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A Sensing Coverage Analysis of a Route Control Method for Vehicular Crowd Sensing

Simulated evaluation of crowd sensing with vehicles for a Smart City. Route cordination of sensing vehicles is a key to enhance sensing coverage of participatory crowd sensing system. We provide a simple methodology to realize suitable cordinated traffic control method by means of shortest cost finding with dedicated cost function aware of sensing demand in a city.

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A Sensing Coverage Analysis of a Route Control Method for Vehicular Crowd Sensing

  1. 1. CASPer 2015 A Sensing Coverage Analysis of a Route Control Method for Vehicular Crowd Sensing Mar 27,2015 Osamu Masutani Chief Engineer, Denso IT Laboratory, Inc. Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 1
  2. 2. Summary Concept Vehicular crowd sensing for city monitoring Methodology Sensing coverage of city monitoring Route finding methods for crowd sensing Evaluation Conclusion & future work Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 2
  3. 3. Concept Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 3
  4. 4. Concept : Vehicular Crowd Sensing for a smart city Major topics of smart city  Energy efficiency for sustainable economy  Cost effective and resilient infrastructure Contribution of vehicles  Efficient traffic control  Crowd sensing by vehicles Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 4 Efficient traffic City monitoring smart city Transportation sector
  5. 5. Vehicle as a powerful sensor A vehicle has huge potential for crowd sensing  Many kinds of in-vehicle sensors  Advanced environmental sensors  Stereo camera, laser rader, milliwave rader Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 5 Denso Technical Review https://www.denso.co.jp/ja/aboutdenso/technology/dtr/v17/files/10.pdf http://www.embedded.com/print/4011081 Smart phones Vehicle 6th Gen iPhone 3rd Gen Prius Sensors <10 Cameras, Accelerometer, Mic, Proximity … 100 Physical, thermal, electric … Processors 1 CPU(2 cores), 1 GPU(4 cores) 70 ECUs Battery 6.7 Wh (1810 mAh@3.7V) 1.3 kWh http://www.car-electronics.jp/files/2012/10/CurrentStateOfIn- vehicleMicrocomputer.pdf
  6. 6. Floating car to Vehicular crowd sensing Floating car systems monitor these phenomena in a city :  Traffic monitoring (congestion, incident) : GPS tracking data  Road condition monitoring (ice) : ABS, road monitoring sensor  Weather monitoring (precipitation) : wiper Vehicular crowd sensing (VCS)  Try to contribute “for a city” rather than “for a drivers“  Wider range of usage Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 6 Environment (pollution, noise) Facility Maintenance (bridge, tunnel) City Mapping (road, building) Public Security (crime, disaster) City monitoring with VCS
  7. 7. Key performance indices for vehicular crowd sensing Quality of data (Accuracy)  Quality of sensors Quantity of data (Coverage)  Number of sensors  Boost the area simultaneously observed  Route of sensors  Track efficient route to visit sensing target  The routes should not be redundant among multiple sensors Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 7 Number of sensors Route (orbit) of sensors
  8. 8. Coverage enhancement of vehicular crowd sensing Number of sensors  Base traffic amount * Participating rate  Enhanced by penetration strategy (enforcement, incentive) Route of sensors  Efficiently track sensing demand in a city  Enhanced via traffic control  Center based navigation  Fleet management  Managed self driving car Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 8 Number of sensors Route of sensors
  9. 9. Methodology Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 9
  10. 10. Definition of sensing demand in a city Sensing demand in a city varies :  In space  In time Three categories of demand :  Uniform : weather, road condition  Static : facility (bridges, tunnels)  Dynamic : crime, traffic Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 10 UNIFORM STATIC DYNAMIC
  11. 11. Evaluation index of sensing coverage Sensing Demand  Defined on each road link  Binary demand (exist or not)  Fully satisfied when the sensing vehicle pass the link Coverage : Demand Satisfaction  How much percentage the demand satisfied in space and time  Varies from 0 (fully satisfied) to 1 (not satisfied) Travel Time  The time taken to destination Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 11 Link Sensing Demand demandlevel 0 1 Not satisfied Satisfied Travel time
  12. 12. Traffic control aware of sensing demand Modification of shortest route in order to pass sensing demand  Make detour to satisfy sensing demand Default route finding  Distance link cost or time cost The cost aware of sensing demand  The link cost is decreased as much as sensing demand  The route is attracted to the sensing demand. Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 12 Sensing demand Default route New route link cost demand
  13. 13. Route reservation to avoid concentration of traffic Traffic concentration to sensing demand  Redundant sensing when multiple vehicle visit at once Solution : route reservation  Each vehicle reserves route before it arrives  Find optimal route according to number of reservations for each links. Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 13 RESERVED RESERVED
  14. 14. Route Reservation Reservation is managed in traffic management center  Each link has reservation slot  Reservation aware route finding is performed in traffic center  All of sensing vehicle follow the route Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 14 link cost demand
  15. 15. Evaluation Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 15
  16. 16. Available on : Evaluation environment “Metro traffic simulator” – simple micro simulation workbench  Car following model  Shortest route search  Grid and OSM based maps Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 16
  17. 17. Result summary Uniform sensing demand : previous work Static sensing demand  For coarse sensing demand, simple sensing demand cost would work.  For higher traffic density, combination with route reservation would work  For longer route, reservation should be considered time slot Dynamic sensing demand  Route reservation with time slot would work Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 17
  18. 18. 0 Uniform sensing demand Reservation has two appropriate effects  Coverage extension  Use alternative routes effectively  Reduction of traffic congestion  Avoid traffic concentration before jam occurs These effects realize higher coverage without travel time extension Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 18 Distance cost Travel time cost Reservation cost Link ID time Previous work : Masutani, O. A proactive route search method for an efficient city surveillance. 21th World Congress on ITS, (2014).
  19. 19. Common setting Map  10 * 10 grid (50m pitch)  10 origin to 10 destination (100 combination)  Updated once in 30 second Sensing demand  Binary sensing demand  Random distribution Simulation duration  20,000 sec Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 19
  20. 20. 1-1 Static Sensing Demand Sensitivity analysis on demand density Three route finding methods  Distance  Travel time  Sensing demand aware Result  For coarse demand, simple sensing demand cost would gain extra coverage.  For dense demand, distribution is similar to uniform case -> previous work Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 20 Advantage in coarse demand Coverage Density Distance Travel time Demand aware
  21. 21. 1-1 Analysis De-tour occurred ? Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 21 coarse moderate dense sensing HIGH selective LOW bound by # of vehicles LOW bound by # of vehicles travel time LOW small detour occurred HIGH much detour occurred LOW don’t need to detour TraveltimeCoverage Density Density Demand aware Demand aware
  22. 22. 1-2 High traffic volume case - reservation Reservation avoid concentration Reservation technique can extend coverage even in higher traffic Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 22 Demand aware Demand aware + reservation Illustrati on Demand aware Excess demand aware Reservation Coverage Traffic volume
  23. 23. 1-2 Effect of reservation cost Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 23 Sensing Demand only Sensing Demand + Reservation 1 2 3 1 2 3 1 2 3 1 2 3 never visited visited
  24. 24. 1-3 Longer route case – predictive reservation Reservation deteriorate when map size is increased  Caused by excess reservations which is not actually necessary  “time slot” of reservation to avoid excess reservation Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 24 current reservation predictive reservation Demand aware Reservation Reservation w/ time slot Map size Coverage : sensing demand
  25. 25. 1-3 Effect of predictive reservation Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 25 1 2 3 1 2 3 1 2 3 1 2 3 Sensing Demand + Reservation Sensing Demand + Reservation w/time slot
  26. 26. 2 Dynamic demand Predictive demand  Known demands on future Time slot work  Only confirmed in reciprocal dynamic demand Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 26 PD PD with current reservation PD with predictive reservation Predictive Demand aw Reservation Reservation w/ time s : sensing demand
  27. 27. Conclusion and Future work Sensing demand and reservation aware route finding  Enhance coverage without extending much travel time  Detour is not zero : need some kind of incentive is needed.  Easily integrated to current center-based navigation Future work  More realistic evaluation : real traffic, participation rate  Optimization technique to maximizing coverage Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 27
  28. 28. Optimization approaches Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 28 Navigation System Vehicular crowd sensing Collaborative routing Fleet Management Traffic Management Small traffic / microscopic Low penetration rate Dedicated vehicles Maintain quality of service Large traffic / macroscopic High penetration rate General vehicles Maintain user equilibrium
  29. 29. Thank you for your attention ! Any questions ? Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 29
  30. 30. Appendix Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 30
  31. 31. Travel time for each evaluation Travel time doesn’t extend in each setting. Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 31 Map size Traveltime Traveltime Traffic demand Evaluation 1-2 Evaluation 1-3

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Simulated evaluation of crowd sensing with vehicles for a Smart City. Route cordination of sensing vehicles is a key to enhance sensing coverage of participatory crowd sensing system. We provide a simple methodology to realize suitable cordinated traffic control method by means of shortest cost finding with dedicated cost function aware of sensing demand in a city.

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