Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term pa...

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Bibliographic Details
Published in:Discrete Dynamics in Nature and Society 2015-01, Vol.2015 (2015), p.391-401-240
Main Authors: Xue, Rui, Chen, Shukai, Sun, Jian
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Architectural engineering
Buses
Buses (vehicles)
Civil engineering
Demand
Economic models
Forecasting
Forecasts and trends
Interactive
Intervals
Kalman filters
Laboratories
Mathematical models
Neural networks
Passengers
Stochastic models
Studies
Time series
Time-series analysis
Traffic flow
Trends
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Summary:Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.
ISSN:1026-0226
1607-887X
DOI:10.1155/2015/682390