Similarity-Based Prediction of Travel Times for Vehicles Traveling on Known Routes

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

30 Citations (Scopus)

Abstract

The use of centralized, real-time position tracking is proliferating in the areas of logistics and public transportation. Real-time positions can be used to provide up-to-date information to a variety of users, and they can also be accumulated for uses in subsequent data analyses. In particular, historical data in combination with real-time data may be used to predict the future travel times of vehicles more accurately, thus improving the experience of the users who rely on such information. We propose a Nearest-Neighbor Trajectory (NNT) technique that identifies the historical trajectory that is the most similar to the current, partial trajectory of a vehicle. The historical trajectory is then used for predicting the future movement of the vehicle. The paper's specific contributions are two-fold. First, we define distance measures and a notion of nearest neighbor that are specific to trajectories of vehicles that travel along known routes. In empirical studies with real data from buses, we evaluate how well the proposed distance functions are capable of predicting future vehicle movements. Second, we propose a main-memory index structure that enables incremental similarity search and that is capable of supporting varying-length nearest neighbor queries.
Original languageEnglish
Title of host publicationProceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
PublisherAssociation for Computing Machinery
Publication date2008
Pages105-114
DOIs
Publication statusPublished - 2008
EventSixteenth ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Irvine, United States
Duration: 5 Nov 20087 Nov 2008
Conference number: 16

Conference

ConferenceSixteenth ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Number16
CountryUnited States
CityIrvine
Period05/11/200807/11/2008

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