Passenger Flow Forecast of Urban Rail Transit Based on BP Neural Networks

2011 ◽  
Author(s):  
Dongquan Zhang ◽  
Lina Wang
Keyword(s):  
Neural Networks ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Bp Neural Networks ◽  
Passenger Flow ◽  
Urban Rail ◽  
Flow Forecast

scholarly journals Research on passenger flow forecast of Urban Rail Transit

2020 ◽  
Vol 213 ◽  
pp. 03026
Author(s):  
Xiao Li
Keyword(s):  
Public Transport ◽  
Urban Rail Transit ◽  
Design Stage ◽  
Rail Transit ◽  
Allocation Model ◽  
Passenger Flow ◽  
Planning And Design ◽  
Dongguan City ◽  
Urban Rail ◽  
Flow Forecast

Urban rail transit has the characteristics of large traffic volume, fast and convenient, so it has become the urban passenger transport mode which is given priority in the planning and design of each city. The prediction and analysis of rail transit passenger flow is a very important part in the planning and design stage. And it also provides important basic data for urban rail planning, construction and operation. When the rail transit network needs to be adjusted, the passenger flow forecast is also an essential step. In this paper, Taking Dongguan urban rail transit line 2 as an example, based on the full consideration of the uniqueness of Dongguan urban rail transit and the prediction of various social and economic indicators of Dongguan City, the “four step method” is adopted to forecast passenger flow. Through the investigation of resident’s travel survey, we can master the current travel distribution of all modes. Based on the analysis of land use change, traffic development policy and related influencing factors of Dongguan City, the trip distribution of each characteristic year is predicted. Then, the OD matrix of public transport (including subway and conventional public transport) is obtained through the study of traffic mode division model, and the predicted rail passenger flow is obtained through the allocation model of cooperation and competition. In the stage of passenger flow assignment, the influence of various factors on travel route selection is comprehensively considered in the public transport allocation model. Finally, the prediction index and sensitivity of rail passenger flow are analyzed.

Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features

2019 ◽  
Vol 13 (10) ◽  
pp. 1475-1482 ◽  
Author(s):  
Dan Yang ◽  
Kairun Chen ◽  
Mengning Yang ◽  
Xiaochao Zhao
Keyword(s):  
Urban Rail Transit ◽  
Rail Transit ◽  
Passenger Flow ◽  
Urban Rail ◽  
Flow Forecast
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