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

机译：基于时间序列模型和交互式多模型方法的短期巴士客运需求预测

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摘要

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.
机译：尽管近年来对公共汽车乘客需求的预测已引起了越来越多的关注，但是在短期乘客需求预测的背景下，研究还很有限。本文提出了一种基于交互式多模型（IMM）滤波算法的模型来预测短期乘客需求。在以15分钟为间隔进行汇总后，使用四个月从繁忙的公交路线收集的乘客需求数据来生成时间序列。考虑到乘客需求在不同的时间范围内表现出各种特征，因此开发了三个时间序列，分别命名为每周，每天和15分钟。经过相关性，周期性和平稳性分析，构建了时间序列模型。特别是，探索时间序列的异方差性以获得更好的预测性能。最后，运用IMM滤波算法将各个预测模型与下一个区间的动态预测乘客需求相结合。对单独模型和混合模型的分析采用了不同的误差指数。性能比较表明，混合模型预测的准确性优于单个模型。这项研究的发现对公交车调度具有理论和实践意义。

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