A Travel Time Prediction Algorithm Scalable to Freeway Networks with Many Nodes with Arbitrary Travel Routes

A travel time prediction algorithm scalable to large freeway networks with many nodes with arbitrary travel routes is proposed. Instead of constructing separate predictors for individual routes, it first predicts the whole future space-time field of travel times and then traverses the required subsection of the predicted travel time field to compute the travel time estimate for the requested route. Compared with the traditional approach that offers the same flexibility, the proposed method substantially reduces the storage and computation time requirements, at the relatively small computational cost at the time of actual prediction. We first establish that travel times computed by traversing travel time fields are compatible with more direct measurements of travel times from a vehicle re-identification technique based on electronic toll collection tags. This provides a conceptual justification of the proposed approach. When applied to loop data from an 8-mile section of the I-80 freeway, the proposed approach with time-varying coefficient (TVC) linear regression model as the component predictor not only improves the baseline historical travel time predictor substantially with 40~60 % reduction in prediction error, but also improves the route predictor using the same TVC regression model, with 6~9% reduction in error. The result suggests that the proposed approach achieves scalability but also improves prediction accuracy, both of which are critical for successful deployment of advanced travelers information system (ATIS) for large freeway networks.