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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]

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities. • A Potential to Kinetic Energy (PKE) framework is introduced to investigate the relationship between green space attractiveness and urban movements. • Potential energy contains five sub-concepts: size, culture, attraction, proximity and location. • Kinetic energy represents urban movement that characterizes the motion of objects resulting from potential energy conversion. • Empirical analysis of Beijing shows the significant role of relative green space attractiveness in shaping urban movement patterns. • The PKE framework supports urban planners to evaluate green space attractiveness, examine the impact of landscape change, and promote inclusive urban environments. [ABSTRACT FROM AUTHOR]

• A two-population 3-strategy evolutionary game model for subway network operation extension. • Theoretical analysis and numerical simulation of Evolutionary Equilibria for the replicator dynamics system. • The best long-term strategies of networks and key factors (motivations) analysis. • An extended game model for investigating the impact of adjustment minutes. With the growing demand for night travel and the development of the night-time economy, extending the operation time of metropolitan subway networks has become essential. This paper proposes a 2-population 3-strategy evolutionary game model for subway network operation extension decisions, which considers the interests of subway companies, passengers, and the government while addressing transfer failure between lines due to uncoordinated subway operation time adjustments. The study resolves the Evolutionary Equilibria (EEs) of the replicator dynamics system and identifies key factors influencing the evolutionary path using theoretical calculations and a ternary phase diagram. Additionally, an extended game model investigates the impact of adjustment minutes. To verify the models, a case study based on the Beijing subway network is conducted. We show that the rule of maximum total fitness ensures the priority of EE strategies for all lines, selecting Nash equilibrium strategies for the lines which cannot select EE in a vastly reduced network strategy profile space (from 729 to 2). The study concludes that combining subsidy measures can eliminate multiple equilibria produced by implementing only penalty measures, and combining penalty measures can have the effect of making small subsidies as effective as large subsidies. The variation of the satisfaction/loyalty affects the system equilibrium only when both game players value passenger satisfaction/loyalty. Finally, we find that the proper adjustment minutes not only can make operation extension a long-term best strategy but also increase the payoff (106,158 RMB/year) and enhance the occupancy rate (20 %) of the last train. The study provides a theoretical foundation for the government to regulate the operation extension of subway companies from multiple perspectives. [ABSTRACT FROM AUTHOR]