We address the problem of tracking vehicles from received signal strength (RSS) sequences generated by mobile phones carried in them. Our main objectives are to provide travel-time estimates for selected roads and provide personal navigation assistance when GPS is unavailable or undesirable. 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. In Chapter 3, we propose local alignment of mobile phone RSS measurements to track vehicles. We use local alignment instead of the traditionally used global alignment to allow for vehicles changing roads. More specifically, we use local dynamic time warping to align the RSS sequence of a phone, to a reference sequence that we had collected for the relevant road. Due to fluctuations in RSS levels and other effects, even at the same location, the set of cell towers reported in a fingerprint and their reported RSS levels vary over time. To model these variations, in Chapter 4.1, we propose a complete observation 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 with a Dynamic Bayesian Network to track vehicles. Unlike traditional observation models, which model only the variation of the RSS levels, we model the variation of the set of cells reported in fingerprints as well. Accurate estimation of the parameters of either traditional or our complete observation model requires recording fingerprints by driving on the roads of interest, which is tedious and expensive. Therefore, to avoid such driving, we propose unsupervised learning in Chapter 5 to estimate model parameters using RSS sequences of phone calls made by road-users. Experiments with RSS data collected on five roads demonstrate that our proposed algorithms produce lower errors than relevant existing methods. Furthermore, application of our algorithms to real subscriber call traces produced travel-time estimates for a given road segment that were, on average, within 13% - 14% of travel-times computed through license plate recognition.