Travel Time Reliability Prediction Using Random Forests

Abstract

The concept of travel time reliability was developed to quantify the variability in travel times. As travel time reliability measures are increasingly used in system planning and performance measurement processes at many transportation agencies, predicting travel time reliability measures has become critical. However, it can be challenging because of the dynamic nature of traffic and the variety of factors contributing to unreliable travel times. This paper developed machine learning models to predict travel time reliability at a planning level. Two random forest algorithms, quantile random forests (QRF) and generalized random forests (GRF), were used to develop prediction models while taking account of a variety of variables from multiple data sources simultaneously. The reliability measures studied are the percentiles of travel times as they are a key component for many commonly used travel time reliability measures. Both QRF and GRF models produced accurate predictions; GRF performed better than QRF at predicting the 50th percentile travel time, and QRF achieved slightly better predictions for the 90th percentile. A case study demonstrated the use of the proposed models for estimating the impact on travel time reliability from an improvement project. The results found both models captured the trend in reliability change, and GRF was preferred over QRF for estimating the level of travel time reliability.