AAAI 2012 Tutorial 22nd July 2012 Tutorial: Traffic Management and AI Biplav Srivastava, Anand Ranganathan IBM Research Toronto, Canada © 2012 IBM Corporation What to Expect: Tutorial Objectives (cid:1) The aim of the tutorial is to make early and experienced researchers aware of the traffic management area, provide – an insightful overview of the current efforts using AI techniques • in Research and • in practice (real world pilots), and – whet interest for newer efforts on important open issues. (cid:1) From the call for tutorial: “a second type of tutorial provides a broad overview for an AI area that potentially crosses boundaries with an interesting application area”. (cid:1) Disclaimer: we are only providing a sample of the traffic management space intended to match audience profile in the available time. 2 © 2010 IBM Corporation Outline 1. Traffic Management Problem 2. Instrumentation 1. Sensing traffic 2. Traffic state estimation 3. Optimizing and combining sensor data 3. Interconnection 1. Middleware 22.. TTrraaffffiicc ssttaannddaarrddss 4. Intelligence 1. Path planning 1. Simple Illustration 2. Path Planning for Individual Vehicles 2. End-user analytics 1. Bus arrival prediction and journey planning, with state-of-art instrumentation 2. Multi-modal journey planning, without sensors 5. Supporting topics 1. Traffic Simulators 2. Practical considerations for real-world pilots 3 © 2010 IBM Corporation Acknowledgements All our collaborators, and especially those in: (cid:1) City agencies around the world – Bengaluru, India – Boston, USA – Dublin, Ireland – Ho Chi Minh City, Vietnam – New Delhi, India –– NNeeww YYoorrkk,, UUSSAA – Stockholm, Sweden (cid:1) Academia (cid:1) IBM: Many including - Raj Gupta, Ullas Nambiar, Srikanth Tamilselvam, L V Subramaniam, Chai Wah Wu, Anand Paul, Milind Naphade, Jurij Paraszczak, Wei Sun, Laura Wynter, Olivier Verscheure, Eric Bouillet, Francesco Calabrese, Tsuyoshi Ide, Xuan Liu, Arun Hampapur, Nithya Rajamani, Vivek Tyagi, Raguram Krishnapuram, Shivkumar Kalyanraman, Manish Gupta, Niterndra Rajput, Krishna Kummamuru, Raymond Rudy, Brent Miller, Jane Xu, Steven Wysmuller, Alberto Giacomel, Vinod A Bijlani, Pankaj D Lunia, Tran Viet Huan, Wei Xiong Shang, Chen WC Wang, Bob Schloss, Rosario Usceda-Sosa, Anton Riabov, Magda Mourad For discussions, ideas and contributions. Apologies to anyone unintentionally missed. 4 © 2010 IBM Corporation AAAI 2012 Tutorial July 2011 Traffic Management and AI Section: Traffic Problem Speaker: Biplav Srivastava Toronto, Canada © 2012 IBM Corporation Outline 1. Traffic Management Problem 2. Instrumentation 1. Sensing traffic 2. Traffic state estimation 3. Optimizing and combining sensor data 3. Interconnection 1. Middleware 22.. TTrraaffffiicc ssttaannddaarrddss 4. Intelligence 1. Path planning 1. Simple Illustration 2. Path Planning for Individual Vehicles 2. End-user analytics 1. Bus arrival prediction and journey planning, with state-of-art instrumentation 2. Multi-modal journey planning, without sensors 5. Supporting topics 1. Traffic Simulators 2. Practical considerations for real-world pilots 6 © 2010 IBM Corporation We All See Traffic Daily. An Illustration from Across the Globe Characteristics New York New Delhi, Beijing, China Moscow, Ho Chi Minh City, Sao Paolo, Brazil City, USA India Russia Vietnam 1 How is traffic pre- Automated Manual Automated Automated, Manual control Automated, manual dominantly managed control, manual control control, manual manual control control, Rotation control control system (# plate based) 2 How is data collected Inductive Traffic Video, GPS, cops GPS, some Traffic surveys, cops Video, GPS, cops loops, cops, surveys, cops video, cops video, GPS 3 How can citizens manage GPS devices, Alerts on alerts on radio, GPS, radio, Alerts on radio GPS devices, alerts their resources alerts on radio, radio road signs road signs, on radio, web web, road signs (variable), mobile mobile alerts (variable) alerts 4 Traffic heterogeneity by Low High Low Low Medium Low vehicle types(Low: <10; Medium 10-25; High: >25) 5 Driving habit maturity High Low Low Low Low Medium (Low: <10 yrs; Medium: 10-20; High: > 20) 6 Traffic movement Lane driving Chaotic Lane driving Lane driving Chaotic Lane Driving Source: Google map for New York City and New Delhi; Search done on Aug 20, 2010 © 2010 IBM Corporation Is it Just Supply-Demand Mismatch? (cid:1) Supply – Roads and their capacity – Personnel available – Capital and operational budget (cid:1) Demands –– TTrraavveell nneeeeddss ooff cciittiizzeennss – Travel needs of organizations: businesses, governments (cid:1) Solution to mis-match? – Keep building supply (roads, bridges, …) – Keep reducing demand (restrict citizens, businesses, …) – May work for some of the cities, for some of the time, • but not for all of the cities and all of the time 8 © 2010 IBM Corporation Levers in Cities’ Hands for Traffic Management (cid:1) Physical infrastructure – New flyovers, roads – Expanding existing capacity – New metros, … (cid:1)(cid:1) PPoolliicciieess – Relocating businesses – Incentivising public transport (cid:1) IT enabled technologies – Intelligent Traffic/ Transportation Systems – Social networking © 2010 IBM Corporation (a) 3 km trip (b) 6 km trip Car (car + motorcycle) takes less time regardless of distance unless there is congestion on road. See speaker notes Which Mode of Transport is for more details. Best for a Particular Distance (c) 12 km trip (d) 24 km trip –Door-to-Door Trip Times Data used: •international data on things like access time to public transportation was taken •Studies were done for Delhi Metro •Minimum assumptions were made. •E.g., walking time for metro access (5 mins) is widely acceptable •See speaker notes too © 2010 IBM Corporation Source: Mythologies, Metros& Future UrbanTransport, by Prof. DineshMohan, TRIPP, 2008