Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning

Wusheng Liu,Qian Tan,Wei Wu,Hussein Abulkasim

doi:10.1155/2020/6625435

Abstract

This paper mainly forecasts the short-term passenger flow of regional bus stations based on the integrated circuit (IC) card data of bus stations and puts forward an early warning model for regional bus passenger flow. Firstly, the bus stations were aggregated into virtual regional bus stations. Then, the short-term passenger flow of regional bus stations was predicted by the machine learning (ML) method of support vector machine (SVM). On this basis, the early warning model for regional bus passenger flow was developed through the capacity analysis of regional bus stations. The results show that the prediction accuracy of short-term passenger flow could be improved by replacing actual bus stations with virtual regional bus stations because the passenger flow of regional bus stations is more stable than that of a single bus station. The accurate prediction and early warning of regional bus passenger flow enable urban bus dispatchers to maintain effective control of urban public transport, especially during special and large-scale activities.

Highlights

Urban public transport is a traffic mode to alleviate traffic congestion and make efficient use of road resources
Bus Station Data and integrated circuit (IC) Card Data. e IC card used in this paper is Shenzhen Tong. It is a kind of stored value card for consumption by Shenzhen bus and Shenzhen Metro, which is manufactured under the supervision of Shenzhen Transport Bureau and issued by Shenzhen public transport settlement management center. e bus data only displays the valid information such as the user card number, card swiping time, and bus license plate number
Two time windows were randomly selected, namely, 11:00-12:00 and 19:00-20:00 on December 4, 2016, and the number of boarding passengers in the 993 grids was counted. e results predicted by our method are compared with the actual data in Figures 6 and 7

Summary

Introduction

Urban public transport is a traffic mode to alleviate traffic congestion and make efficient use of road resources. In the forecast of short-term passenger flow at a bus station, the entry and exit volumes mainly come from the card swiping records of the automatic fare collection (AFC) system Taking such continuous data as a time series, many models have been designed for traffic flow prediction based on statistical principles, including autoregressive (AR) model, moving average (MA) model, autoregressive integrated moving average (ARIMA) model, and seasonal ARIMA (SARIMA) model [1,2,3,4]. E results show that the prediction accuracy of short-term passenger flow could be improved by replacing actual bus stations with virtual regional bus stations On this basis, we designed an early warning model for regional bus passenger flow, which monitors the passenger flow in important areas during the period of special activities (e.g., large events) and takes control measures in advance to ensure the smooth progress of these activities.

Bus Station Distribution and IC Card Data Processing

Short-Term Passenger Flow Prediction at Regional Bus Stations

Prediction Results and Model Test

Early Warning Model for Regional Bus Passenger Flow

15-16 Time window