Mobile Device Data Analytics for Next-Generation Traffic Management

Quality data is critically important for research and policy-making. The availability of device location data carrying rich, detailed information on travel patterns has increased significantly in recent years with the proliferation of personal GPSenabled mobile devices and fleet transponders. However, in its raw form, location data can be inaccurate and contain embedded biases that can skew analyses. This report describes the development of a method to process, clean, and enrich location data. Researchers developed a computational framework for processing large scale location datasets. Using this framework several hundred days of location data from the San Francisco Bay Area was (a) cleaned, to identify and discard inaccurate or problematic data, (b) enriched, by filtering and annotating the data, and (c) matched to links on the road network. This framework provides researchers with the capability to build link-level metrics across large scale geographic regions. Various applications for this enriched data are also discussed in this report (including applications related to corridor planning, freight planning, and disaster and emergency management) along with suggestions for further work.