A NUMA-aware Trajectory Store for Travel-Time Estimation

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Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
RedaktørerFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
Antal sider10
ForlagAssociation for Computing Machinery
Publikationsdato2019
Sider209-218
ISBN (Elektronisk)978-1-4503-6909-1
DOI
StatusUdgivet - 2019
BegivenhedInternational Conference on Advances in Geographic Information Systems - Chicago, USA
Varighed: 5 nov. 20198 nov. 2019
Konferencens nummer: 27th

Konference

KonferenceInternational Conference on Advances in Geographic Information Systems
Nummer27th
LandUSA
ByChicago
Periode05/11/201908/11/2019

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  • Citationsformater

    Waury, R., Jensen, C. S., & Torp, K. (2019). A NUMA-aware Trajectory Store for Travel-Time Estimation. I F. Banaei-Kashani, G. Trajcevski, R. H. Guting, L. Kulik, & S. Newsam (red.), Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (s. 209-218). Association for Computing Machinery. https://doi.org/10.1145/3347146.3359371