A NUMA-aware Trajectory Store for Travel-Time Estimation

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The increasingly massive volumes of vehicle trajectory data that are becoming available hold the potential to enable more accurate vehicle travel-time estimation than hitherto possible. To enable such uses, we present a multi-threaded, in-memory trajectory store that supports efficient and accurate travel-time estimation for road-network paths based on network-constrained trajectories. The trajectory store employs advanced indexing to support so-called strict-path queries that retrieve all trajectories that traverse a given path to provide accurate travel-time estimations. As a key novel feature, the store is designed and implemented to exploit modern non-uniform memory access (NUMA) systems. We provide a detailed experimental study of the performance of the trajectory store using a synthetic trajectory data set based on real traffic data. The study shows that query latency can be halved compared to our baseline system.
Original languageEnglish
Title of host publicationProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
EditorsFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
Number of pages10
PublisherAssociation for Computing Machinery
Publication date2019
ISBN (Electronic)978-1-4503-6909-1
Publication statusPublished - 2019
EventInternational Conference on Advances in Geographic Information Systems - Chicago, United States
Duration: 5 Nov 20198 Nov 2019
Conference number: 27th


ConferenceInternational Conference on Advances in Geographic Information Systems
CountryUnited States


  • Indexing
  • Moving objects
  • NUMA

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