There are numerous studies on traffic volume prediction, using either non-parametric or parametric methods. The main shortcoming of parametric methods is low prediction accuracy. Non-parametric methods show higher prediction accuracy, but they are criticised due to lack of support from theory. The innovation of this paper is to combine bootstrap with the conventional parametric ARIMA model with the aim of improving prediction accuracy while maintaining theory adherence. The outcome of this process is an ensemble of ARIMA models (E-ARIMA) where each model is developed using a random subsample of data. The validity of the proposed model is examined by comparing E-ARIMA with ARIMA and Long Short-Term Memory (LSTM) as representatives for parametric and non-parametric methods respectively. One year of traffic count data on four main arterial roads in Sydney, Australia is used for calibration and validation purposes. The results suggest that creating an ensemble of models improves prediction accuracy.

中文翻译：

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ARIMA 集成：结合参数和引导技术进行交通流预测

有许多关于交通量预测的研究，使用非参数或参数方法。参数化方法的主要缺点是预测精度低。非参数方法显示出更高的预测精度，但由于缺乏理论支持而受到批评。本文的创新之处在于将 bootstrap 与传统的参数化 ARIMA 模型相结合，目的是在保持理论依从性的同时提高预测精度。此过程的结果是 ARIMA 模型 (E-ARIMA) 的集合，其中每个模型都是使用数据的随机子样本开发的。通过将 E-ARIMA 与 ARIMA 和长短期记忆（LSTM）分别作为参数和非参数方法的代表，来检验所提出模型的有效性。澳大利亚悉尼 4 条主要干道上一年的交通计数数据用于校准和验证目的。结果表明，创建模型集合可以提高预测准确性。