Potential trend discovery for highway drivers on spatio‐temporal data

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dc.contributor.authorDing, Weilong
dc.contributor.authorWang, Zhe
dc.contributor.authorChen, Jun
dc.contributor.authorXia, Yanqing
dc.contributor.authorWang, Jianwu
dc.contributor.authorZhao, Zhuofeng
dc.date.accessioned2022-10-17T16:42:55Z
dc.date.available2022-10-17T16:42:55Z
dc.date.issued2021-02-07
dc.description.abstractInter-city transportation plays an important role in modern cities, and has accumulated massive spatio-temporal data from various sensors by IoT (Internet of things) technologies. Travel characteristics and future trends of highway behind data are valuable for traffic guidance and personalized service. As a routine domain analysis, trend discovery for highway drivers faces challenges in processing efficiency and predictive accuracy. Insufficient profiles of those drivers are available directly, sensible executive latency on huge data is hard to guarantee, and inadequate features among spatio-temporal correlations hinder the analytical accuracy. In this paper, a travel-characteristic based method is proposed to discover the potential trend of payment identity for highway drivers. Considering time, space, subjective preference and objective property, travel characteristics are modeled on monthly data from highway toll stations, through which predictive errors can be reduced by gradient boosting classification. With real-world data of one Chinese provincial highway network, extensive experiments and case studies show that our method has second-level executive latency with more than 85% F1-score for trend discovery.en_US
dc.description.sponsorshipThis work was supported by National Natural Science Foundation of China (No. 61702014), Beijing Municipal Natural Science Foundation (No. 4192020 and No. 4202021), Top Young Innovative Talents of North China University of Technology (No. XN018022), and “Yuyou” Talents of North China University of Technology (No. XN115013).en_US
dc.description.urihttps://link.springer.com/article/10.1007/s11276-020-02536-4en_US
dc.format.extent17 pagesen_US
dc.genrejournal articlesen_US
dc.genrepostprintsen_US
dc.identifierdoi:10.13016/m2ywyy-mlxj
dc.identifier.citationDing, W., Wang, Z., Chen, J. et al. Potential trend discovery for highway drivers on spatio‐temporal data. Wireless Netw 27, 3407–3422 (2021). https://doi.org/10.1007/s11276-020-02536-4en_US
dc.identifier.urihttps://doi.org/10.1007/s11276-020-02536-4
dc.identifier.urihttp://hdl.handle.net/11603/26198
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11276-020-02536-4en_US
dc.titlePotential trend discovery for highway drivers on spatio‐temporal dataen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170en_US

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