Route Coverage Analysis Vehicular Crowd Sensing
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The document proposes and evaluates route control methods for vehicular crowd sensing to maximize sensing coverage of a city. It presents three key ideas: (1) modifying vehicle routes to pass through areas of high sensing demand, (2) reserving routes to avoid traffic concentration, and (3) using predictive reservations for longer routes. The methodology and evaluation show that these methods can enhance coverage without significantly increasing travel time, especially for static and uniform demands. Future work includes optimization techniques and more realistic simulations.
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Route Coverage Analysis Vehicular Crowd Sensing
- 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. 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. Concept Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 3
- 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. 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. 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. 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. 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. Methodology Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 9
- 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. 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. 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. 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. 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. Evaluation Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 15
- 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Thank you for your attention ! Any questions ? Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 29
- 30. Appendix Copyright (C) 2015 DENSO IT LABORATORY,INC. All Rights Reserved. 30
- 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
Notas del editor
- Hello everyone I am Osamu Masutani from Denso IT Laboratory, Inc. in Japan. Our company is a subsidiary company of Denso which is a automotive component manufacturer. Our mission is developing and evaluating intelligent transportation system (ITS). Today I’ll present you topic “A Sensing Coverage Analysis of a Route Control Method for Vehicular Crowd Sensing”.
- Here is a summary of today’s presentation. First of all I’ll show you the fundamental concept of this project. Then I’ll show you methodologies [which are sensing coverage for city monitoring and route finding methods for crowd sensing]. Then evaluation result of how they work well. Finally I’ll conclude and show future work
- There’s 2 [major topics of smart city] related with this work. One is [energy efficiency for sustainable economy]. The other is [flexible and resilient infrastructure]. We think [the contribution of vehicles] for these topics are [efficient traffic control] which reduce transportation energy and [crowd sensing by vehicles] which provide flexible sensing infrastructure. This work mainly focus on [crowd sensing] factor.
- From a point of view of crowd sensing [vehicle can be seen as a powerful sensor]. There exist [many kinds of in-vehicle sensors] for example physical sensors, thermal sensors, electromagnetic sensors, and so on. And recently vehicle start to equip [advanced environmental sensors] such as [stereo camera, laser rader, milliwave rader] to recognize environment around the vehicle.
- [Floating car] is a similar concept with vehicular crowd sensing in ITS . [Floating car systems try to monitor these phenomena in a city]. For example, [traffic monitoring] is the most major usage of floating car system. And some other usages such as [road condition monitoring or weather monitoring] are already introduced. In contrast to floating car, [vehicular crowd sensing] has different point of view. It [try to contribute for a city rather than for a drivers]. It aims [wider range of usage] such as [city mapping, environmental sensing, facility maintenance, and city public security].
- [Key performance indices of vehicular crowd sensing] consists of these two. The one is [quality of data and the other is quantity of data]. [Quality of data or accuracy] is directly brought by [quality of sensors]. [Quantity of data or coverage] has two factors. [Number of sensors] can [boost the area simultaneously observed]. The other factor is [route of sensors]. Sensing vehicles should [track efficient route to visit sensing target]. And [the routes should not be redundant among multiple sensors]. We focused on [coverage] of vehicular crowd sensing.
- [The number of sensors] is derived from [base traffic amount by participation rate]. This can be [enhanced by penetration strategy for example enforcement or incentives]. We focused on second factor [route of sensors]. The route should [efficiently track sensing demand in a city]. The route can be [enhanced via traffic control]. The example deployment of traffic control are for example [center based navigation system, fleet management system , or managed self-driving car system].
- Here is a [definition of sensing demand or sensing target in a city]. [Sensing demand in a city might varies in space and time]. So there are [three categories of sensing demand]. [Uniform demand , non-uniform static demand and dynamic demand] These are example for each sensing demand categories.
- We also define [evaluation index of sensing coverage]. Sensing demand is [defined on each road link]. We employ [binary demand which represent demand exist or not]. Sensing demand is [fully satisfied when sensing vechile pass the link]. We use [demand satisfaction] as a coverage index. Demand satisfaction is [how much percentage the sensing demand satisfied in space and time]. It [varies from 0 which is fully satisfied to 1 which is not satisfied at all]. We also measured [travel time] which is [the time taken to destination].
- Then I explain [traffic control aware of sensing demand]. We modify [shortest route in order to pass sensing demands]. Therefore vehicles [make detour to satisfy sensing demands]. The [default route finding] is based on [distance cost or time cost]. We modify [the cost to be aware of sensing demand]. [The link cost is decreased as much as sensing demand], then [the route is attracted to the sensing demand].
- We also introduce [route reservation method to avoid concentration of traffic]. Using sensing demand as a cost causes [traffic concentration to sensing demand]. It is [redundant sensing when multiple vehicle visit at once]. Route reservation tries to solve this problem In route reservation , [each vehicle reserves route before it arrives] Then each vehicle [find optimal route according to number of reservations.
- This is how actually [route reservation] is processed. [Route reservation is managed in traffic management center]. [Each link in a city has reservation slot] or attribute. [Reservation aware route finding is performed in the traffic center]. And [then all of sensing vehicle follows the route]
- We evaluated using “Metro traffic simulator” which is “simple micro simulation workbench” we built. We already published the software to Windows store which is available in every Windows 8 PCs. It has minimal functionalities to simulate traffic, [car following model, shortest route search and generator of grid based and Open street map based maps].
- Here is summary of the results We’ve already confirmed how to extend coverage in a case [uniform sensing demand] in our [previous work] We evaluated our method in [static sensing demand] at first. [For coarse demand, simple sensing demand cost would work to expand coverage]. [For higher traffic density, route reservation would work]. [For longer route, reservation should be considered time slot]. In dynamic sensing demand the [reservation with time slot would also work].
- In previous work, we already confirmed [reservation has these two appropriate effects]. Reservation enable to [extend coverage and reduce traffic congestion simultaneously] [These effects realize higher coverage without travel time extension] in some situation].
- We performed [sensitivity analysis on demand density]. [Three route finding methods are used. distance, travel time, and sensing demand aware]. The graph’s x axis is demand density and y axis is coverage. The result shows [for coarse demand, simple sensing demand cost would gain extra coverage]. [For dense demand, the demand distribution is similar to uniform case], so the method previous work will work.
- For the coarse demand, each vehicle selectively satisfies sensing demands. And the total sensing demand is small so the satisfaction ratio is relatively higher. In moderate to dense demand, ratio of satisfaction decline as the total number of demand increase. In coarse case, only small detour occurred so travel time extension is relatively low. For overall using sensing demand cost for coarse sensing demand will work.
- We evaluated the effect of reservation in [higher traffic volume case]. The result shows [Reservation technique can extend coverage even in higher traffic] by avoiding traffic concentration.
- This is a video of proof of concept of reservation method. Left side is the simulation which using sensing demand cost. Right side is the simulation which reservation cost is used. There are 3 traffic from left to right. As you can see, with simple sensing demand cost all of traffic is concentrated to middle lane. Therefore sensing demand here is never visited by any cars. With reservation, both of northern and southern demand are also visited.
- We observed effect of [reservation deteriorate when map size is increased]. This is [caused by excess reservation which is not actually necessary]. We introduce [“time slot” of reservation to avoid the excess reservation].
- This video shows how time slot effectively avoid unnecessary reservation. The upper one is reservation without time slot. The most of northern and southern traffic cannot satisfy traffic demand because middle lane always occupied by reservation which middle traffic made. The lower one is reservation with time slot. Northern and southern traffic can satisfy traffic demand because reservation is made at only the timing it will pass the link.
- For dynamic demand the demand we assume the future demand is fully predicted. We dynamic demand which randomly changes we cannot find any advantage with these reservation technique. We find the advantage only in case the sensing demand which changes reciprocally.
- Let me conclude my presentation. We revealed [sensing demand aware and reservation based route finding] has potential to [enhance coverage without extending much travel time]. However detour is not exactly zero so [some kind of incentive will be needed]. These technique can be [easily integrated to current center-based navigation] because of its simplicity. For future work we’d like to evaluate these methods in [more realistic situation]. And we also will introduce optimization techniques to maximize converge.
- We’d like to try two approaches to optimize the route for vehicular crowd sensing. One is fleet management technique which solve vehicle routing problem. The other is traffic management technique which solve dynamic traffic assignment. We think these techniques is able to assure pseudo optimal coverage or limit the travel time extension.