Trajectory Data Acquisition via Private Car Positioning Based on Tightly-coupled GPS/OBD Integration in Urban Environments

Zhu Xiao, Yanxun Chen, Mamoun Alazab, Hongyang Chen

    Research output: Contribution to journalArticlepeer-review


    The explosive growth of road vehicles especially the private cars has brought unprecedented pressure to a series of problems in urban transportation systems, such as traffic congestion and environmental pollution. Private cars trajectory data and perceiving their information provide a promising solution to these problems. However, the collection of large-scale trajectory data for private cars with high accuracy and reliability is still delicate tasks in urban environments. In this paper, we propose a low-cost and user-friendly implementation method for achieving large-scale private cars trajectory data acquisition via designing lightweight GPS module and On Board Diagnostics (OBD) reader. To ensure reliable trajectory data acquisition via GPS/OBD integration, we propose an ensemble learning based Gauss Process Regression (GPR) method so as to cope with the non-linearity, non-stationarity and incremental training problems during trajectory collection. We design a classification-type loss (CTL) function and build a regression to classification (R2C) method with Learn++ for realizing ensemble learning. The proposed approach implements incremental learning when new trajectory data arrives and is able to resolve the concept drifting problem. Experiments in real-world urban environment have demonstrated the effectiveness and reliability of the proposed method, it achieves better trajectory prediction performance than the comparative methods under various road conditions in GPS-denied areas.

    Original languageEnglish
    Article number9680
    Pages (from-to)9680-9691
    Number of pages12
    JournalIEEE Transactions on Intelligent Transportation Systems
    Issue number7
    Early online dateAug 2021
    Publication statusPublished - 1 Jul 2022


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