The urban rail transit (URT) system in China has undergone development spanning over 50 years. In the period from 2008 to 2015, numerous URT lines were under construction. After 2015, an increasing number of cities have transitioned to multi-line network operations, with greater emphasis on system efficiency and passenger service. This transition has been accompanied by numerous successful innovations and applications aimed at enhancing system intelligence and automation. This paper provides a review of operational statistics based on annual reports, successful operational practices, and industry development characteristics over the past decade in mainland China. Additionally, suggestions and trends for the further development of URT in China are proposed.

In response to the global trend of urbanization, there has been an increasing focus on transit-oriented development (TOD). However, the prioritization of economic factors in the establishment of TOD often takes precedence over concerns for social equity. This research seeks to address this gap by examining the economic performance and demographic characteristics of 46 rail transit station areas (RSAs) in the city center of Dalian. The study employs the Gini coefficient and affinity clustering to assess the overall economic performance and inequality among different resident groups within RSAs. Furthermore, regression analysis is utilized to identify the key variables influencing economic performance equity in these areas. The results indicate significant disparities in economic dimensions among different resident groups, with station areas in commercial centers and functional core zones demonstrating higher economic performance. Housing prices and job–housing density are identified as crucial factors influencing consumer behavior across various station areas. Despite the presence of employment opportunities and urban development features in RSAs, differences in socioeconomic status and accessibility to public facilities significantly impact resident social equity. These results can assist policymakers in evaluating disparities in the allocation of RSAs among different regions and demographic groups. This study adds to the existing knowledge on equity in the economic performance of RSAs and supports the development of inclusive TOD strategies specific to different locations and populations.

As the scale of development continues to expand, carbon emissions of urban rail transit (URT) in China have gradually increased each year, posing certain pressure on the carbon emission reduction efforts in the URT industry. To evaluate the current status of low-carbon development in the URT industry, this paper first constructs an annual carbon emission accounting model for URT that considers both the construction and operation processes. Based on this model, the annual carbon emissions of URT in China from 2015 to 2023 are calculated. Furthermore, based on decoupling theory, the relationship between carbon emissions and industry development in URT is explored from both the construction and operation aspects. The results show that: (1) From 2015 to 2023, the total carbon emissions of URT in China increased significantly from 14.425 million tons to 24.7072 million tons. The proportion of carbon emissions from construction is higher during the study period, but the growth rate of carbon emissions from operation is faster. (2) The order of carbon emissions from URT in different regions from largest to smallest is: East > West > Central > Northeast. (3) During the study period, the carbon emissions from URT construction in China were weakly decoupled from the operational lines. (4) There was an expansive coupling relationship between carbon emissions from URT operation and passenger transport turnover in China. These findings can provide some guidance for the sustainable development of the URT industry.

Enhancing the resilience of train timetables can effectively improve their resistance and recovery capabilities against operational disturbances, thereby ensuring the stable operation of the railway system and the quality of passenger transportation services. This paper defined timetable resilience as three parts: disturbance absorptive capacity, resistance capacity, and post-disturbance recovery capacity. These capacities were quantitatively evaluated using the buffer time of trains, the number of train delays at departures and arrivals, and the duration of delay state. The evaluation metrics of the three resilience capacities and the total travel time of trains were used as the objective to establish a spatiotemporal network model. Utilizing actual operational data from the Beijing–Shanghai high-speed railway in China, the model was validated through a case study. Sensitivity analysis of the model's key parameters was conducted under two experimental scenarios: routine operational disturbances and speed restrictions on specific sections. Results showed that our model can effectively reduce the delay time and the number of delays in both the routine operational disturbance scenario and the 300 km/h speed restriction scenario with a frequent disturbance value of 2 min. Moreover, the model's performance with 3-min frequent disturbance outperformed that with 2-min frequent disturbance in speed restriction at 250 km/h and 200 km/h, yielding a 30% improvement.

Considering the unique interplay of trams with road traffic, this study explored the issue of instability in tram operations—a prominent medium-capacity rail transit. Our goal was to design a timetable slack time optimization method for scheduling slack time to improve the stability of tram operations. To facilitate this, we derived the travel/dwelling time distribution from historical data, which assisted in estimating interference times and evaluating the requisite slack time. We then developed an integer programming model to calculate both the punctuality rate and expected delay under varying travel times, enabling the creation of alternative slack time schemes. Using a unique tram operation simulation logic, we assessed the operational efficiency and reliability of these alternate schemes based on specific operational indicators. The results suggest that our novel approach to timetable optimization significantly enhances the tram’s adaptability to disruptions, directly improving the passenger experience and tram competitiveness. This work offers a robust framework for timetable optimization for semi-independent right-of-way public transportation.

When no more than one train is feasibly contained in the separation headway times of two other trains, a triangular gap problem-based method is used to compute the consumed capacity in linear time. This is a strong condition limiting its applicability, while real-world operations often feature mixed traffic of various speeds, creating more complex structures beyond the characterization of a triangular gap. In this paper, we attempt to investigate the multi-train gap scenario and provide a general solution to the railway timetable structure and capacity analysis problem. Given a timetable, we use an incidence graph, the so-called train contraction minor, for representing the consecutive train operations and show that a longest path for spanning the compressed timetable is any path in a graph induced by some topological subsequence of trains that connects the predefined vertices in the train contraction minor. By vector-valued vertex labeling of this minor, we acquire an efficient algorithm that computes the consumed capacity of the timetable. Our algorithm runs in O(mn) time, where m and n are the number of trains and stations, respectively, and is free from the limitation and outperforms the near-linear time policy iteration implementation in the max-plus system of train operations. A toy example and a real-world case study demonstrate the effectiveness and computational performance of the proposed method. The proposed method based on train contraction minor contributes to the railway operations community by promoting the efficient computation of railway line capacity into linear time.