Communicating with Infrastructure: Using Intelligent Transportation System Technologies
Matthew BarthYeager Families Professor of Engineering
Director: Center for Environmental Research and TechnologyUniversity of California-Riverside
Acknowledgements:
• Kanok Boriboonsomsin, George Scora, Alex Vu, Sindhura Mandava, Qichi Yang
• University of California Transportation Center (UCTC), California Department of Transportation, Federal Highway Administration, EPA, VW/Audi, Nissan, BMW, others
UCR CE-CERTLaboratories:
Emissions and Fuels ResearchEmissions measurement/analysis, fuel effects, new
instrumentation/methods, after-treatment
Atmospheric Processes Researchsecondary pollutant formation, mitigation methods
Transportation Systems Research ITS, vehicle activity, energy/emissions modeling
Sustainable Energy Researchcellulosic ethanol, synthetic diesel fuel, solar energy
Environmental Modelingmodels to predict long-term regional effects; micro dispersion
www.cert.ucr.edu
Intelligent Transportation SystemsTargeted Benefits:
• Improving Safety• reducing accidents• making accidents more survivable
• Improving Transportation Efficiency:• increasing throughput• reducing congestion• maximizing economics
• Energy/Environment:• in-direct benefits of lower emissions and fuel savings• directed benefits to target lower emissions/fuel
Communications is key for Advanced ITS Applications:• V2V (adhoc networks, VANET)• V2I• I2V
Many ITS applications require real-time link-level information: speed, density, flow, energy, emissions, etc.
Vehicle Activity: Real-Time Traffic Data
• real-time traffic density, speed, and flow is become more readily available
• Example: California Traffic Performance Measurement System (PeMS)
• Real-Time data can be used measure congestion
PROBE VEHICLES:
using GPS dataloggers
0
10
20
30
40
50
60
70
80
90
100
7:00 7:03 7:06 7:09 7:12 7:14 7:17 7:20
Time [hr:min]
Vel
oci
ty [
km/h
r]
Highway Arterial Residential
• when and where trips start and end
• measurement of position, speed, and acceleration
• which roadway route (post map match)
Traffic Surveillance• Proliferation of Video Cameras:
• off-board• on-board• better algorithms
Arterial Roadway Testbed Chula Vista, California
re-identification
Simulation coverage
Minimum probe or other data coverage(overlaps with thesimulation network are not marked)
Simulation coverage
Minimum probe or other data coverage(overlaps with thesimulation network are not marked)
Google Earth or Google Maps Interface
Traffic simulation result for arterials
PeMS data for freeways
Probe Vehicle network (arterials and freeways)
NAVTEQ underlying network data
Dynamic Network Information: Data Integration
Traffic signal/detector info
INRIX traffic data
Broadcasting
Mobile communications network
Base station (Antenna)
On-board network (ITS Data Bus), sensors, collision avoidance radar, ...
Roadside network(Ex. Optical fiber)
Pedestrian
Cell phone, Satellite portable phone, MCA, Radio pager, ...
Existing network
ITS information communications platform
ITS information
Truck(Transport company)
Bus, Taxi(Public transportation)
Passenger vehicle
GPS
ITS information
ITS information
Roadside network(Ex. Optical fiber)
ITS info
Broadcasting (FM multiplex, Terrestrial digital, Satellite digital, …)
Road-vehicle communications
DSRC
Base station (Antenna)
Vehicle-vehicle communications
ITS Communication
UCR Testbed Vehicle
GPS-based location system and wireless communication capability
Programmable navigation system with touch-screen capability available to driver and passengers
On-board computer interfaces with the vehicle CAN bus, navigation system, and
wireless communications system
“Dynamic Eco-Driving”ECO-Driving Advice with Dynamic Feedback• Static ECO-Driving:
• shift up as soon as possible• maintain a steady speed• anticipate traffic flow• accelerate smoothly• decelerate softly• check the tire pressure frequently
• Dynamic ECO-Driving: providing real-time advice/feedback:• speed management, intelligent speed adaptation• Instantaneous fuel economy readings• Cumulative real-time travel cost display
Dynamic ECO-Driving
DestinationCurrentLocation
Dynamic ECO-Driving
Destination
Current Speed ECO-Speed
40 35MPHMPH
11+ mi. over ECO-Speed
6-10 mi. over ECO-Speed
1-5 mi. over ECO-Speed
At or under ECO-Speed
Dynamic ECO-Driving
Destination
Current Speed ECO-Speed
21 25MPHMPH
11+ mi. over ECO-Speed
6-10 mi. over ECO-Speed
1-5 mi. over ECO-Speed
At or under ECO-Speed
Dynamic ECO-Driving
Destination
Current Speed ECO-Speed
29 35MPHMPH
11+ mi. over ECO-Speed
6-10 mi. over ECO-Speed
1-5 mi. over ECO-Speed
At or under ECO-Speed
Dynamic ECO-Driving
Destination
Current Speed ECO-Speed
61 45MPHMPH
11+ mi. over ECO-Speed
6-10 mi. over ECO-Speed
1-5 mi. over ECO-Speed
At or under ECO-Speed
Dynamic ECO-Driving
Destination
Current Speed ECO-Speed
52 45MPHMPH
11+ mi. over ECO-Speed
6-10 mi. over ECO-Speed
1-5 mi. over ECO-Speed
At or under ECO-Speed
Dynamic ECO-Driving
Destination
Current Speed ECO-Speed
45 40MPHMPH
11+ mi. over ECO-Speed
6-10 mi. over ECO-Speed
1-5 mi. over ECO-Speed
At or under ECO-Speed
Dynamic ECO-Driving
Destination
Current Speed ECO-Speed
33 35MPHMPH
11+ mi. over ECO-Speed
6-10 mi. over ECO-Speed
1-5 mi. over ECO-Speed
At or under ECO-Speed
0
20
40
60
80
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120
0 1 2 3 4 5 6 7 8 9 10 11
Distance Traveled, km
Veh
icle
Sp
eed
, km
/h
Non-ISA ISA ISA Maximum Recommended Speed
0
20
40
60
80
100
120
11 12 13 14 15 16 17 18 19 20 21 22
Distance Traveled, km
Veh
icle
Sp
eed
, km
/h
Dynamic Eco-Driving Field Experiments: Example Results
same travel time results:
reference:
M. Barth and K. Boriboonsomsin (2008) “Energy and Emissions Impacts of a Freeway-Based Dynamic Eco-Driving System”, in press, Transportation Research Part D: Environment, Elsevier Press, August, 2008.
. -13%15341766Fuel (g)
-37%3.976.28NOx (g)
-41%1.903.20HC (g)
-48%50.4797.01CO (g)
-12%47815439CO2 (g)
DifferenceECONon-ECOEnergy/Emissions
-13%15341766Fuel (g)
-37%3.976.28NOx (g)
-41%1.903.20HC (g)
-48%50.4797.01CO (g)
-12%47815439CO2 (g)
DifferenceNon-Energy/Emissions
Dynamic ECO-Driving for Arterial Roads: Signalized Corridor Speed Management
• Much fuel is wasted and CO2 is emitted by vehicles waiting at signalized intersections
• vehicle speed trajectory can be planned based on knowledge of signal timing
• concept: broadcast signal timing information to vehicle to plan vehicle trajectories
• potential target demonstration: California VII corridor where DSRC equipment is already in place with signal information
Signalized Corridor Time-Distance Diagram
time
distance
signal 1
signal 2
signal 3
signal 4
signal n
vehicle trajectories
Simple Signalized Corridor Planning (freeflow)
time
distance
signal 1
signal 2
signal 3
signal 4
signal n
simple heuristic: adjust vehicle speed to catch the green time
Velocity Algorithm• Target velocity is set to get through the green phase of the next
signal (time-distance calculation)• Initial velocity may be above or below target velocity• objective is to:
limit10
1
1102110
(v)
(iv)
(iii)
(ii)
))(()2
1( (i) : subject to
minimize
vatv
PPP
ttt
tt
Dttatvattv
a
enginesaccessorietf
tractive
rg
v0 = velocity of the vehicle at the instant it enters the DSRC ranget = total time taken to reach the intersectiont1 = the portion of time spent accelerating or decelerating with an acceleration rate a(t-t1) = portion of time spent traveling at uniform velocity before reaching the intersection
accelerate with respect to vh
],0[],[ maxvvvV hlpossible intrg dtt ,,
maxv
possiblec Vv
if
YES
NO
NO
YES
lc vv
if
decelerate with respect to vh
fuel or emissions
speed
TARGET
Signalized Corridor Planning Simulation Results
0 50 100 150 200 250 300 350 400 4500
2000
4000
6000
Time in sec
Dis
tanc
e in
met
ers
0 50 100 150 200 250 300 350 400 4500
5
10
15
20
25
Time in sec
spee
d in
met
ers
per
sec
0 50 100 150 200 250 300 350 400 450-3
-2
-1
0
1
Time in sec
acce
lera
tion
in m
eter
s pe
r se
c2
0 50 100 150 200 250 300 350 400 450 5000
2000
4000
6000
Time in sec
Dis
tanc
e in
met
ers
0 50 100 150 200 250 300 350 400 450 5000
5
10
15
20
25
Time in sec
spee
d in
met
ers
per
sec
0 50 100 150 200 250 300 350 400 450 500-3
-2
-1
0
1
Time in sec
acce
lera
tion
in m
eter
s pe
r se
c2
without advanced information with advanced information
LDV24 Without With % Diff.
in Avg. p-value of
t-test Avg. S.D. Avg. S.D. Fuel (g/mi) 118.3 13.2 103.8 9.3 -12.3 8.7E-06 CO2 (g/mi) 371.0 41.2 318.8 25.3 -14.1 3.2E-07
TT (sec) 456.7 60.7 451.9 56.9 -1.06 0.635
Navigation Techniques:• route finding uses famous minimum path algorithms
(Dijkstra, etc.)• shortest
distance
• shortest duration (requires traffic info)
• lowest energy, lowest emissions (requires traffic info, road grade, energy & emissions models)
New Navigation Tool: ECO-Routing:
• shortest-distance or shortest-duration path will oftenbe the path that minimizes energy use or emissions
• roadway congestion and other factors (e.g. grade) create scenarios where minimum-energy and minimum-emissions path may be different than shortest duration or distance
WAN
LAN
Management Interface
Wireless Infrastructure(Cellular, 802.11)
802.11 a/b/g/p - CMDA - GPRS
WAN
MCU
CMEMOBD-II
GPS
Data MiningPost Processing Web Interface XML-RPC Cluster
Database Cluster
ProxyComm Cluster
Universal Mobile Emissions Telematics System (UMETS)
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