Spatiotemporal route estimation consistent with human mobility using cellular network data

Continuous personal position information has been attracting attention in a variety of service and research areas. In recent years, many studies have applied the telecommunication histories of mobile phones (CDRs: call detail records) to position acquisition. Although large-scale and long-term data are accumulated from CDRs through everyday use of mobile phones, the spatial resolution of CDRs is lower than that of existing positioning technologies. Therefore, interpolating spatiotemporal positions of such sparse CDRs in accordance with human behavior models will facilitate services and researches. In this paper, we propose a new method to compensate for CDR drawbacks in tracking positions. We generate as many candidate routes as possible in the spatiotemporal domain using trip patterns interpolated using road and railway networks and select the most likely route from them. Trip patterns are feasible combinations between stay places that are detected from individual location histories in CDRs. The most likely route could be estimated through comparing candidate routes to observed CDRs during a target day. We also show the assessment of our method using CDRs and GPS logs obtained in the experimental survey.