Lane-based road network information, such as the number and locations of traffic lanes on a road, has played an important role in intelligent transportation systems. In this paper, we propose a Collecting Lane-based Road Information via Crowdsourcing (CLRIC) method, which can automatically extract detailed lane structure of roads by using crowdsourcing data collected by vehicles. First, CLRIC filters the high-precision GPS data from the raw trajectories based on region growing clustering with prior knowledge. Second, CLRIC mines the number and locations of traffic lanes through optimized constrained Gaussian mixture model. Experiments are conducted with taxi GPS trajectories in Wuhan, China, and the results show that CLRIC is quantified and displays detailed road networks with the number and locations of traffic lanes comparing with the satellite image and human-interpreted situation.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing




    Erscheinungsdatum :

    2016




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



    CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing

    Tang, Luliang / Yang, Xue / Dong, Zhen et al. | IEEE | 2016


    Crowdsourcing the collection of road surface information

    FRIDMAN OFER | Europäisches Patentamt | 2021

    Freier Zugriff

    CRSM: Crowdsourcing Based Road Surface Monitoring

    Chen, Kongyang / Lu, Mingming / Tan, Guang et al. | IEEE | 2013


    Efficient Lane-Level Map Building via Vehicle-Based Crowdsourcing

    Shu, Jiangang / Wang, Songlei / Jia, Xiaohua et al. | IEEE | 2022


    Crowdsourcing-Based Road Surface Evaluation and Indexing

    Daraghmi, Yousef-Awwad / Wu, Tsung-Hsiang / Ik, Tsi-Ui | IEEE | 2022