Passenger Flow Pushing Assignment Method for an Urban Rail Network Based on Hierarchical Path and Line Decomposition

Abstract

: For urban rail transit, an environmentally-friendly transportation mode, reasonable passenger flow assignment is the basis of train planning and passenger control, which is conducive to the sustainability of finance, operation and production. With the continuous expansion of the scale of urban rail networks, passenger travel path decision-making tends to be complex, which puts forward higher requirements of networked transportation organization. Based on undirected graphs and the idea of the recursive divide-and-conquer algorithm, this paper proposes a hierarchical effective path search method made up of a three-layer path generation strategy, which consists of deep search line paths, key station paths composed of origin–destination (O-D) nodes and transfer stations, and the station sequence path between the key stations. It can effectively simplify the path search and eliminate obvious unreasonable paths. Comparing the existing research results based on the classical polynomial Logit model, a practical Improved C-Logit multi-path passenger flow assignment model is proposed to calculate the selection ratio of each path in the set of effective paths. Combining the hierarchical path search strategy, the O-D pairs of passenger flow are divided into local-line and cross-line situations. The time-varying cross-line passenger flow is decomposed into a series of passenger sections along the key station paths. A passenger flow pushing assignment algorithm based on line decomposition is designed, which satisfies the dynamic, time-varying and continuous characteristics. The validation of Guangzhou Metro’s actual line network and time-varying O-D passenger demand in 2019 shows that the spatio-temporal distribution results of the passenger pushing assignment have a high degree of coincidence with the actual statistical data.

Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan

“…According to the results obtained from the assignment of the passenger fow of the URT network [5], sections of Line 2 during peak hours (when surging trafc volumes are mainly caused by commuters in the morning and evening) have the following characteristics: (1) the passenger fow is obviously centripetal, (2) the peak values of passenger fow between the morning and evening periods are close and the corresponding sections are the same; (3) the distribution trends of the passenger fow in the morning peak and evening peak in both the up and down direction are similar. Maximum cross-sectional passenger fow in the morning peak is slightly larger than the amount in the evening peak, which shows a certain symmetry, as shown in Figure 6.…”

“…It focuses on a single URT line and an operational organization scheme that is consistent with most URT systems in China, including fxed vehicle size, invariant train routing, and an all-stop mode. According to the riding O-D (origin-destination) of the line obtained by the passenger fow pushing the assignment method [5], a multiobjective nonlinear integer programming model is constructed, with the optimization goal of minimizing the passenger's generalized travel cost, total travel mileage of trains, and fuctuation of trains for each routing in each period. By comprehensively considering the constraints of both the maximum and minimum headways and fully loaded rates, the TOP and the dynamic train stopping time in each period are collaboratively optimized.…”

Modeling urban rail transit route choice with comprehensive travel cost

“…Narayan J et al propose a combined passenger route choice model for fixed and flexible public transport services, and applies it to the network of Amsterdam using agent-based simulation [4]. Deng et al introduce a passenger flow pushing assignment method based on line decomposition and hierarchical path search strategy, which can capture the dynamic, time-varying, and continuous characteristics of urban rail transit passenger flow [5]. Aiming at the problem of coordinated passenger flow control, Meng et al use a spatio-temporal network representation based on the timetable.…”

Research on passenger flow distribution under dynamic passenger flow demand

“…At present, the research on the passenger flow distribution of urban rail transit is mainly divided into the research on the passenger flow allocation model and the research on the passenger flow allocation solution algorithm. For the research on the passenger flow distribution model, Deng Lianbo et al [1] proposed a practical improved C-Logit multi-path passenger flow distribution model in combination with the hierarchical route search strategy in terms of the impact of the route on urban rail transit, which is used to calculate efficient the selection rate of each route in the route set makes a significant contribution to the efficient route search process in the passenger flow allocation process. Sun Luanying, Wang Jing et al [2] introduced the calculation method of passenger travel impedance and the random loading of the network based on the selection of a model suitable for the study of urban rail transit passenger flow distribution, considering the factors that affect the travel route selection behavior of urban rail transit passengers.…”