In this thesis, we develop methods for modeling route choice behavior using smartphone data. The developing global positioning system (GPS) technology and the popularity of smartphones have revolutionized the revealed preference route choice data collection. Nowadays, smartphones are embedded with various kinds of sensors that are able to provide mobility related information. These sensors include GPS, accelerometer and bluetooth. The recorded raw data is not directly applicable to travel behavior study, information such as the paths and transport modes of travels have to be inferred. The inference procedure is challenging due to the poor quality and the variety of the data. This thesis deals with these challenges by proposing probabilistic methods that account for errors in the data, and fusing various kinds of smartphone data in an integrated framework. Based on the inference methods, a route choice modeling framework exploiting GPS data is developed. The low cost sensors of smartphones observe measurements with significant errors. Moreover, due to practical constraints, such as the limits on smartphone battery volume and the cost of data transmitted via wireless networks, data are usually recorded in a relatively large time interval (low frequency). These drawbacks preclude path identification (a.k.a. map-matching, MM) algorithms that are designed for dense and accurate data from dedicated GPS devices. Therefore, we first propose a probabilistic unimodal MM method that infers the traveled paths from GPS data recorded during a car trip. Instead of deterministically matching a sequence of GPS points to one path, it generates a probabilistic path observation which is composed of a set of candidate paths, and a measurement likelihood for each path. The candidate paths are generated by a candidate path generation algorithm from GPS data. It is capable of dealing with both accurate and dense data (1 second interval) from dedicated GPS devices, and noisy and sparse data (more than 10 seconds interval) from smartphones. A probabilistic measurement model is constructed to calculate the measurement likelihood, which is the likelihood that the observed GPS data is recorded along a given path. The probabilistic measurement model employs structural equation modeling techniques, and the latent status for each measurement is defined as the true location where the measurement is observed. A GPS sensor measurement model relates the status to each GPS measurement; a structural travel model captures the status over time in the network. In this approach, besides geographical coordinates, speed and time recorded from GPS also contribute to the identification of the true path. Applications and analyses on real data illustrate the robustness and effectiveness of the proposed approach. Based on the framework designed for the unimodal MM, a multimodal MM method is developed to deal with a more general problem where the trips can be multimodal and the modes are unknown. We infer both path and mode information simultaneously from various kinds of data. The candidate path generation algorithm is extended to deal with multimodal networks, and to generate multimodal paths, of which a transport mode is associated with each road. The latent status includes both location and mode, and the correlation between them is exploited. For example, if the mode is bus, the path should follow bus routes. Besides the most useful GPS data, acceleration and bluetooth also contribute mobility information, so they are integrated in the probabilistic measurement model by constructing a sensor measurement model for each. ACCEL provides motion status that can be used to infer the transport mode. BT data gives the amount of nearby BT devices, which can be used to recognize, for instance, a public transport environment if there are a lot of BT devices nearby. This approach is flexible in two aspects. First, any kind of sensor data can be integrated as long as a corresponding sensor measurement model is provided. Second, any transport network can be added or removed according to necessity and availability. Data recorded from a trip does not need to be preprocessed into unimodal travel segments, so the risk of wrong segmentation is attenuated. Numerical experiments include map visualizations of some example trips, and an analysis of the performance of the transport mode inference. In the last part of the thesis, we develop a comprehensive and operational route choice modeling framework for estimating route choice models from GPS data. It integrates three components: the probabilistic unimodal MM method for generating probabilistic path observations from GPS data; the “network-free” data approach proposed by Bierlaire & Frejinger (2008) for estimating route choice models from probabilistic path observations; and a new importance sampling based algorithm for sampling path alternatives for the choice model estimation. The proposed path sampling algorithm produces more relevant alternatives by exploiting the GPS data. Numerical analyses using a real transportation network and synthetic choices empirically show that the proposed path sampling algorithm yields more precise parameter estimates than other importance sampling algorithms. The proposed framework accounts for the imprecision in GPS data. The necessary modifications of each method for GPS data are presented. A route choice model estimated from smartphone GPS data shows the viability of applying the proposed route choice modeling framework with real data.