Traffic monitoring during extreme [ News Gazette , 12] congestion - PDF document

6/7/13 Traffic monitoring during extreme [ News Gazette , ‘12] congestion events Dan Work Assistant Professor, Civil and Environmental Engineering & Coordinated Science Laboratory University of Illinois at Urbana–Champaign 1 Limitations of current systems • Surface streets – Sparsity of sensing – Limited (but increasing) GPS data from mobile devices • Rely on statistical algorithms – Heavily influenced by historical priors 2 2 1

6/7/13 Extreme congestion events • Event driven congestion – Sporting events – Natural disasters • Impact on transportation infrastructure – Network topology changes – Damage to physical components – Loss of cyber components – Change in travel demands Need for cheap, instantly deployable (temporary) sensing [A. Savulich, New York Daily News, 2012] 3 3 TrafficTurk smartphone app When a vehicle passes the intersection, swipe its movement on the screen. 4 4 2

6/7/13 Inspiration for TrafficTurk Amazon’s Mechanical Turk The mechanical Turk Turning movement counters (Transportation’s Mechanical Turk) 5 5 100+ sensors deployed to monitor football traffic 220,000+ vehicles swiped 140 volunteers 6 6 3

6/7/13 7 7 TrafficTurk Experiment - NYC • Hurricane Sandy – November 3 and 4, 2012 • Garment District, Manhattan • Overnight map deployment 10+ hours monitoring • Recruitment at Columbia University • Real disaster response experience [NSF RAPID # 1308842] [ Scientific American Citizen Science featured project ‘12] 8 8 4

6/7/13 Processing techniques: Phase Inference via Hidden Markov Modeling • Goal: identify traffic signal phases from maneuver data • Motivation: Phase6 – Recovery of traffic phase timings Phase5 – Simplified TrafficTurk user interface Phase4 Phase real estimated Phase3 Phase2 Phase1 0 100 200 300 400 500 600 Time(sec) [M. Reisi Gahrooei & D. Work, IEEE ITSC 13] 9 9 Processing techniques: Inverse optimal traffic signal control • Goal: recover traffic signal control logic via learning on the cost function • Motivation: – Flow model forecasting on surface streets – Limited information on existing infrastructure (none at large scales) – Human traffic control [S. Gowrishankar & D. Work, IEEE ITSC 13] 10 10 5

6/7/13 Next steps • Processing TrafficTurk data for NY (phase detection controller detection) • Integration into real-time traffic estimation algorithms • Acquiring (FOIL) NYC GPS taxi data pre and post Sandy. 11 11 Traffic monitoring during extreme [ News Gazette , ‘12] congestion events Dan Work Assistant Professor, Civil and Environmental Engineering & Coordinated Science Laboratory University of Illinois at Urbana–Champaign 12 6