Dynamic Spatio-temporal Integration of Traffic Accident Data

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

Up to 50% of delay in traffic is due to non-reoccurring events such as traffic accidents. Accidents lead to delays, which can be costly for transport companies. Road authorities are also very interested in warning drivers about accidents, e.g., to reroute them. This paper presents a novel and efficient approach and system for uncovering effects from traffic accidents by dynamic integration of GPS, weather, and traffic-accident data. This integration makes it possible to explore and quantify how accidents affects traffic. Dynamic integration means that data is combined at query time as it becomes available. This is necessary, because data can be missing (weather station down) or late arriving (accident not officially reported by the police yet). Further, the integration can be parameterized by the user, e.g., distance to accident, which is important due to inaccuracy in reporting. We present the integrated data on a map and show the
effectiveness of the integration by allowing users to interactively browse all accidents or pick a single accident to study it in very fine-grained details. Using information from 31 433 road accidents and 38 billion GPS records, we show that the proposed dynamic data integration scales so very large data sets.
OriginalsprogEngelsk
TitelProceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
RedaktørerLi Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel
Antal sider4
ForlagAssociation for Computing Machinery
Publikationsdato6 nov. 2018
Sider596-599
ISBN (Trykt)978-1-4503-5889-7
ISBN (Elektronisk)9781450358897
DOI
StatusUdgivet - 6 nov. 2018
BegivenhedACM SIGSPATIAL GIS 2018 - Seattle Marriott Waterfront, Seattle, USA
Varighed: 6 nov. 20189 nov. 2018
Konferencens nummer: 26
https://sigspatial2018.sigspatial.org/

Konference

KonferenceACM SIGSPATIAL GIS 2018
Nummer26
Lokation Seattle Marriott Waterfront
LandUSA
BySeattle
Periode06/11/201809/11/2018
Internetadresse

Emneord

    Citer dette

    Andersen, O., & Torp, K. (2018). Dynamic Spatio-temporal Integration of Traffic Accident Data. I L. Xiong, R. Tamassia, K. F. Banaei, R. H. Guting, & E. Hoel (red.), Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (s. 596-599). Association for Computing Machinery. https://doi.org/10.1145/3274895.3274972
    Andersen, Ove ; Torp, Kristian. / Dynamic Spatio-temporal Integration of Traffic Accident Data. Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . red. / Li Xiong ; Roberto Tamassia ; Kashani Farnoush Banaei ; Ralf Hartmut Guting ; Erik Hoel. Association for Computing Machinery, 2018. s. 596-599
    @inproceedings{5a10df48a4d5444480ef4d8e97e66db2,
    title = "Dynamic Spatio-temporal Integration of Traffic Accident Data",
    abstract = "Up to 50{\%} of delay in traffic is due to non-reoccurring events such as traffic accidents. Accidents lead to delays, which can be costly for transport companies. Road authorities are also very interested in warning drivers about accidents, e.g., to reroute them. This paper presents a novel and efficient approach and system for uncovering effects from traffic accidents by dynamic integration of GPS, weather, and traffic-accident data. This integration makes it possible to explore and quantify how accidents affects traffic. Dynamic integration means that data is combined at query time as it becomes available. This is necessary, because data can be missing (weather station down) or late arriving (accident not officially reported by the police yet). Further, the integration can be parameterized by the user, e.g., distance to accident, which is important due to inaccuracy in reporting. We present the integrated data on a map and show theeffectiveness of the integration by allowing users to interactively browse all accidents or pick a single accident to study it in very fine-grained details. Using information from 31 433 road accidents and 38 billion GPS records, we show that the proposed dynamic data integration scales so very large data sets.",
    keywords = "Data integration, GPS, Spatio-temporal, Traffic accidents, Weather",
    author = "Ove Andersen and Kristian Torp",
    year = "2018",
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    doi = "10.1145/3274895.3274972",
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    Andersen, O & Torp, K 2018, Dynamic Spatio-temporal Integration of Traffic Accident Data. i L Xiong, R Tamassia, KF Banaei, RH Guting & E Hoel (red), Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . Association for Computing Machinery, s. 596-599, ACM SIGSPATIAL GIS 2018, Seattle, USA, 06/11/2018. https://doi.org/10.1145/3274895.3274972

    Dynamic Spatio-temporal Integration of Traffic Accident Data. / Andersen, Ove; Torp, Kristian.

    Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . red. / Li Xiong; Roberto Tamassia; Kashani Farnoush Banaei; Ralf Hartmut Guting; Erik Hoel. Association for Computing Machinery, 2018. s. 596-599.

    Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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    Andersen O, Torp K. Dynamic Spatio-temporal Integration of Traffic Accident Data. I Xiong L, Tamassia R, Banaei KF, Guting RH, Hoel E, red., Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . Association for Computing Machinery. 2018. s. 596-599 https://doi.org/10.1145/3274895.3274972