Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)
Accurately predicting passenger flow at rail stations is an effective way to reduce operation and maintenance costs, improve the quality of passenger travel while meeting future passenger travel demand. The improvement of data acquisition capability allows fine-grained and large-scale built environm...
Saved in:
Main Authors: | Luzhou Lin, Yuezhe Gao, Bingxin Cao, Zifan Wang, Cai Jia |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2023-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2023/1430449 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM
by: Haijun Li, et al.
Published: (2022-01-01) -
Research on Coordinated Passenger Inflow Control for the Urban Rail Transit Network Based on the Station-to-Line Spatial-Temporal Relationship
by: Ruixia Yang, et al.
Published: (2022-01-01) -
Urban Rail Transit Scheduling under Time-Varying Passenger Demand
by: Xing Zhao, et al.
Published: (2018-01-01) -
Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China
by: Yu Wang, et al.
Published: (2023-01-01) -
Joint Operating Revenue and Passenger Travel Cost Optimization in Urban Rail Transit
by: Wenxin Li, et al.
Published: (2018-01-01)