A Hybrid Short-Term Forecasting Model of Passenger Flow on High-Speed Rail considering the Impact of Train Service Frequency

Minshu Ma,Yongji Luo,Qingying Lai,Jun Liu

doi:10.1155/2017/1828102

Abstract

Short-term forecasting of OD (origin to destination) passenger flow on high-speed rail (HSR) is one of the critical tasks in rail traffic management. This paper proposes a hybrid model to explore the impact of the train service frequency (TSF) of the HSR on the passenger flow. The model is composed of two parts. One is the Holt-Winters model, which takes advantage of time series characteristics of passenger flow. The other part considers the changes of TSF for the OD in different time during a day. The two models are integrated by the minimum absolute value method to generate the final hybrid model. The operational data of Beijing-Shanghai high-speed railway from 2012 to 2016 are used to verify the effectiveness of the model. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of the TSF.

Highlights

As of September 10, 2016, the operating length of China’s high-speed rail (HSR) has exceeded 20,000 km
This paper proposes a hybrid model to explore the impact of the train service frequency (TSF) of the HSR on the passenger flow
The income of the HSR mainly comes from two parts of the passenger flow, one is the newly generated HSR passenger traffic, and the other one is the passenger who transforms from other modes of transport

Summary

Introduction

As of September 10, 2016, the operating length of China’s HSR has exceeded 20,000 km A lot of attention has been paid to the short-term forecasting These models can be generally divided into three categories: time series models, causal models, and hybrid models. Wardman [17] proposed a forecasting model for railway traffic flow using external factors including GDP, variations in times, and fuel costs. We construct a hybrid forecasting model to forecast the daily passenger flow (DPF) by taking the advantages of the time series model and the causal model considering the impact of the TSF.

Daily Passenger Flow Analysis

Hybrid Forecasting Model

Case Analysis

Findings

Conclusions