A Complete Observation Model for Tracking Vehicles from Mobile Phone Signal Strengths and Its Potential in Travel-Time Estimation

Tracking vehicles from mobile phones has applications, among others, in traffic monitoring, location-based services, and personal navigation. We address the problem of tracking vehicles from received signal strength (RSS) sequences generated by mobile phones carried by passengers. A mobile phone periodically measures the RSS levels from the associated cell tower and several (six for GSM) strongest neighbor cell towers. Each such measurement is known as an RSS fingerprint. However, due to various effects, the contents of fingerprints may vary over time even when measured at the same location. These variations have two components. First is the fluctuation of the RSS levels. Second is the variation of the set of cell towers reported in fingerprints. The latter is not properly modeled by traditional methods. To address both components of variation, we propose a probabilistic model for RSS fingerprints that specifies for each gird-location in the area of interest, the distribution of the probability of observing any fingerprint at that location. We then use it as the observation model of a Dynamic Bayesian Network to track vehicles. Experiments on several roads demonstrate a 40% reduction in average error with our method compared to its traditional counterparts. Using RSS sequences of phone calls made by road users, our algorithm produced better travel-time estimates than comparison methods for a selected road segment with an average error of 13% with respect to travel-times computed through manual license plate recognition.