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Machine Learning and Optimization For Traffic and Emergency Resource Management.

Machine Learning and Optimization For Traffic and Emergency Resource Management. Milos Hauskrecht Department of Computer Science University of Pittsburgh. Students: Branislav Kveton, Tomas Singliar UPitt collaborators: Louise Comfort, JS Lin External: Eli Upfal (Brown), Carlos Guestrin (CMU).

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Machine Learning and Optimization For Traffic and Emergency Resource Management.

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  1. Machine Learning and Optimization For Traffic and Emergency Resource Management. Milos Hauskrecht Department of Computer Science University of Pittsburgh Students: Branislav Kveton, Tomas Singliar UPitt collaborators: Louise Comfort, JS Lin External: Eli Upfal (Brown), Carlos Guestrin (CMU)

  2. S-CITI related projects • Modeling multivariate distributions of traffic variables • Optimization of (emergency) resources over unreliable transportation network • Traffic monitoring and traffic incident detection • Optimization of distributed systems with discrete and continuous variables: Traffic light control

  3. S-CITI related projects • Modeling multivariate distributions of traffic variables • Optimization of (emergency) resources over unreliable transportation network • Traffic monitoring and traffic incident detection • Optimization of control of distributed systems with discrete and continuous variables: Traffic light control

  4. Traffic network • Traffic network systems are • stochastic (things happen at random) • distributed (at many places concurrently) • Modeling and computational challenges • Very complex structure • Involved interactions • High dimensionality PITTSBURGH

  5. Challenges • Modeling the behavior of a large stochastic system • Represent relations between traffic variables • Inference (Answer queries about model) • Estimate congestion in unobserved area using limited information • Useful for a variety of optimization tasks • Learning (Discovering the model automatically) • Interaction patterns not known • Expert knowledge difficult to elicit • Use Data Our solutions: probabilistic graphical models, statistical Machine learning methods

  6. Road traffic data • We use PennDOT sensor network155 sensors for volume and speed every 5 minutes

  7. Models of traffic data • Local interactions • Markov random field • Effects are circular • Solution: Break the cycles

  8. The all-independent assumption • Unrealistic!

  9. Mixture of trees • A tree structure retains many dependencies but still loses some • Have many trees to represent interactions

  10. Latent variable model • A combination of latent factors represent interactions

  11. Four projects • Modeling multivariate distributions of traffic variables • Optimization of (emergency) resources over unreliable transportation network • Traffic monitoring and traffic incident detection • Optimization of distributed systems with discrete and continuous variables: Traffic light control

  12. Optimizations in unreliable transportation networks • Unreliable network – connections (or nodes) may fail • E.g. traffic congestion, power line failure

  13. Optimizations in unreliable transportation networks • Unreliable network – connections (nodes) may fail • more than one connection may go down to

  14. Optimizations in unreliable transportation networks • Unreliable network – connections (nodes) may fail • many connections may go down together

  15. Optimizations in unreliable transportation networks • Unreliable network – connections (nodes) may fail • parts of the network may become disconnected

  16. Optimizations of resources in unreliable transportation networks • Example: emergency system. Emergency vehicles use the network system to get from one location to the other

  17. Optimizations of resources in unreliable transportation networks • One failure here won’t prevent us from reaching the target, though the path taken can be longer

  18. Optimizations of resources in unreliable transportation networks • Two failures can get the two nodes disconnected

  19. Optimizations of resources in unreliable transportation networks • Emergencies can occur at different locations and they can come with different priorities

  20. Optimizations of resources in unreliable transportation networks • … considering all possible emergencies, it may be better to change the initial location of the vehicle to get a better coverage

  21. Optimizations of resources in unreliable transportation networks • … If emergencies are concurrent and/or some connections are very unreliable it may be better to use two vehicles …

  22. Optimizations of resources in unreliable transportation networks • where to place the vehicles and how many of them to achieve the coverage with the best expected cost-benefit tradeoff ? ? ? ? ? ? ? ? ? ?

  23. Solving the problem A two stage stochastic program with recourse Problem stages: • Find optimal allocations of resources (em. vehicles) • Match (repeatedly) emergency demands with allocated vehicles after failures occur Curse of dimensionality: many possible failure configurations in the second stage Our solution: Stochastic (MC) approximations (UAI-2001, UAI-2003) Current: adapt to continuous random quantities (congestion rates,traffic flows and their relations)

  24. Four projects • Modeling multivariate distributions of traffic variables • Optimization of (emergency) resources over unreliable transportation network • Traffic monitoring and traffic incident detection • Optimization of distributed systems with discrete and continuous variables: Traffic light control

  25. Incident detection on dynamic data incident no incident incident

  26. Incident detection algorithms • Incidents detected indirectly through caused congestion • State of the art: California 2 algorithm • If OCC(up) – OCC(down) > T1, next step • If [OCC(up) – OCC(down)]/ OCC(up) > T2, next step • If [OCC(up) – OCC(down)]/ OCC(down) > T3, possible accident • If previous condition persists for another time step, sound alarm • Hand-calibrated for the specific section of the road Occupancy spikes Occupancy falls

  27. Incident detection algorithms Machine Learning approach (ICML 2006) • Use a set of simple feature detectors and learn the classifier from the data • Improved performance SVM based model California 2

  28. Four projects • Modeling multivariate distributions of traffic variables • Optimization of (emergency) resources over unreliable transportation network • Traffic monitoring and traffic incident detection • Optimization of control of distributed systems with discrete and continuous variables: Traffic light control

  29. Dynamic traffic management • A set of intersections • A set of connection (roads)in between intersections • Traffic lights regulating the traffic flow on roads • Traffic lights are controlled independently • Objective: coordinate traffic lights to minimize congestions and maximize the throughput

  30. Solutions • Problems: • how to model the dynamic behavior of the system • how to optimize the plans • Our solutions (NIPS 03,ICAPS 04, UAI 04, IJCAI 05, ICAPS 06, AAAI 06) • Model: Factored hybrid Markov decision processes • continuous and discrete variables • Optimization: • Hybrid Approximate Linear Programming • optimizations over 30 dimensional continuous state spaces and 25 dimensional action spaces • Goals: hundreds of state and action variables

  31. Thank you • Questions

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