In order to reduce passenger delays and prevent severe overcrowding in the subway system, it is necessary to accurately predict the short-term passenger flow during special events. However, few studies have been conducted to predict the subway passenger flow under these conditions. Traditional methods, such as the autoregressive integrated moving average (ARIMA) model, were commonly used to analyze regular traffic demands. These methods usually neglected the volatility (heteroscedasticity) in passenger flow influenced by unexpected external factors. This paper, therefore, proposed a generic framework to analyze short-term passenger flow, considering the dynamic volatility and nonlinearity of passenger flow during special events. Four different generalized autoregressive conditional heteroscedasticity models, along with the ARIMA model, were used to model the mean and volatility of passenger flow based on the transit smart card data from two stations near the Olympic Sports Center, Nanjing, China. Multiple statistical methods were applied to evaluate the performance of the hybrid models. The results indicate that the volatility of passenger flow had significant nonlinear and asymmetric features during special events. The proposed framework could effectively capture the mean and volatility of passenger flow, and outperform the traditional methods in terms of accuracy and reliability. Overall, this paper can help transit agencies to better understand the deterministic and stochastic changes of the passenger flow, and implement precautionary countermeasures for large crowds in a subway station before and after special events.

Complex movement patterns of pedestrian traffic, ranging from unidirectional to multidirectional flows, are frequently observed in major public infrastructure such as transport hubs. These multidirectional movements can result in increased number of conflicts, thereby influencing the mobility and safety of pedestrian facilities. Therefore, empirical data collection on pedestrians’ complex movement has been on the rise in the past two decades. Although there are several reviews of mathematical simulation models for pedestrian traffic in the existing literature, a detailed review examining the challenges and opportunities on empirical studies on the pedestrians complex movements is limited in the literature. The overall aim of this study is to present a systematic review on the empirical data collection for uni- and multidirectional crowd complex movements. We first categorized the complex movements of pedestrian crowd into two general categories, namely, external governed movements and internal driven movements based on the interactions with the infrastructure and among pedestrians, respectively. Further, considering the hierarchy of movement complexity, we decomposed the externally governed movements of pedestrian traffic into several unique movement patterns including straight line, turning, egress and ingress, opposing, weaving, merging, diverging, and random flows. Analysis of the literature showed that empirical data were highly rich in straight line and egress flow while medium rich in turning, merging, weaving, and opposing flows, but poor in ingress, diverging, and random flows. We put emphasis on the need for the future global collaborative efforts on data sharing for the complex crowd movements.

Data missing phenomena and random anomalies are ubiquitous in intelligent transportation systems (ITS), resulting in poor data quality and usability, which is a major impediment to real-world ITS applications. Most studies regarding traffic data recovery either assume that the original data are clean or complete, while such two issues often coexist in reality due to inevitable data measurement errors like detector malfunctions. In this paper, we fully exploit the algebraically low-rank property of traffic spatiotemporal data and develop an innovative tensor completion approach (termed SCPN) based on the tensor Schatten capped $ p $ norm, a unified representation of tensor norms with a high flexibility. Furthermore, we extend the proposed method to a robust form (termed RSCPN) by leveraging the sparsity of unstructured outliers, with the aim to reconstruct ground-truth values from corrupted and incomplete observations. Finally, associated optimization solutions based on the alternating direction multiplier method are derived. Extensive experiments on four datasets substantiate the significant superiority of our proposed models over other state-of-the-art methods on both missing data imputation and corrupted data recovery tasks with miscellaneous simulated scenarios.

In metro train systems, energy-saving operations can include timetable optimization and speed control. Timetable optimization aims to promote the effective utilization of regenerative energy, and speed control aims to minimize the tractive energy consumption. To effectively reduce the net energy consumption (i.e., the difference between tractive energy consumption and regenerative energy utilization), this paper introduces an integrated optimization model that incorporates timetable and speed profile optimization. A cataclysmic genetic algorithm was developed to solve the proposed optimization model. Data were collected from the No. 2 metro line in Nanjing, China; the results showed that the proposed model could reduce the practical energy consumption by 17.5% in comparison with the original timetable. In addition, compared with the cooperative scheduling model that optimized the timetable and speed separately, the proposed model reduced the practical energy consumption by 8.9% on average. Finally, sensitivity analyses were also conducted to investigate the effects of the number of stations on the energy-saving operations.