Simulation of Passenger Flows on Urban Rail Transit Platform based on Adaptive Agents

Because urban tail transit becomes the preferred way to travel for more travelers, the passenger flow of rail transit is increasing fast. Due to the increased passenger, the congestion on platform reduces the comfort and puts passengers in danger. This article analyses the model of island platform short-time arrivals based on the probability theory and historical statistics. The calculation method is studied and the Feasibility and algorithm is testified by setting numerical examples.

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Influence of Emergency Passenger Flow Distribution in Urban Rail Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.450-451.295 ◽

2012 ◽

Vol 450-451 ◽

pp. 295-301 ◽

Cited By ~ 1

Author(s):

Ling Hong ◽

Jia Gao ◽

Rui Hua Xu

Keyword(s):

Flow Distribution ◽

Urban Rail Transit ◽

Rail Transit ◽

Abstract Model ◽

Flow Volume ◽

Rail Network ◽

Passenger Flow ◽

Urban Rail ◽

Passenger Flows ◽

Urban Rail Transit Network

The emergency disposal of urban rail transit needs to accurately estimate the emergency range and total affected passenger flow volume. The urban rail transit network could be simplified to an abstract model which is easy to be analyst based on the graph theory method. Considering the actual network back-turning lines and vehicle storage tracks of urban rail network, the emergency range could be estimated effectively. The affected passenger flow could be classified as different kinds based on the different paths of passenger flow. The classification of passenger flow mainly includes “delay passenger flow”, “detour passenger flow” and “loss passenger flow”. Considering the emergency range, the different affected passenger flows could be superposed over time based on the abstract model, then the affected passenger flow volume and virtual loss time could be calculated out. The results could provide basis for the emergency disposal in urban rail transit. The example analysis is verified the feasibility of this method.

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RANDOM COEFFICIENT MODELING RESEARCH ON SHORT-TERM FORECAST OF PASSENGER FLOW INTO AN URBAN RAIL TRANSIT STATION

Transport ◽

10.3846/16484142.2016.1128484 ◽

2016 ◽

Vol 31 (1) ◽

pp. 94-99 ◽

Cited By ~ 6

Author(s):

Xuesong Feng ◽

Hemeizi Zhang ◽

Tiantian Gan ◽

Qipeng Sun ◽

Fei Ma ◽

...

Keyword(s):

Estimation Procedure ◽

Random Coefficient ◽

Urban Rail Transit ◽

Rail Transit ◽

Short Term ◽

Metro Station ◽

Factors Affecting ◽

Urban Rail ◽

Passenger Flows ◽

Transit Station

Taking a representative metro station in Beijing as example, this research has newly developed a random coefficient model to predict the short-term passenger flows with sudden increases sometimes into an urban rail transit station. The hierarchical Bayesian approach is iteratively applied in this work to estimate the new model and the estimation outcomes in each of the iterative calibrations are improved by sequential Bayesian updating. It has been proved that the estimation procedure is able to effectively converge to rational results with satisfying accuracies. In addition, the model application study reveals that besides sufficient preparations in manpower, devices, etc.; the information of the factors affecting the passenger flows into an urban rail transit station should be timely transferred in advance from important buildings, road intersections, squares and so on in neighborhood to this station. In this way, this station is able to cope with the unexpectedly sharp increases of the passenger flows into the station to ensure its operation safety.

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Urban Rail Transit System Network Reliability Analysis Based on a Coupled Map Lattice Model

Journal of Advanced Transportation ◽

10.1155/2021/5548956 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Shaojie Wu ◽

Yan Zhu ◽

Ning Li ◽

Yizeng Wang ◽

Xingju Wang ◽

...

Keyword(s):

Complex Network ◽

Network Reliability ◽

Urban Rail Transit ◽

Rail Transit ◽

Weighted Network ◽

Network Nodes ◽

Transit Network ◽

Passenger Flow ◽

Urban Rail ◽

Passenger Flows

During the last twenty years, the complex network modeling approach has been introduced to assess the reliability of rail transit networks, in which the dynamic performance involving passenger flows have attracted more attentions during operation stages recently. This paper proposes the passenger-flow-weighted network reliability evaluation indexes, to assess the impact of passenger flows on network reliability. The reliability performances of the rail transit network and passenger-flow-weighted one are analyzed from the perspective of a complex network. The actual passenger flow weight of urban transit network nodes was obtained from the Shanghai Metro public transportation card data, which were used to assess the reliability of the passenger-flow-weighted network. Furthermore, the dynamic model of the Shanghai urban rail transit network was constructed based on the coupled map lattice (CML) model. Then, the processes of cascading failure caused by network nodes under different destructive situations were simulated, to measure the changes of passenger-flow-weighted network reliability during the processes. The results indicate that when the scale of network damage attains 50%, the reliability of the passenger-flow-weighted network approaches zero. Consequently, taking countermeasures during the initial stage of network cascading may effectively prevent the disturbances from spreading in the network. The results of the paper could provide guidelines for operation management, as well as identify the unreliable stations within passenger-flow-weighted networks.

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Research on Passenger Flow Early Warning of Urban Rail Transit Station Based on System Dynamics

MATEC Web of Conferences ◽

10.1051/matecconf/202030801003 ◽

2020 ◽

Vol 308 ◽

pp. 01003

Author(s):

Hui Chen ◽

Bo Wang ◽

Wei He ◽

Jianhu Zheng

Keyword(s):

System Dynamics ◽

Early Warning ◽

Large Scale ◽

Urban Rail Transit ◽

Rail Transit ◽

Passenger Flow ◽

Urban Rail ◽

Passenger Flows ◽

Transit Station ◽

The Impact

Large-scale passenger flows occur frequently during the peak hours of urban rail transit stations and on holidays. Thus, the timely and accurate early warning of impending large-scale passenger flows can positively impact the operational safety of the entire station. By further deepening the definition of passenger flow warnings in stations, a new model of urban rail transit station passenger flow based on system dynamics is constructed. The method of determining the key area of passenger flows in the early warning stage based on streamlines is proposed; the key indicators and thresholds affecting early warnings are studied. Finally, taking a typical station as an example, a station model is built using Anylogic software. The parameter sensitivity analysis is used to determine the impact of each key indicator on the passenger flow in the key area of the station early warning, and the reference threshold of each indicator is determined.

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Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

Journal of Advanced Transportation ◽

10.1155/2017/5824051 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 9

Author(s):

Wei Zhu ◽

Wei Wang ◽

Zhaodong Huang

Keyword(s):

Flow Distribution ◽

Urban Rail Transit ◽

Load Factor ◽

Rail Transit ◽

Urban Rail ◽

Collection Data ◽

Data Driven Approach ◽

Passenger Flows ◽

Route Choices ◽

Shanghai Metro

An urban rail transit (URT) system is operated according to relatively punctual schedule, which is one of the most important constraints for a URT passenger’s travel. Thus, it is the key to estimate passengers’ train choices based on which passenger route choices as well as flow distribution on the URT network can be deduced. In this paper we propose a methodology that can estimate individual passenger’s train choices with real timetable and automatic fare collection (AFC) data. First, we formulate the addressed problem using Manski’s paradigm on modelling choice. Then, an integrated framework for estimating individual passenger’s train choices is developed through a data-driven approach. The approach links each passenger trip to the most feasible train itinerary. Initial case study on Shanghai metro shows that the proposed approach works well and can be further used for deducing other important operational indicators like route choices, passenger flows on section, load factor of train, and so forth.

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