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With the fast development of metro systems in many big cities, it is important to study the characteristics of passenger flows based on metro data for the management to guarantee service quality and safety. In this article, we build statistical models for the data of passengers' tap-in and tap-out times in both no-transfer and one-transfer cases, and propose a Bayesian approach to estimate parameters in the models. These estimators can be used to evaluate a number of measures, which describe degrees of congestion and comfort, and to quantify their uncertainties. Application of our approach to Beijing metro shows different passengers follow different patterns between different routes and between off-peak and peak hours. [ABSTRACT FROM AUTHOR]

With the rapid development of metro systems, it has become increasingly important to study phenomena such as passenger flow distribution and passenger boarding behavior. It is difficult for existing methods to accurately describe actual situations and to extend to the whole metro system due to the limitations from parameter uncertainties in their mathematical models. In this article, we propose a passenger‐to‐train assignment model to evaluate the probabilities of individual passengers boarding each feasible train for both no‐transfer and one‐transfer situations. This model can be used to understand passenger flows and crowdedness. The input parameters of the model include the probabilities that the passengers take each train and the probability distribution of egress time, which is the time to walk to the tap‐out fare gate after alighting from the train. We present the likelihood method to estimate these parameters based on data from the automatic fare collection and automatic vehicle location systems. This method can construct several nonparametric density estimates without assuming the parametric form of the distribution of egress time. The EM algorithm is used to compute the maximum likelihood estimates. Simulation results indicate that the proposed estimates perform well. By applying our method to real data in Beijing metro system, we can identify different passenger flow patterns between peak and off‐peak hours. [ABSTRACT FROM AUTHOR]

Traffic state evaluation is a basic and critical work in the research on road traffic congestion. It can provide basic data support for the improvement measures and information release in traffic management and service. The aim of this research is to obtain a comprehensive value to describe traffic state accurately based on the evaluation index system. In this paper, it is carried out using fuzzy c-means (FCM) algorithm and fuzzy entropy weight method. In the framework, traffic flow was classified into six different states to determine the fuzzy range of indices using the improved FCM clustering analysis. Besides, fuzzy entropy weight method is proposed to compute the evaluation result of traffic state for section, road and road network, respectively. The experiments based on the traffic information in a subset of Beijing's road network prove that the findings of traffic evaluation are in accordance with the actual situation and people's sense of traffic state. [ABSTRACT FROM AUTHOR]

With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]