Urban Rail Transit Passenger Flow Forecasting Method Based on the Coupling of Artificial Fish Swarm and Improved Particle Swarm Optimization Algorithms

Yuan Yuan, Chunfu Shao, Zhichao Cao, Wenxin Chen, Anteng Yin, Hao Yue and Binglei Xie
Additional contact information
Yuan Yuan: Key Laboratory of Transport Industry Big Data Application Technologies for Comprehensive Key Laboratory of Transport, Beijing Jiaotong University, Beijing 100044, China
Chunfu Shao: Key Laboratory of Transport Industry Big Data Application Technologies for Comprehensive Key Laboratory of Transport, Beijing Jiaotong University, Beijing 100044, China
Zhichao Cao: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Wenxin Chen: Key Laboratory of Transport Industry Big Data Application Technologies for Comprehensive Key Laboratory of Transport, Beijing Jiaotong University, Beijing 100044, China
Anteng Yin: School of Architecture, Harbin Institute of Technology, Harbin 150001, China
Hao Yue: Key Laboratory of Transport Industry Big Data Application Technologies for Comprehensive Key Laboratory of Transport, Beijing Jiaotong University, Beijing 100044, China
Binglei Xie: School of Architecture, Harbin Institute of Technology, Harbin 150001, China

Sustainability, 2019, vol. 11, issue 24, 1-13

Abstract: Urban rail transit passenger flow forecasting is an important basis for station design, passenger flow organization, and train operation plan optimization. In this work, we combined the artificial fish swarm and improved particle swarm optimization (AFSA-PSO) algorithms. Taking the Window of the World station of the Shenzhen Metro Line 1 as an example, subway passenger flow prediction research was carried out. The AFSA-PSO algorithm successfully preserved the fast convergence and strong traceability of the original algorithm through particle self-adjustment and dynamic weights, and it effectively overcame its shortcomings, such as the tendency to fall into local optimum and lower convergence speed. In addition to accurately predicting normal passenger flow, the algorithm can also effectively identify and predict the large-scale tourist attractions passenger flow as it has strong applicability and robustness. Compared with single PSO or AFSA algorithms, the new algorithm has better prediction effects, such as faster convergence, lower average absolute percentage error, and a higher correlation coefficient with real values.

Keywords: urban traffic; subway passenger flow prediction; AFSA-PSO algorithm; normal passenger flow; large-scale tourist attractions passenger flow (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/24/7230/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/24/7230/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:24:p:7230-:d:299712

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().