Modeling and Comparative Analysis of Urban Rail Transit Passenger Flow Forecasting

With the progression of smart urbanization, passenger flow prediction has emerged as an in- 1 tegral component of urban rail transit. To comprehensively characterize the essence of passenger flow prediction in urban rail transit, this paper focuses on modeling short-term prediction issues. Firstly, mathematical definitions are given for line network and station passenger flow, Origin-Destination (OD) passenger flow, and sectional passenger flow. Based on this foundation, a comprehensive mathematical modeling of short-term passenger flow prediction is conducted. Finally, this paper 6 summarizes the advantages, disadvantages, and conducts a comparative analysis of methods for short-term passenger flow prediction. Through mathematical modeling and comparative analysis of methods, this paper presents a comprehensive modeling framework for short-term passenger flow prediction. It decomposes the problem into more specific components, facilitating a profound understanding of its complexity and offering clear directions for diverse research aspects.With the continuous expansion of subway construction and the rapid development of intercity railways in urban areas, the subway network is becoming increasingly complex and the operating entities are becoming increasingly diverse. It is becoming increasingly urgent to establish an objective, fair, and reasonable ticketing system. The core issue is the allocation of travel routes under various transfer modes. This paper propose a rail transit network model based on station, route, network topology, and line vehicle dispatch information, which includes in-station and out-station travel time, transfer travel time, waiting time, and driving time. Furthermore, the influencing factors of each part of the travel time were analyzed, and theoretical analysis and simulation experiments revealed the impact of the randomness of travel time on passenger travel time under the constraint of exact vehicle entry and exit times.