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

Ciyun Lin,Bowen Gong,Kang Wang,Dayong Wu

doi:10.3390/su12176844

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.

Highlights

With the rapid socio-economic development in China, China has experienced urbanization on a scale unprecedented in recent decades
The main work of this paper focuses on: (1) Using 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, and exploring the potential association between the land uses and the metro passenger flow; (2) Providing a feasible solution to predict the passenger flow based on land uses around the metro stations and potentially improving the understanding of the land uses around the metro station impact on the metro passenger flow, exploring the prediction procedure of the land uses to metro passenger flow
We used mathematical and neural network modeling methods to identify the relationship between the land uses around a metro station and the metro passenger flow

Summary

Introduction

With the rapid socio-economic development in China, China has experienced urbanization on a scale unprecedented in recent decades. Urbanization leads to high-density development of land use in urban areas for the increasing population. High-density land uses cause high-intensity traffic demand. Land use and transport are hot topics within sustainable transportation in China, as they are undergoing a major demographic transition of rapid and intense urbanization [1]. As to relieve the burden of traffic network for high-intensity traffic demand, the public transport leading oriented development is considered as a rational and sustainable strategy to balance the urban high-density land use development and the high-intensity traffic demand. With the advantages of being efficient, smooth, green, safe, large-volume, and land-saving, is the first choice of transport mode which is developing in many metropolises all over the world [2]

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