As the level of passenger demand in rail transit systems increases, major railway stations in urban centres face serious capacity issues. Both analytical and simulation methods have been used to analyse complex station areas; however, prior efforts have only focused on either train or pedestrian movements with over-simplified assumptions that do not properly capture the impact of their interaction on capacity. This study applies an integrated crowd and transit simulation platform “Nexus” to simultaneously study the impact of pedestrian and train movements on the system performance of a complex railway station. Unlike other methods such as sequential simulation methods, the integrated simulation platform permits linkage between commercial-grade simulators. Instead of treating each simulator separately, this integrated method enables detailed modelling of how the train and crowd dynamic interact at station platforms. Such integration aims to explore the interactive effect on both types of movement and enable performance analysis possible only through this combination. To validate the model, a case study is performed on Toronto’s Union Station. Extensive data were collected, processed and input into railway and pedestrian models constructed using OpenTrack and MassMotion, respectively, and integrated via Nexus. Examining scenarios of increased levels of train and passenger volumes, a 9% drop in on-time performance of train operation is observed, while the level of service experienced by passengers on the platform deteriorates significantly due to crowding. Both length and variation in dwell time due to pedestrian movement are recognized as the main factors of performance deterioration, especially when the system approaches capacity limit. The simulation model produces estimates of the practical track-side capacity of the station and associated platform crowding levels, and helps identify locations where passengers experience severe overcrowding, which are not easily obtainable from mathematical models.

Tram manufacturers have different ways of approaching the design of low-floor trams with compact and reliable running gears, and therefore several tram architectures can still be found. A complete standardization of trams is nearly impossible, and technical innovations can be more easily introduced if compared to conventional railway vehicles, but the trend towards large-scale standardization based on vehicle “platforms” can be seen in recent years. However, the current “standard” tram architecture, which includes only non-pivoting bogies, is not able to solve some typical problems of tram operations, such as high wheel and rail wear and high-pitched tonal noise (squeal) in sharp curves, which are described in the present paper. This research analyses the tram market with the aim of describing the state of the art of currently available products and comparing their main technical parameters. The analysis is based on information available from the literatures (journals, web) where data about the vehicles can be found, while a new designation code (tram architecture designation, TAD for short) is specifically introduced for easier identification of the different tram architectures. Even if the complete low floor is still one of the main requested features, several solutions combining pivoting and non-pivoting bogies are commercially available, showing a tendency to give more relevance to running quality performance with respect to the recent past.

Extreme weather events, such as typhoon and hurricane, have characteristics of high uncertainty, large destructiveness, and extensiveness, which threat the daily life and cause apparent perturbations to human mobility. In order to investigate the perturbation on human mobility, this study collects the metro transaction data before and during a typhoon weather event in Fuzhou, China, to conduct analyses. The ridership before and during the typhoon weather event is innovatively compared at system, station and origin-destination level. Besides, it is of novelty to examine the travel time distribution of metro trips in the normal and perturbed state by comparing three candidate models with the Akaike information criterion method. Results validate that the typhoon weather event severely influences the ridership at system, station, and origin-destination level, with various degrees. There is also significant impact on the relative total traveled stations from the typhoon weather event, especially for leisure trips. Moreover, the travel time of metro trips follows the gamma distribution in both the normal state and the perturbed state with different magnitudes. It is found that both the number of traveled stations and travel time are lower in the typhoon state when compared to those in the normal state. In general, this study can provide some helps to assist the metro management under extreme weather events.

Communication-based train control (CBTC) has been the prevailing technology of the urban transit signaling system. However, CBTC also faces a few issues to extend and maintain because of its complicated structure. This paper presents a novel urban transit signaling system architecture, software-defined train control (SDTC), which is based on cloud and high-speed wireless communication technology. The core functions of the proposed SDTC, including the onboard controller, are implemented in the cloud platform, with only sensors and input–output (IO) units remaining on the trackside and the train. Because of the scalable framework, the system function can be expanded according to the user’s demand, making signaling as a service possible. With warm standby server redundancy, SDTC has better reliability. Compared with the traditional CBTC architecture, the mean time between failures is improved by 39% by calculating typical project parameters by the Markov model based on some assumptions.

Making accurate predictions of subway passenger flow is conducive to optimizing operation plans. This study aims to analyze the regularity of subway passenger flow and combine the modeling skills of deep learning with transportation knowledge to predict the short-term subway passenger flow in the scenarios of workdays and holidays. The processed data were collected from two months of Automated Fare Collection (AFC) data from Xizhimen station of Beijing metro. The data were first cleaned by the established cleansing rules to delete malformed and abnormal logic data. The cleaned data were used to analyze the spatial characteristics in passenger flow. Second, a short-term subway passenger flow prediction model was built on the basis of long short-term memory (LSTM). Determining that the error will be relatively high in peak hours, we proposed gradual optimizations from data input by dividing one whole day into different time periods, and then used particle swarm optimization (PSO) to search for the optimal hyperparameters setting. Finally, inbound passenger flow of Beijing Xizhimen subway station in 2018 was selected for numerical experiments. Predictions of the LSTM-based model had higher accuracy than the traditional machine learning support vector regression (SVR) model, with mean absolute percentage error (MAPE) of 21.97% and 4.80% in the scenarios of workdays and holidays, respectively, which are both lower than those of the SVR model. The optimized PSO-LSTM model has been verified for its effectiveness and accurateness by the AFC data.

The understanding and management of station stops continues to be a key issue in the operation of urban railways. This paper reports a statistical meta-analysis of passenger alighting and boarding rates from an expansion of a real-life worldwide data set which includes 34 different variables reflecting characteristics of passenger flow, rolling stock design, infrastructure and management actions. This has enabled the authors to identify, test hypotheses about, and quantify the impact of, previously-untested variables. A stepwise regression method using the R statistical package was proposed and developed into a more tractable model with fewer variables. This process eliminated those variables shown to provide no statistical explanation (including the presence of platform edge doors). Of the remaining 18 hypothesised variables, all provided some form of statistical explanation at the 90% level (or more) in one model or another. The results will help railways and transport authorities around the world manage station stops, through timetabling and appropriate investment.