Operations, management and planning of urban transit systems have evolved substantially since the application of transit data collection technologies, such as, automated fare collection (AFC), Global Position System (GPS), smartphones and face identification. A diversity of detailed sensor data in urban transit systems are being used as fundamental data sources to observe passenger travel behavior, reschedule operation plans and adjust policy decisions from the daily operations to the long-term network planning. This review aims to summarize and analyze those related challenges and data-driven applications. Firstly, we review the data collecting technologies since the late 1990s by classifying the various technologies into two groups: traditional technologies and advanced technologies. A vast body of literature has been developed in this area given the wide range of problems addressed under the transit data label. A summary diagram is proposed to demonstrate the transit data applications and research topics. The data applications are classified into three branches: passenger behavior, operation optimization, and policy application. For each branch, the hot research direction and dimension shown as sub-branches are represented by reviewing the highly cited and the latest literature. As a result, this article discussed the concept and characteristics of transit data and its collection technologies, and further summarized the methodology and potential for each transit data application and suggested a few promising implications for future efforts.

Urban transit system is an important part of city transportation, which is an interdisciplinary industry, including traffic engineering, operation research, and computer science. To provide smart services for passengers while applying the new technologies, it is necessary to build an optimal transit network and transit service. A smart transit system is processed from strategic planning, tactical planning, operational planning, transit evaluation to marketing and policy. For each stage, large quantities of related literature have been introduced from different perspectives. The aim of this research is to document the main smart urban transit models, topics and implementations for future references and research in each stage. For the planning part, this paper first summarized the objectives, constraints, algorithms, and implications of the models currently in use and classified the objectives and constraints with classic category and new category. The prominent topics and potential research were captured clearly when comparing the two categories. The methodologies for solving those models were proposed and the genetic algorithm and simulated annealing have been mostly used, which will be helpful for filling the gaps for further research. Despite of the model updates, this study also summarized the application trends such as integrated network design in strategic planning, synchronization and timetable recovery from disruption in tactical and operational planning. To improve the transit system and service, evaluation models on service reliability, service accessibility, timetable robustness, and energy consuming are proposed, which highlight the gap between the idealized service and the real service. Some flexible fare scheme, investments, and commercial strategies are discussed in the financial part. The conclusion highlighted the future scope of the smart urban transit in passenger demand management, travel information service, facility and service optimization and shared mobility, in order to make it more convenient for the passengers and more friendly to the environment.

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.

Given the security problems of the urban rail transportation, this paper proposes a methodology for congestion risk evaluation and precaution in order to guarantee the safety of passengers and improve the operation efficiency in metro stations. To begin with, we selected the retention rate of the platform, the waiting rate of the ticket gate and the transfer efficiency as the indexes for the station congestion precaution system and created models to calculate the threshold respectively. By reasonably combining these three indexes, a congestion warning level function was set up to evaluate the congestion risk and determine the warning level. The characteristics of the proposed model are its quick decision results and ease of handling. With the determined warning level, some strategies are suggested to relieve the pressure. Finally, a practical application in Beijing’s GuoMao metro station was performed to verify the feasibility and practicability of the proposed congestion precaution model.

Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k‐means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in‐vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.

Optimal strategy, one of the main transit assignment models, can better demonstrate the flexibility for passengers using routes in a transit network. According to the basic optimal strategy model, passengers can board trains based on their frequency without any capacity limitation. In the metropolitan cities such as Beijing, Shanghai, and Hong Kong, morning commuters face huge transit problems. Especially for the metro system, there is heavy rush in metro stations. Owing to the limited train capacity, some passengers cannot board the first coming train and need to wait for the next one. To better demonstrate the behavior of passengers pertaining to the limited train capacity, we consider capacity constraints for the basic optimal strategy model to represent the real situation. We have proposed a simulation‐based algorithm to solve the model and apply it to the Beijing Subway to demonstrate the feasibility of the model. The application of the proposed approach has been demonstrated using the computational results for transit networks originating from practice.