Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study

Ciyun Lin, Kang Wang, Dayong Wu and Bowen Gong
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Ciyun Lin: Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
Kang Wang: Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
Dayong Wu: Texas A&M Transportation Institute, Texas A&M University, College Station, Texas, TX 77843, USA
Bowen Gong: Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China

Sustainability, 2020, vol. 12, issue 17, 1-22

Abstract: High-density land uses cause high-intensity traffic demand. Metro as an urban mass transit mode is considered as a sustainable strategy to balance the urban high-density land uses development and the high-intensity traffic demand. However, the capacity of the metro cannot always meet the traffic demand during rush hours. It calls for traffic agents to reinforce the operation and management standard to improve the service level. Passenger flow prediction is the foremost and pivotal technology in improving the management standard and service level of metro. It is an important technological means in ensuring sustainable and steady development of urban transportation. This paper uses mathematical and neural network modeling methods to predict metro passenger flow based on the land uses around the metro stations, along with considering the spatial correlation of metro stations within the metro line and the temporal correlation of time series in passenger flow prediction. It aims to provide a feasible solution to predict the passenger flow based on land uses around the metro stations and then potentially improving the understanding of the land uses around the metro station impact on the metro passenger flow, and exploring the potential association between the land uses and the metro passenger flow. Based on the data source from metro line 2 in Qingdao, China, the perdition results show the proposed methods have a good accuracy, with Mean Absolute Percentage Errors (MAPEs) of 11.6%, 3.24%, and 3.86 corresponding to the metro line prediction model with Categorical Regression (CATREG), single metro station prediction model with Artificial Neural Network (ANN), and single metro station prediction model with Long Short-Term Memory (LSTM), respectively.

Keywords: passenger flow prediction; land use; artificial neural network; long short-term memory; metro station (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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