Project Info

Crowdsourcing-assisted Prediction of Traffic Conditions

Qi Han | qhan@mines.edu

Road safety and congestion are a formidable challenge for communities, with recent increases in the number of fatalities and commuting times. Incident management practices are largely reactive in response to road user reports. However, if incident likelihood could be predicted, cities could proactively deploy assets and manage traffic. This would reduce emergency response times, saving lives, and minimizing disruptions to traffic. This project aims to radically transform traffic management, emergency response, and urban planning practices via predictive analytics on rich data streams from increasingly prevalent instrumented and connected vehicles, infrastructure, and people. For the MURF project, the student will be working on pre-processing of different types of data including Waze data and GIS road network data, learn how to use DynusT, a large-scale mesoscopic vehicular traffic simulation and assignment model and determine how to best interface with our system.

1. My website:
https://pecs.mines.edu/projects/

Grand Engineering: Restore and improve urban infrastructure