Model of Passenger Flow Assignment for Urban Rail Transit Based on Entry and Exit Time Constraints

Precise estimation of passenger spatial-temporal trajectory is the basis for urban rail transit (URT) passenger flow assignment and ticket fare clearing. Inspired by the correlation between passenger tap-in/out time and train schedules, we present a method to estimate URT passenger spatial-temporal trajectory. First, we classify passengers into four types according to the number of their routes and transfers. Subsequently, based on the characteristic that passengers tap-out in batches at each station, the K-means algorithm is used to assign passengers to trains. Then, we acquire passenger access, egress, and transfer time distribution, which are used to give a probability estimation of passenger trajectories. Finally, in a multi-route case of the Beijing Subway, this method presents an estimation result with 91.2% of the passengers choosing the same route in two consecutive days, and the difference of route choice ratio in these two days is 3.8%. Our method has high accuracy and provides a new method for passenger microcosmic behavior research.

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Research on the Modeling Methods of the Service Network Meeting the Requirements of Passenger Flow Assignment in Urban Rail Transit

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.779-780.815 ◽

2013 ◽

Vol 779-780 ◽

pp. 815-820

Author(s):

Jian Yu ◽

Xing Chen Zhang ◽

Bin Xu

Keyword(s):

Urban Rail Transit ◽

Service Networks ◽

Service Network ◽

Passenger Flow ◽

Construction Methods ◽

Network Meeting ◽

Urban Rail ◽

Flow Assignment ◽

Passenger Flow Assignment ◽

Network Type

The conceptual introduction of the service network and its function in passenger flow assignment are given. Based on the summary of the related research results, the service networks are divided into two classes as the matrix type and the network type according to the different construction methods. The modeling principles and methods of the network type service network are then proposed. The methods are explained in three aspects including node modeling, segment (or arc) modeling, and partial network modeling, which can, for a certain physical network and a certain line plan, construct the virtual service network that is required by the passenger flow assignment process. Corresponding modifications are required when using these methods in specific researches.

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

Sustainability ◽

10.3390/su11226441 ◽

2019 ◽

Vol 11 (22) ◽

pp. 6441

Author(s):

Deng ◽

Zeng ◽

Mei

Keyword(s):

Temporal Distribution ◽

Urban Rail Transit ◽

Divide And Conquer ◽

Time Varying ◽

Passenger Flow ◽

Urban Rail ◽

Flow Assignment ◽

Path Search ◽

Passenger Flow Assignment ◽

Transportation Organization

: 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.

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Construction and Application of Passenger Flow Simulation Evaluation Index System in Urban Rail Transit Transfer Stations

MATEC Web of Conferences ◽

10.1051/matecconf/20168104005 ◽

2016 ◽

Vol 81 ◽

pp. 04005

Author(s):

Xiaohong Fang ◽

Aijun Feng

Keyword(s):

Index System ◽

Flow Simulation ◽

Urban Rail Transit ◽

Evaluation Index System ◽

Evaluation Index ◽

Rail Transit ◽

Passenger Flow ◽

Simulation Evaluation ◽

Urban Rail ◽

Transfer Stations

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Passenger Flow Forecast of Urban Rail Transit Based on Support Vector Regression

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.612 ◽

2013 ◽

Vol 433-435 ◽

pp. 612-616 ◽

Cited By ~ 1

Author(s):

Bin Xia ◽

Fan Yu Kong ◽

Song Yuan Xie

Keyword(s):

Support Vector Regression ◽

Risk Function ◽

Urban Rail Transit ◽

Support Vector ◽

Rail Transit ◽

Risk Minimization ◽

Promising Alternative ◽

Minimization Principle ◽

Passenger Flow ◽

Urban Rail

This study analyses and compares several forecast methods of urban rail transit passenger flow, and indicates the necessity of forecasting short-term passenger flow. Support vector regression is a promising method for the forecast of passenger flow because it uses a risk function consisting of the empirical error and a regularized term which is based on the structural risk minimization principle. In this paper, the prediction model of urban rail transit passenger flow is constructed. Through the comparison with BP neural networks forecast methods, the experimental results show that applying this method in URT passenger flow forecasting is feasible and it provides a promising alternative to passenger flow prediction.

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