a sensing coverage analysis of a route control method for vehicular crowd sensing

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

Summary

Concept

Vehicular crowd sensing for city monitoring

Methodology

Sensing coverage of city monitoring

Route finding methods for crowd sensing

Evaluation

Conclusion & future work



Concept


LABORATORY,INC. All Rights Reserved.3

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



Efficient traffic City monitoring

smart city

Transportation sector

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



Denso Technical Review

https://www.denso.co.jp/ja/aboutdenso/technology/dtr/v17/files/10.pdf

http://www.embedded.com/print/4011081Smart 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 [email protected]) 1.3 kWh

http://www.car-electronics.jp/files/2012/10/CurrentStateOfIn-

vehicleMicrocomputer.pdf

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



Environment

(pollution, noise)

Facility

Maintenance

(bridge, tunnel)

City Mapping

(road, building)

Public Security

(crime, disaster)

City monitoring with VCS

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



Number of sensors

Route (orbit) of sensors

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



Number of sensors

Route of sensors

Methodology



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



UNIFORM STATIC DYNAMIC

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



Link

Sensing Demand

dem

an

d level

0

1

Not satisfied

Satisfied

Travel time

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.



Sensing demand

Default route

New route

link cost

demand

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.



RESERVED

RESERVED

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



link cost

demand

Evaluation



Available on :

Evaluation environment

“Metro traffic simulator” – simple micro simulation workbench

Car following model

Shortest route search

Grid and OSM based maps



http://www.windowsphone.com/en-us/store/app/metro-traffic-simulator/9ce5418d-7caf-4a54-a20b-23e015edd2d1

http://www.windowsphone.com/en-us/store/app/metro-traffic-simulator/9ce5418d-7caf-4a54-a20b-23e015edd2d1

http://apps.microsoft.com/windows/en-us/app/metro-traffic-simulator/b96c55d4-158d-4347-a6c6-471bb3ddd80b

http://apps.microsoft.com/windows/en-us/app/metro-traffic-simulator/b96c55d4-158d-4347-a6c6-471bb3ddd80b

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



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



Distance cost

Travel time cost

Reservation cost

Link ID

tim

e

Previous work : Masutani, O. A proactive route search method for an efficient city surveillance. 21th World Congress on ITS, (2014).

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



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



Advantage in coarse demand

Co

vera

ge

Density

Distance

Travel time

Demand aware

1-1 Analysis

De-tour occurred ?



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

Travel ti

me

Co

vera

ge

Density

Density

Demand aware

Demand aware

1-2 High traffic volume case - reservation

Reservation avoid concentration

Reservation technique can extend coverage even in higher traffic



Demand aware Demand aware

+ reservation

Illustrati

on

Demand aware

Excess demand aware

Reservation

Co

vera

ge

Traffic volume

1-2 Effect of reservation cost



Sensing Demand only Sensing Demand + Reservation

1

2

3

1

2

3

1

2

3

1

2

3

never visited visited

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



current reservation predictive reservation

Demand aware

Reservation

Reservation w/ time slot

Map size

Co

vera

ge

: sensing demand

1-3 Effect of predictive reservation



1

2

3

1

2

3

1

2

3

1

2

3

Sensing Demand +

Reservation

Sensing Demand +

Reservation

w/time slot

2 Dynamic demand

Predictive demand

Known demands on future

Time slot work

Only confirmed in reciprocal dynamic demand



PD PD with current reservation PD with predictive reservation

Predictive Demand aware

Reservation

Reservation w/ time slot

: sensing demand

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



Optimization approaches



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

Thank you for your attention !

Any questions ?



Appendix



Travel time for each evaluation

Travel time doesn’t extend in each setting.



Map size

Travel ti

me

Travel ti

me

Traffic demand

Evaluation 1-2 Evaluation 1-3

a sensing coverage analysis of a route control method for vehicular crowd sensing

Software

sensing coverage analysis

crowd sensing factor

sensing demand aware

simple sensing demand

subsidiary company of

demand density

vehicular crowd sensingmar

demand distribution