Predicting incident duration using random forests. Issue 3 (1st January 2020)

• Record Type: Journal Article
• Authors: Hamad, Khaled; Al-Ruzouq, Rami; Zeiada, Waleed; Abu Dabous, Saleh; Khalil, Mohamad Ali
• Is Part Of: Transportmetrica. Volume 16:Issue 3(2020)
• Page Start: 1269
• Page End: 1293
• Publication Date: 2020-01-01
• Subjects: Random forests -- incident duration -- machine learning -- artificial neural networks -- variable importance; Transportation -- Periodicals; Transportation -- Research -- Periodicals; 388.072
• DOI: 10.1080/23249935.2020.1733132
• Languages: English
• ISSNs: 2324-9935

Abstract

Abstract : 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.