01698188

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Cellular probe data, which is collected by cellular network operators, has emerged as a critical data source for human-trace inference in large-scale urban areas. However, because cellular probe data of individual mobile phone users is temporally and spatially sparse (unlike GPS data), few studies predicted people-flow using cellular probe data in real-time. In addition, it is hard to validate the prediction method at a large scale. This paper proposed a data-driven method for dynamic people-flow prediction, which contains four models. The first model is a cellular-probe data pre-processing module, which removes the inaccurate and duplicated records of cellular data. The second module is a grid-based data transformation and data integration module, which is proposed to integrate multiple data sources, including transportation network data, point-of-interest data, and people movement inferred from real-time cellular probe data. The third module is a trip-chain based human-daily-trajectory generation module, which provides the base dataset for data-driven model validation. The fourth module is for dynamic people-flow prediction, which is developed based on an online inferring machine-learning model (Random Forest). The feasibility of dynamic people-flow prediction using real-time cellular probe data is investigated. The experimental result shows that the proposed people-flow prediction system could provide prediction precision of 76.8% and 70% for outbound and inbound people, respectively. This is much higher than the single feature model, which provides prediction precision around 50%.

This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.

19-02689

English

25p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Cellular telephones; Data collection; Flow; Machine learning; Probes (Measuring devices); Traffic flow; Travel demand; Trip chaining; Urban areas

China

Data and Information Technology; Highways; Pedestrians and Bicyclists; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2019 Paper #19-02689

TRIS, TRB, ATRI

Dec 7 2018 9:48AM