实名认证乘客与普通乘客对乘客进行分流,实行差异化安检,其中乘客信用体系构建、身份验证、违禁品精准识别是关键技术.通过分析安检组成、通道布局、安检流程等探讨差异化安检具体实施措施.实名认证乘客执行快速安检,普通乘客执行常规安检,可以减少站厅滞留乘客,提高安检效率,缩短乘客安检排队时间,提高安检效率和安检服务水平.

精准的客流预测是轨道交通运输计划编制的基础和依据,为提高城市轨道交通短时客流的预测精准度,基于城市轨道交通短时客流的动态性、非线性、不确定性、周期性、非平稳性及时序性等特点,提出一种组合模型预测方法,即VMD-GRU神经网络预测模型,由变分模态分解和门控循环单元组合而成.变分模态分解的作用是分解短时客流,降低数据中的噪声,减少数据波动;门控循环单元的作用是基于分解的短时客流,进行客流预测.经南京地铁的数据验证,该模型在地铁短时客流预测方面效果良好.与GRU相比,VMD-GRU在15、30和60 min的时间粒度下,预测准确度分别提升7.57％,16.93％,18.47％.该模型可为地铁运营管理部门对车站客流管理、日常行车计划制定等提供有效的数据支撑,从而提升线网总体运营效率以及轨道交通系统的服务水平.