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.
A Sensing Coverage Analysis of a Route Control Method for 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.
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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
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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
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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
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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
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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
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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
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Number of sensors
Route of sensors
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
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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
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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.
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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.
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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
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link cost
demand
16. Available on :
Evaluation environment
“Metro traffic simulator” – simple micro simulation
workbench
Car following model
Shortest route search
Grid and OSM based maps
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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
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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
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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
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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
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Advantage in coarse demand
Coverage
Density
Distance
Travel time
Demand aware
21. 1-1 Analysis
De-tour occurred ?
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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
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Demand aware Demand aware
+ reservation
Illustrati
on
Demand aware
Excess demand aware
Reservation
Coverage
Traffic volume
23. 1-2 Effect of reservation cost
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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
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current reservation predictive reservation
Demand aware
Reservation
Reservation w/ time slot
Map size
Coverage
: sensing demand
25. 1-3 Effect of predictive reservation
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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
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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
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28. Optimization approaches
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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 ?
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31. Travel time for each evaluation
Travel time doesn’t extend in each setting.
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Map size
Traveltime
Traveltime
Traffic demand
Evaluation 1-2 Evaluation 1-3
Hinweis der Redaktion
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.