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Proactive vehicle re-routing strategies for congestion avoidance

Juan (Susan) Pan * , Mohammad A. Khan * , Iulian Sandu Popa + , Karine Zeitouni + and Cristian Borcea * * New Jersey Institute of Technology, USA + University of Versailles Saint-Quentin-en-Yvelines, France DCOSS 2012 . Proactive vehicle re-routing strategies for congestion avoidance.

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Proactive vehicle re-routing strategies for congestion avoidance

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  1. Juan (Susan) Pan*, Mohammad A. Khan*, IulianSanduPopa+, KarineZeitouni+ and CristianBorcea* *New Jersey Institute of Technology, USA +University of Versailles Saint-Quentin-en-Yvelines, France DCOSS 2012 Proactive vehicle re-routing strategies for congestion avoidance

  2. Traffic congestion: an ever-increasing problem • In 2010, congestion caused urban Americans a cost of $101 billions • By 2015, this cost will rise to $133 billion and the amount of wasted fuel will jump to 2.5 billion gallons • Increase road capacity? • Optimize traffic signal control? • Provide traffic guidance to drivers?

  3. Congestion avoidance using mobile sensing and actuation (1) • Smart phones (mobile sensors) & road-side sensors monitor traffic at fine granularity • Mobile sensors can be vehicular embedded systems • Road-side sensors: loop detectors, cameras, etc • Demonstrated by other researchers • Traffic management service (TMS) collects data and estimates congestion in real-time

  4. Congestion avoidance using mobile sensing and actuation (2) • TMS provides real-time, proactive, individually-tailored re-routing guidance to drivers to prevent congestion • Drivers provide their origin-destination information • Guidance is pushed to drivers’ smart phones when signs of congestion are observed on their current route • Drivers may or may not follow the guidance • The main focus of our research

  5. Comparison with existing work (1) • Google, Microsoft & Inrix: real time traffic info to compute traffic-aware shortest routes • Reactively provide same guidance for all drivers • Problem: move congestion from one spot to another • Similar to route oscillation in computer networks

  6. Comparison with existing work (2) • Transportation researchers proposed dynamic traffic assignment models • Iteratively calculate shortest paths and assign routes to each driver to achieve the user/system equilibrium • Example of systems: DynaMIT, Contram • Problems • Tractability at scale (providing real-time guidance) • Ability to work when not all drivers are part of the system • Robustness to drivers who ignore the guidance

  7. Outline • Motivation & related work • Our 3 proactive re-routing strategies • Simulation results • Conclusion and future work

  8. The 4 phases of re-routing • Road network represented as graph, with estimated travel time as edge weight • Travel time estimation • Greenshield’s model for travel time estimation • Traffic congestion estimation • Density greater than threshold (δ=0.7) • Selection of candidate vehicles for re-routing • Re-routing: alternative route computation and assignment to drivers

  9. Selection of candidate vehicles for re-routing Step 1:Detect road segments with signs of congestion Step 2: Recursively select incoming segments to “congested” segment until depth L Blue: 1st level incoming segments Green: 2nd level incoming segments Step3: Select vehicles on these road segments

  10. Our 3 re-routing strategies • Dijkstra’s Shortest Path (DSP) • Computes one single shortest path for each driver • Potential to switch congestion from one spot to another • Random K Shortest Paths (RkSP) • Compute k shortest paths for each driver and randomly pick one • Solves DSP problem, but could be far from optimal • Entropy Balanced K Shortest Paths (EBkSP) • Prioritize candidate vehicles • Compute k shortest paths for each driver and pick the one with least popularity • Improves on RkSP by choosing better paths

  11. EBkSP popularity entropy Def: the weighted footprint counterfciof a road segment i is: fci =niхwi ni is the number of vehicles that are assigned to paths that include this segment, and wiis the weight of the road segment • Let (p1,…, pk) be the set of k paths that will be assign next • Let (r1,…, rn) be the union of all segments of (p1,…, pk), and (fc1,…,fcn) be the set of weighted footprint counters • Def: Entropy(pj) is the entropy of pjand is computed as • Def:

  12. EBkSP example ? a b c d e g j f h i k V1, assignedpath (ab, bg,gh, hi, ij ) V2, assignedpath (fg, gh, hi, ij ) V3, assigned path (ch, hk ) P1 (ab, bg, gh, hi, ij) fc(P1)(1,1,2,2,2) entropy=1.49 P2 (ab, bc, ch, hi, ij) fc(P2)(1,0,1,2,2)  entropy=1.16 P3 (ab, bc, cd, di, ij) fc(P3)(1,0,0,0,2)  entropy=0.58

  13. Evaluation • Goals • Effectiveness average travel time • Users’ experience  number of re-routings • Robustness average travel time as function of driver compliance rate & system penetration rate • Real-time CPU time • Simulation setup • SUMO-0.13 microscopic simulator, open source, car following model • TRACI python library  send commands to SUMO to assign new paths Brooklyn (1000 vehicles) Newark (908 vehicles)

  14. Average travel time • All strategies improve average travel time significantly • EBkSP has the best performance • Compared to “no-reroute” reduces the travel time by up to 81% and 104% • Compared to DSP, it is better by 15% and 25%

  15. Average number of re-routings • EBkSP has least number of re-routings • Less distraction to drivers

  16. Average travel time vs. compliance and penetration rate • Performance increases with compliance rate up to a point • Low compliance still much better than “no-reroute” • Performance increases with penetration rate • With road-side sensors help to detect vehicular density, low penetration rate still much better than “no-reroute”

  17. CPU time • DSP lowest CPU time • Dijkstra lower complexity than k shortest path • O(E + V log(V )) vs. O(kV (E+V log(V ))) • EBkSP better than RkSP • Fewer origin-destination pairs due to better re-routing

  18. Conclusion & future work • All re-routing strategies decrease significantly the average travel time • EBkSP is the best—careful path selection • 104% improvement compared to the “no rerouting” baseline • Lowers with 34% the number of re-routings • Good improvements are observed even for relatively low penetration/compliance rates • To improve scalability and real-time response, we plan to work on hybrid system architectures • Offload part of computation to mobile nodes • Use ad hoc communication in addition to Internet communication

  19. Thank you !Acknowledgment: NSF Grant CNS-0831753http://www.njit.edu/~borcea/invent/

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