An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

Qi Ouyang,Jihui Ma,Yongbo Lv,Jing Li

doi:10.3390/app10113788

Abstract

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

Highlights

Public transport is a system of transport for passengers by group travel systems, typically managed on a schedule, operated on established routes, and that charge a posted fee for each trip
To solve the problem of passenger flow prediction, especially for real-time passenger flow prediction, this paper proposes a passenger flow prediction model based on the history and real-time data (HRPFP)
Based on the long short-term memory (LSTM) structure, this paper proposes a new passenger flow prediction model that considers real-time data

Summary

Introduction

Public transport ( known as public transportation, public transit, or mass transit) is a system of transport for passengers by group travel systems, typically managed on a schedule, operated on established routes, and that charge a posted fee for each trip. The bus operation system is a major component of public transport and plays an important role. Ways to predict the passenger demand by bus are always the research focuses of scholars. The real-time passenger flow forecasting (observing the passenger flow in a short period time and predicting the passenger number for a period of time) is a very important part of the bus operation system. To solve the problem of passenger flow prediction, especially for real-time passenger flow prediction, this paper proposes a passenger flow prediction model based on the history and real-time data (HRPFP). The HRPFP model mainly contains four parts: the feature extraction part, the history information encoding part, the real-time information encoding part, and the decoding part. In the feature extraction part, we use the Xgboost algorithm to combine the smart card data and the points of interest together

Methods

Results

Conclusion