On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network

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

Resumé

Road networks are a type of spatial network, where
edges may be associated with qualitative information such as
road type and speed limit. Unfortunately, such information is
often incomplete; for instance, OpenStreetMap only has speed
limits for 13% of all Danish road segments. This is problematic
for analysis tasks that rely on such information for machine
learning. To enable machine learning in such circumstances, one
may consider the application of network embedding methods to
extract structural information from the network. However, these
methods have so far mostly been used in the context of social
networks, which differ significantly from road networks in terms
of, e.g., node degree and level of homophily (which are key to
the performance of many network embedding methods).
We analyze the use of network embedding methods, specifically node2vec, for learning road segment embeddings in road
networks. Due to the often limited availability of information
on other relevant road characteristics, the analysis focuses on
leveraging the spatial network structure. Our results suggest that
network embedding methods can indeed be used for deriving
relevant network features (that may, e.g, be used for predicting
speed limits), but that the qualities of the embeddings differ from
embeddings for social networks.
OriginalsprogEngelsk
TitelProceedings of the 2018 IEEE International Conference on Big Data
Antal sider10
ForlagIEEE
Publikationsdato2018
Sider3421-3430
ISBN (Elektronisk)978-1-5386-5035-6
DOI
StatusUdgivet - 2018
Begivenhed2018 IEEE International Conference on Big Data - Seattle, USA
Varighed: 10 dec. 201813 dec. 2018

Konference

Konference2018 IEEE International Conference on Big Data
LandUSA
BySeattle
Periode10/12/201813/12/2018

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    @inproceedings{b391f9dee0f74d9a8818794b84b07ae7,
    title = "On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network",
    abstract = "Road networks are a type of spatial network, whereedges may be associated with qualitative information such asroad type and speed limit. Unfortunately, such information isoften incomplete; for instance, OpenStreetMap only has speedlimits for 13{\%} of all Danish road segments. This is problematicfor analysis tasks that rely on such information for machinelearning. To enable machine learning in such circumstances, onemay consider the application of network embedding methods toextract structural information from the network. However, thesemethods have so far mostly been used in the context of socialnetworks, which differ significantly from road networks in termsof, e.g., node degree and level of homophily (which are key tothe performance of many network embedding methods).We analyze the use of network embedding methods, specifically node2vec, for learning road segment embeddings in roadnetworks. Due to the often limited availability of informationon other relevant road characteristics, the analysis focuses onleveraging the spatial network structure. Our results suggest thatnetwork embedding methods can indeed be used for derivingrelevant network features (that may, e.g, be used for predictingspeed limits), but that the qualities of the embeddings differ fromembeddings for social networks.",
    keywords = "road network, machine learning, feature learning, network embedding",
    author = "Jepsen, {Tobias S.} and Jensen, {Christian S{\o}ndergaard} and Nielsen, {Thomas Dyhre} and Kristian Torp",
    year = "2018",
    doi = "10.1109/BigData.2018.8622416",
    language = "English",
    pages = "3421--3430",
    booktitle = "Proceedings of the 2018 IEEE International Conference on Big Data",
    publisher = "IEEE",
    address = "United States",

    }

    On Network Embedding for Machine Learning on Road Networks : A Case Study on the Danish Road Network. / Jepsen, Tobias S.; Jensen, Christian Søndergaard; Nielsen, Thomas Dyhre; Torp, Kristian.

    Proceedings of the 2018 IEEE International Conference on Big Data. IEEE, 2018. s. 3421-3430.

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

    TY - GEN

    T1 - On Network Embedding for Machine Learning on Road Networks

    T2 - A Case Study on the Danish Road Network

    AU - Jepsen, Tobias S.

    AU - Jensen, Christian Søndergaard

    AU - Nielsen, Thomas Dyhre

    AU - Torp, Kristian

    PY - 2018

    Y1 - 2018

    N2 - Road networks are a type of spatial network, whereedges may be associated with qualitative information such asroad type and speed limit. Unfortunately, such information isoften incomplete; for instance, OpenStreetMap only has speedlimits for 13% of all Danish road segments. This is problematicfor analysis tasks that rely on such information for machinelearning. To enable machine learning in such circumstances, onemay consider the application of network embedding methods toextract structural information from the network. However, thesemethods have so far mostly been used in the context of socialnetworks, which differ significantly from road networks in termsof, e.g., node degree and level of homophily (which are key tothe performance of many network embedding methods).We analyze the use of network embedding methods, specifically node2vec, for learning road segment embeddings in roadnetworks. Due to the often limited availability of informationon other relevant road characteristics, the analysis focuses onleveraging the spatial network structure. Our results suggest thatnetwork embedding methods can indeed be used for derivingrelevant network features (that may, e.g, be used for predictingspeed limits), but that the qualities of the embeddings differ fromembeddings for social networks.

    AB - Road networks are a type of spatial network, whereedges may be associated with qualitative information such asroad type and speed limit. Unfortunately, such information isoften incomplete; for instance, OpenStreetMap only has speedlimits for 13% of all Danish road segments. This is problematicfor analysis tasks that rely on such information for machinelearning. To enable machine learning in such circumstances, onemay consider the application of network embedding methods toextract structural information from the network. However, thesemethods have so far mostly been used in the context of socialnetworks, which differ significantly from road networks in termsof, e.g., node degree and level of homophily (which are key tothe performance of many network embedding methods).We analyze the use of network embedding methods, specifically node2vec, for learning road segment embeddings in roadnetworks. Due to the often limited availability of informationon other relevant road characteristics, the analysis focuses onleveraging the spatial network structure. Our results suggest thatnetwork embedding methods can indeed be used for derivingrelevant network features (that may, e.g, be used for predictingspeed limits), but that the qualities of the embeddings differ fromembeddings for social networks.

    KW - road network

    KW - machine learning

    KW - feature learning

    KW - network embedding

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