IEEE ITSC2010 Workshop on Emergent Cooperative Technologies in Intelligent Transportation Systems
ECO-Routing Navigation System based on MultiSource Historical and Real-Time Traffic Information
Kanok Boriboonsomsin, Matthew Barth (Senior Member, IEEE), Weihua Zhu, and Alexander Vu
College of Engineering – Center for Environmental Research and Technology
University of California, Riverside
Riverside, CA, 92507
{kanok, barth, wzhu, alexvu}@cert.ucr.edu
Abstract— Due to increased public awareness on global climate
change as well as other energy and environmental problems, a
variety of strategies are being developed and used to reduce the
energy consumption and environmental impact of roadway
travel. In the area of Advanced Traveler Information Systems,
recent efforts have been made in developing a new navigation
concept called “eco-routing” that finds a route requiring the least
amount of fuel and/or producing the least amount of emissions.
This paper presents an eco-routing navigation system that
determines the most eco-friendly route between a trip origin and
a destination. It consists of several components, including: (a) a
Dynamic Roadway Network database, which is a digital map of
roadway network that integrates historical and real-time traffic
information from multiple data sources through an embedded
data fusion algorithm; (b) an Energy/Emissions Operational
Parameter Set, which is a compilation of energy/emission factors
for a variety of vehicle types under various roadway
characteristics and traffic conditions; (c) a routing engine, which
contains shortest-path algorithms used for optimal route
calculation; and (d) user interfaces that receive origin-destination
inputs from users and display route maps to the users. Each of
the system components and the system architecture are
described. Example results are also presented to prove the
validity of the eco-routing concept and to demonstrate the
operability of the developed eco-routing navigation system.
Keywords - roadway navigation; vehicle emissions; fuel
consumption; traffic information; data fusion; shortest-path
algorithm
I.
INTRODUCTION
Travel demand on the U.S. roadway systems consumes
large quantities of fuel, corresponding to approximately 30% of
the nation’s energy use [1]. In addition, it is responsible for
about one-third of U.S. carbon dioxide (CO2) emissions [2].
These issues need to be addressed, not only by improving
vehicle efficiency and developing alternative fuels, but also by
reducing vehicle miles traveled and making roadway travel
more efficient. The last of these four elements can be achieved
through the deployment of various intelligent transportation
system (ITS) technologies.
One of the major successes in ITS technology in recent
This work was supported in part by University of California
Transportation Center, Audi AG, and Nissan Motor Company.
years has been in the area of Advanced Traveler Information
Systems. Specifically, there has been a significant proliferation
of both off-board and on-board navigation systems that are
designed to provide route guidance to drivers. These navigation
systems primarily find a route corresponding to the shortest
distance between an origin (or the current location of a vehicle)
and a destination. Newer generation systems are also capable of
incorporating real time traffic information, providing the option
of finding the shortest-duration route in addition to the standard
shortest-distance route.
This paper builds on a new navigation concept called “ecorouting”, first introduced in [3] and [4], which finds a route
requiring the least amount of fuel and/or producing the least
amount of emissions. An overview of the general concept is
given in Section II. Based on this concept, an eco-routing
navigation system has been developed by the authors since
2006. The system consists of several components. Each of the
components is described in detail in Section III. Section IV
contains example results to prove the validity of the ecorouting concept and to demonstrate the operability of the
developed eco-routing navigation system. Finally, Section V
provides conclusions of the paper and discusses the current
limitations of the system and other pending improvements.
II.
BACKGROUND
A. Eco-Routing Concept
Studies have shown that selecting different travel routes
between the same origin-destination pair can result in
significant differences in the amount of fuel consumed and
emissions produced [5, 6]. An exploratory study in Sweden
found that 46% of the trips were not made on the most fuelefficient route. These trips could have saved fuel by an average
of 8% with the help of a fuel-optimized navigation system [3].
Recently, efforts have been made in developing eco-routing
navigation systems that find a route requiring the least amount
of fuel and/or producing the least amount of emissions [3, 4]. It
has been previously shown that these least-fuel or leastemission routes are not always the same as the shortestduration route [4]. This is partly because of the nonlinear
relationship between travel speed and vehicle fuel
consumption/emissions [7]. It is also due to other factors
affecting vehicle fuel consumption/emissions including vehicle
characteristics (e.g. vehicle type, model year), roadway
characteristics (e.g. roadway type, vertical grade), and traffic
conditions (e.g. speed, congestion level) [8-13].
It is important to differentiate between the concepts of ecorouting and eco-driving, and understand how they can work
cooperatively to minimize the energy use and environmental
impact of roadway travel. Eco-driving, by the authors’
definition, is fuel efficient operation of a vehicle to achieve
better fuel economy and lower tailpipe emissions while not
compromising the safety of oneself and other road users. Thus,
it is not recommended, for example, to drive a vehicle at 45
mph to maximize fuel economy on a freeway where the
prevailing speed of traffic is 65 mph. This may compromise the
safety of other road users as it has been evident that speed
variation is one of the contributing factors to vehicle crashes
[14-16]. Instead, for any trip, an eco-route can be chosen based
on roadway characteristics and current (and possibly future)
prevailing traffic conditions. Then, a variety of eco-driving
techniques can be employed en route based on actual traffic
circumstances experienced. By being on an eco-friendly route,
eco-driving techniques (e.g. maintaining optimal speed for fuel
economy) can be employed more easily and safely.
B. Energy/Emissions Estimation in Support of Eco-Routing
All shortest-path algorithms require that link cost factors
are available for roadway links in the network. Thus, before an
eco-route can be calculated, energy/emissions cost factors of
each roadway link must be determined and/or updated based on
available traffic information. There are several tools, such as
Comprehensive Modal Emissions Model (CMEM) [17] and
VT-Micro [18], which can be used to estimate
energy/emissions from vehicles very accurately. However,
these microscopic tools require extensive input data (e.g.
second-by-second vehicle velocity profiles) and have high
computation cost, which may not be suitable for real-time
applications as in roadway navigation. Alternatively, a
mesoscopic approach—estimating energy/emissions cost
factors as a function of a set of explanatory variables at a
roadway link level—seems to be a more viable and practical
option.
There are several approaches for estimating vehicle
energy/emissions at a roadway link level. One approach is to
use the readily available regulatory emission models (i.e. the
U.S. federal’s MOBILE6 [19] and California’s EMFAC [20])
and estimate energy/emission factors based on link speed. This
link speed input can be refined by taking into account various
roadway and traffic characteristics, which results in emission
factor output that better represents the traffic condition on the
link [13]. Also, an approach based on vehicle specific power
(VSP) has been proposed that creates link-based emission
factors as a function of link speed [21]. All these approaches
are targeted at estimating energy/emissions from the traffic
stream on roadway links. Using any of these approaches to
provide link energy/emissions cost factors of a specific vehicle
make, model, and year for use in roadway navigation will
require significant data collection (i.e. energy/emission
measurement) efforts.
Alternatively, a hybrid approach has been proposed that
combines a microscopic energy/emissions model (i.e. CMEM)
with a large vehicle activity database to create functional
relationships between link-based emission factors and a set of
link-based explanatory variables [7]. The vehicle activity
database consists of a large number of real-world second-bysecond velocity profiles collected in Southern California using
GPS-instrumented vehicles. These velocity profiles have been
broken down into snippets based on congestion level (as
defined by level of service or LOS), where a new snippet starts
whenever there is a change in LOS in the velocity profile.
Thus, each snippet represents only one LOS. As CMEM uses a
physical, power-demand approach based on a parameterized
analytical representation of fuel consumption and emissions
production, it can be calibrated to a specific vehicle make,
model, and year using only a limited amount of measured
energy/emissions data. Then, the previously processed vehicle
activity snippets can be applied to the calibrated model to result
in energy/emission estimates for the specific vehicle under a
variety of driving conditions, as presented in [8]. For each of
the snippets, the average speed as well as average fuel
consumption and emission values can be calculated, which can
be used to develop functional relationships between
energy/emissions and speed and congestion.
III.
SYSTEM COMPONENTS AND ARCHITECTURE
The initial development of the eco-routing navigation
system focused on freeway-only networks where fuel
consumption and emission attributes are estimated for each
freeway link based on measured traffic volume, density, and
average speed from a traffic performance measurement system
[4]. These link attributes are then used as cost factors in lieu of
the standard distance or travel time attributes when calculating
an optimal route for a trip. The methodology has been
expanded to include other roadway types in the roadway
network, and enhanced to include traffic performance data
from multiple sources as described below.
A. Dynamic Roadway Network Database
At the heart of any route planning or roadway navigation
tools are digital roadway maps such as NAVSTREETS (see
http://www.navteq.com). These digital roadway maps are
usually created in a Geographic Information System (GIS)
database, which stores static information regarding the
characteristics (e.g. length, functional class, speed limit, etc.) of
each roadway link as a data layer. More data layers can be
added to include time-varying data such as historical traffic
performance on the roadway links. They can also be updated
periodically to store real-time traffic information for use in
route calculation or map display.
A Dynamic Roadway Network (DynaNet) database has
been developed by the authors for the entire state of California
in a MySQL database environment. It uses NAVSTREETS as
the underlying digital roadway map, and incorporates traffic
performance data—both historical and real-time—from
multiple sources as additional data layers (see Fig. 1).
Historical data include those obtained from travel demand
models, traffic simulation models, as well as traffic monitoring
systems for an area. The main source for real-time traffic
information on freeways is the California’s Freeway
Performance Measurement System or PeMS, which gathers
traffic measurements (i.e. flow, speed, density) from thousands
of loop detectors on California freeways [22]. Data from PeMS
are acquired by DynaNet at five-minute intervals or ondemand. In addition to PeMS, DynaNet also receives data from
a limited number of probe vehicles traveling on both freeways
and surface streets.
congestion level, among others. For instance, vehicles that
carry heavier loads will consume more fuel and emit more
emissions. Vehicles with different engine model years will
have different emission rates as the engines are certified to
different emission standards. Lastly, driving under different
levels of congestion will have different speed profiles, which
impact the fuel consumption and emissions of vehicles. Figure
3 shows an example of the effects of vehicle speed and road
grade on fuel consumption of a light-duty car [8].
Google Earth or Google Maps
interfaces
Real-time probe vehicle data
(for freeways and surface streets)
Real-time data from other sources
Real-time PeMS data
(for freeways)
Historical data from travel demand
or traffic simulation models
Underlying digital roadway
network with speed limit info
Figure 1. Multiple sources of traffic performance data used in Dynamic
Roadway Network Database.
All traffic performance data received by DynaNet are
processed and combined by data fusion algorithms. Part of the
data processing involves determining traffic performance
measures for each roadway link in the underlying roadway
network. This step is handled differently for data from different
sources. For instance, PeMS provides point measurements of
traffic performance at the locations of its sensors. Thus, traffic
performance measurements at each sensor need to be projected
onto the roadway links. The methodology is illustrated in Fig.
2. With the knowledge of the distances between the adjacent
pairs of sensors, a set of virtual links i whose spatial coverage
is ci is created, where
l + l l + l 
c i =  i −1 i , i i +1 
2 
 2
(1)
and li is a centerline distance in km from a starting location to
sensor i.
L
M
N
O
P
Q
R
Figure 3. Energy/Emissions Operational Parameter Set indexed by average
speed and road grade [8].
It can be seen that the estimation of Energy/Emissions
Operational Parameter Set (EOPS) is complicated, involving a
number of factors that have different impacts on the fuel
consumption and emissions of vehicles. Even more
complicated is the interaction among these factors. For
instance, in Figure 3 it is shown that the optimal speed for fuel
consumption is not the same for each road grade. Likewise, the
optimal speed for a vehicle pulling a loaded trailer will be
different from the optimal speed for when it is not. So, it could
be best for this vehicle to use one route when pulling a load to a
destination, and to use another route when returning empty. In
order to take such interactions into account, multiple regression
is employed to estimate the EOPS for a vehicle as:
S
EOPSi α f(V, R, T, O)
1
2
3
4
5
6
7
8
9
(2)
10
where
Figure 2. Link-based energy/emissions factors assignment methodology.
Each link in the roadway network (i.e. Links 1-10 in Fig. 2)
is then assigned a traffic performance value of the overlapping
virtual link(s) weighted by the overlapping distance. For the
example in Fig. 2, E1 = (3/4)EL + (1/4)EM; E2 = (3/7)EM +
(4/7)EN; E3 = EO; and so forth.
B. Energy/Emissions Operational Parameter Set
There are several factors that influence the fuel
consumption and emissions of vehicles. These include vehicle
speed, loaded weight, model year, road grade, road type,
i = {fuel, CO2, carbon monoxide (CO), hydrocarbon
(HC), oxides of nitrogen (NOx), particulate
matter (PM)}
V = vector of vehicle characteristics, e.g. vehicle
type, model year, loaded weight, etc.
R = vector of roadway characteristics, e.g. roadway
type, vertical grade, speed limit, type of
intersection at link ends (stop-sign, signalized,
none), etc.
T = vector of traffic characteristics, e.g. speed, flow,
density or congestion level, etc.
O = vector of other explanatory variables, e.g. driver
characteristics, the environment, etc.
In the generic function above, vehicle characteristic
variables are based on user inputs. Roadway characteristic
variables are obtained directly from the roadway link data table
in DynaNet. Traffic characteristic variables are supplied by the
fused traffic performance data from DynaNet. It is important to
note that the variables to be used for EOPS estimation,
especially those related to traffic characteristics need to be
available or measurable on a link-by-link basis, preferably in
real-time. For example, it is well known that acceleration rate is
another important explanatory variable of vehicle emissions,
where frequent and intense accelerations and deceleration
events will result in higher vehicle emissions and fuel
consumption. However, this variable is not currently measured
by any traffic monitoring systems. Therefore, congestion level
as described by traffic density is used as a surrogate. Traffic
density is calculated based on speed and flow obtained from
PeMS. Uncongested traffic (low traffic density) usually has
smooth
flow of traffic
with
little and
mild
acceleration/deceleration. On the other hand, congested traffic
(high traffic density) involves stop-and-go driving with more
frequent and harder acceleration/deceleration.
C. Routing Engine
Route calculation in the eco-routing navigation system is
based on the Dijkstra’s algorithm with binary heap priority
queue that searches for a least-cost path in a graph with
nonnegative edge path costs [23]. Path construction can take
into consideration users’ route preferences such as preferring
highways or avoiding toll roads. In addition, it can take the
number of passengers as an input for determining the vehicle’s
eligibility to use high-occupancy vehicle lanes. Note that for
each trip, the eco-routing navigation system can generate up to
seven routes based on different minimization criteria (i.e.
distance, travel time, fuel, CO2, CO, HC, and NOx), some of
which may be the same.
D. User Interfaces
Two front-end software applications have been developed
to serve as the user interfaces of the eco-routing navigation
system. One of them is a web-based tool that takes advantage
of Google Maps’ Application Programming Interfaces (API),
as depicted in Fig. 4. The other one is a stand-alone software
program that has Google Earth embedded, making use of its
satellite map display and visualization capability (see Fig. 5).
Figure 4. Web-based application of the eco-routing navigation system.
Figure 5. Stand-alone application of the eco-routing navigation system.
E. System Architecture
The overall methodology of the eco-routing navigation
system is shown in Fig. 6. First, the system receives a request
from the driver through the User Interface. Then, it triggers the
EOPS Updater to update EOPS for each link in the roadway
network based on the latest set of fused traffic performance
data from DynaNet and the calibrated regression coefficients
for the vehicle. Note that DynaNet can be set to acquire and
update traffic performance data periodically (e.g. every 5
minutes) or on-demand (i.e. only when there is a route request).
Once the EOPS has been updated, it is stored in a data table in
DynaNet along with other link cost factors (i.e. distance and
travel time). This data table, in conjunction with other data
tables (such as the one defining connectivity among links), is
then used by the Routing Engine in finding optimal routes
according to the criteria set by the driver. Finally, the routes are
displayed to the driver via the User Interface.
User Interface
EOPS Updater
Dyna
Net
Routing Engine
Figure 6. Eco-routing system architecture.
IV.
EXAMPLE RESULTS
A. Demonstration of Eco-Routing Navigation System
In [6], using a pair of fairly parallel freeways in Southern
California with traffic performance data from PeMS, it was
shown that the least-fuel or least-emission routes are not
always the same as the shortest-duration route. That example,
in essence, proves the validity of the eco-routing concept. In
this paper, further proofs are provided by demonstrating how
the eco-routing navigation system could suggest different
optimal routes for the same trip given different route preference
criteria. This is shown in Fig. 4 for a trip from the Los Angeles
Airport to downtown Los Angeles in a typical weekday late
evening. The blue route represents the shortest-distance route,
violet the shortest-duration route, and green the least-fuel route.
The estimated travel costs for each route are given in Table 1.
TABLE I.
TRAVEL COSTS FOR DIFFERENT ROUTES FROM LOS ANGELES
AIRPORT TO DOWNTOWN LOS ANGELES
80
Measured
70
T
20.8
25.0
0.89
7.76
44.80
2.00
6.26
F
16.1
27.0
0.67
5.82
16.70
1.12
4.40
T vs D
+30
-14
+32
+31
+180
+85
+44
F vs D
+1
-7
-2
-2
+4
+4
+1
EcoNav
F vs T
-23
+8
-25
-25
-63
-44
-30
60
Speed (mph)
Distance (mi)
Time (min)
Fuel (gal)
CO2 (kg)
CO (g)
HC (g)
NOx (g)
D
16.0
29.0
0.68
5.93
16.00
1.08
4.35
50
40
30
20
10
It should be noted that the diversity of optimal routes
according to different criteria depends in part on the number of
route choices available between an origin-destination pair.
Thus, the benefits of the eco-routing navigation system, as well
as any roadway navigation systems in general, will be more
pronounced in areas whose roadway network has a high degree
of route choice freedom [24].
0
0
10000
20000
30000
40000
50000
60000
40000
50000
60000
40000
50000
60000
Distance (m)
4500
Measured
4000
EcoNav
Travel Time (second)
3500
3000
2500
2000
1500
1000
500
0
0
10000
20000
30000
Distance (m)
2.4
Measured
2.1
EcoNav
1.8
Fuel (gallon)
Given Fig. 4 and Table 1, the driver can see the different
route options and their associated travel costs (i.e. distance,
travel time, fuel consumption, and the various emissions). The
shortest-distance route takes a direct path on surface streets to
the destination. On the other hand, the shortest-duration route
takes a detour onto freeways in order to profit from higher
travel speeds. Although the distance traveled is 30% longer, the
travel time is shortened by 14%. The least-fuel route also takes
a direct path on surface streets, and most of it overlaps with the
shortest-distance route. However, it takes a 1% longer-distance
path towards the end of the trip, which helps save fuel by 2%
and travel time by 7%. When comparing the least-fuel route
with the shortest-duration route, it is found that a combination
of milder speed and shorter distance on surface streets provides
fuel savings of 25% although the travel time is 8% longer.
Equipped with all these information, the driver can consider the
tradeoffs and make a choice with regards to which route to take
based on his/her preference, the circumstances, etc.
1.5
1.2
0.9
0.6
B. Validation of Eco-Routing Navigation System
To validate the accuracy of the travel costs (e.g. travel time,
fuel consumption) estimated by the eco-routing navigation
system, a number of test drives were made between multiple
pairs of origin-destination on different routes. An Audi A8
vehicle was instrumented with a GPS data logger and an OBDII reader so that the vehicle’s position, speed, and fuel
consumption were recorded at 10 Hz during the trip. Fig. 7
shows the comparison between the measured speed, travel
time, and fuel consumption and those estimated by the system
for one of the test drives. It was an approximately 56-km trip
from Berkeley, California to Palo Alto, California. The trip
consisted mainly of surface-street driving except for the section
between the 36th and 44th km. According to Fig. 7, the link
speed reported by DynaNet matches up fairly well with the
actual vehicle velocity profile. The system overestimates the
true travel speed on many roadway links while it
underestimates on several others. These discrepancies nullified
each other to some extent, resulting in the estimation error of
the total travel time of 5%. The same effect is also seen in the
estimated fuel consumption but at a lesser degree. For this trip,
the total fuel use was underestimated by 12%. These results
suggest a reasonable estimation performance of the system.
0.3
0
0
10000
20000
30000
Distance (m)
Figure 7. Comparison between measured and estimated travel costs.
V.
CONCLUSIONS AND FUTURE WORK
This paper presents an eco-routing navigation system that
determines the most eco-friendly route between a trip origin
and a destination. The system consists of several components:
(a) Dynamic Roadway Network database, which is a digital
map of roadway network that integrates historical and real-time
traffic information from multiple data sources through an
embedded data fusion algorithm, (b) Energy/Emissions
Operational Parameter Set, which is a compilation of
energy/emission factors for a variety of vehicle types under
various roadway characteristics and traffic conditions, (c)
routing engine, which contains shortest-path algorithms used
for optimal route calculation, and (d) user interfaces that
receive origin-destination inputs from users and display route
maps to the users.
The current version of the eco-routing navigation system,
although operable and fairly accurate, has some limitations that
can cause errors in the estimated trip fuel consumption and
emissions. Work is underway to improve the estimation
performance of the eco-routing navigation system by
addressing some of the causes of error as discussed below.
•
•
•
•
Data aggregation: This error is due to the loss of data
variation at the mesoscale level as compared to the
microscale level; for example, when link average speed
is used to represent the entire vehicle velocity profile
on the link. This error arises from the tradeoff for
model simplicity and low computation time.
Model inputs: EOPS is estimated from a set of
explanatory variables. Therefore, the error in these
variables will certainly propagate to the estimated fuel
consumption and emissions. For instance, if the traffic
speed and congestion level are misrepresented in the
model, the fuel consumption and emissions estimates
will not be accurate. This is problematic for roadway
links where measured traffic performance data are
lacking (e.g. arterials, local roads). Efforts are being
made to seek additional sources of traffic performance
data on these roadways (e.g. Bluetooth, wireless
vehicle sensors, etc.).
State of traffic: In the example results shown, route
calculation is performed based on the state of traffic at
the time of calculation (i.e. before the trip begins).
Thus, an error is inevitably introduced when the state
of traffic changes during the trip. Work is being
planned to incorporate traffic state prediction models
into the eco-routing methodology.
Regression: As EOPS is based on a regression
technique, a random error exists that represents
unexplainable causal factors in the regression models.
Research is being conducted to improve these models
by including additional explanatory variables such as
intersection type and delay.
ACKNOWLEDGMENT
The authors thank George Scora of the University of
California at Riverside and James Misener, Wei-Bin Zhang,
Meng Li, and Guoyuan Wu of the California Partners for
Advanced Transit and Highways for their contribution.
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