Brief Project Description:
Near miss traffic vehicle statistics are very informative for stake holders. However, since they are described via extreme statistics rather than averaging, they are significantly hard to capture. Using an integrated model-based and data-driven approach we examine the internal structure of traffic on a road segment, calculate the probability of deceleration to stop and subsequently relate that to the near-miss statistics.
Poster:
Fact Sheet Presentation Slides Other Q&A
Presenter: Arghavan Louhghalam, UMass Lowell, and Mazdak Tootkaboni, UMass Dartmouth
Contact: Arghavan Louhghalam, UMass Lowell, arghavan_louhghalam@uml.edu; Mazdak Tootkaboni, UMass Dartmouth, mtootkaboni@umassd.edu; Mohammad Pourghasemi Saghand, UMass Dartmouth, mpourghasemisaghand@umassd.edu