This paper presents the development of a new model for predicting traffic incident duration using random forests (RFs), a data-driven machine learning technique. Utilizing an extensive dataset with over 140,000 incident records and 52 variables, the developed models were optimized by fine-tuning their parameters. The best-performing RF model achieved a mean absolute error (MAE) of 36.652 min, which is acceptable given the wide range of incident duration considered (1–1,440 min). Another set of models was developed using a short range of 5- to 120-minute incident duration. The performance of the best models for the short range improved significantly, i.e. the MAE decreased to 14.979 min (about a 40% reduction). In comparison, the ANN models developed using the same dataset slightly outperformed (only 0.24%) their RF counterparts; nevertheless, the RF models showed more stable results with a small-error range. Further analysis confirmed that the accuracy of the predictions could be slightly downgraded in return for a substantial reduction in the number of variables utilized.

中文翻译：

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使用随机森林预测事件持续时间

本文介绍了使用随机森林 (RF)（一种数据驱动的机器学习技术）预测交通事故持续时间的新模型的开发。利用包含超过 140,000 条事件记录和 52 个变量的广泛数据集，开发的模型通过微调参数进行了优化。性能最佳的 RF 模型实现了 36.652 分钟的平均绝对误差 (MAE)，考虑到所考虑的事件持续时间范围很广（1-1,440 分钟），这是可以接受的。另一组模型是使用短范围的 5 到 120 分钟事件持续时间开发的。短程最佳模型的性能显着提高，即 MAE 降低到 14.979 分钟（减少约 40%）。相比之下，使用相同数据集开发的 ANN 模型略微优于（仅 0.24%）它们的 RF 模型；尽管如此，RF 模型显示出更稳定的结果，误差范围较小。进一步的分析证实，预测的准确性可能会略微降低，以换取大量减少使用的变量数量。