Data

Motivated by the proliferation of Smartphone devices, the authors present a system architecture to enable the delivery and exchange of intelligent transportation systems (ITS) messages to Smartphones on the road. The purpose is to provide an ITS situation awareness system architecture that would enable application developers to build Smartphone applications for ITS mobility and safety. The authors present a “Slow Traffic Ahead” application as a means to validate the architecture and perform a field experiment in the San Francisco Bay Area comprised of 30 users to test the performance of...

Recently, transit agencies have begun opening their route configuration and schedule data to the public, as well as providing online application programming interfaces to real-time bus positions and arrival estimates. On the basis of this infrastructure for providing transit data over the Internet, the authors developed an algorithm to calculate the travel times of K shortest paths in a public transportation network where all wait and travel times were known only in real time. Although there was a large body of work on routing algorithms in transit networks, the authors took cues from an...

With the advent of ubiquitous mobile sensing and self-tracking groups, travel demand researchers have a unique opportunity to combine these two developments to improve the state of the art of travel diary collection. While the use of mobile phones and the inference of travel diaries from GPS and sensor data allows for lower-cost, longer surveys, we show how the self-tracking movement can be leveraged to interest people in participating over a longer period of time. By compiling personalized feedback and statistics on participants’ travel habits during the survey, we can provide the...

In this paper, the authors address the problem of determining the transportation mode at the trip level using mobile phones. The authors describe an algorithm that processes raw global positioning system (GPS) and accelerometer data to detect whether a person is standing, walking, biking, driving or taking public transit and further processes this data to determine the trip mode. This work contributes to existing research, which determines exact transportation modes at each time instant, by adding context to the mode data with trip definitions that are defined by the activity-based travel...