Real-time travel time prediction algorithm using spatiotemporal speed interval patterns matching

Abstract

Accurate, efficient and robust travel time prediction is crucial to the development of advanced traveler information systems for providing route guidance information. To achieve this goal, this paper proposed a travel time prediction through the matching of the current speed interval pattern to that in a historical database. Speed intervals, instead of speeds, are considered in this study to simplify the structure of matching patterns for improving matching efficiency. In this study, speed interval patterns are defined by sets of link speed intervals that are either spatially or temporarily correlated with the link considered. With the speed interval patterns, the algorithm is developed for searching the historical pattern(s) that is/are the closest match with the current one. Then, link speeds from these matched patterns are combined for travel time prediction. By using the GPS probe taxi data, which the collected speeds are aggregated in every 5 minutes, the proposed travel time prediction system is implemented in Bangkok. With the speed data from probe taxi, this paper has chosen four links/paths with different geometric and flow characteristics for testing the performance of the proposed travel time prediction system. From these tests, it is found that the optimal speed interval pattern should include: 1) speeds of the studied link within three preceding time intervals and; 2) speeds of links in the first connection level of the studied link. Also, while the computational time is capable of real-time application, the proposed prediction algorithm is more accurate under uninterrupted flow conditions.