Intelligent Transportation Systems

Recent advances in mobile devices and internet technology have led Caltrans to investigate a data collection solution that offers improved data reliability and availability at a significantly lower cost. It has been postulated that information from GPS cell phones could provide position and speed data for highways and arterials in near real time over much of the transportation network. Position and speed data provided by mobile phones or other GPS enabled devices being transported in vehicles is normally referred to as probe data. The Mobile Millennium project was established to...

Traffic monitoring using probe vehicles with GPS receivers promises significant improvements in cost, coverage, and accuracy over dedicated infrastructure systems. Current approaches, however, raise privacy concerns because they require participants to reveal their positions to an external traffic monitoring server. To address this challenge, we describe a system based on virtual trip lines and an associated cloaking technique, followed by another system design in which we relax the privacy requirements to maximize the accuracy of real-time traffic estimation. We introduce virtual trip...

The "iShake" system uses smartphones as seismic sensors to measure and deliver ground motion intensity parameters produced by earthquakes more rapidly and accurately than currently possible. Shaking table tests followed by field trial with approximately 30 iShake users were implemented to evaluate the reliability of the phones as seismic monitoring instruments and the functionality of the iShake system. In addition, user experiences were investigated with 59 iShake users, who provided feedback through a mobile questionnaire. Research included participative planning with a focus group to...

This article presents a hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming GPS probe data. The model is based on a well-established theory of traffic flow through signalized intersections and is combined with a machine learning framework to both learn static parameters of the roadways (such as free flow velocity or traffic signal parameters) as well as to estimate and predict travel times through the arterial network. The machine learning component of the approach uses the significant amount of historical data collected by the Mobile...