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Fueoogle: A Participatory Sensing Fuel-Efficient Maps
Application
Nam Pham, Raghu Ganti, Saurabh Nangia, Thadpong Pongthawornkamol, Shameem Ahmed, Tarek Abdelzaher,
Jin Heo, Maifi Khan, and Hossein Ahmadi
Department of Computer Science, University of Illinois, Urbana-Champaign
nampham2, rganti2, nangia1, tpongth2, ahmed9, zaher, jinheo, mmkhan2, hahmadi2@illinois.edu
Abstract
This paper develops a participatory sensing service, called
Fueoogle, that maps vehicular fuel consumption on city
streets, allowing drivers to find the most fuel-efficient routes
for their vehicles between arbitrary end-points. The service
exploits measurements of vehicular sensors, available via the
OBD-II interface that gives access to most gauges and engine
instrumentation. The OBD-II sensors are standardized in all
vehicles produced in the US since 1996, constituting some of
the largest “sensor deployments” to date. Using fuel-related
measurements contributed by participating vehicles, we de-
velop a route planner that maps normalized fuel-efficiency
of city streets, enabling vehicles to compute minimum fuel
routes from one point to another. Street congestion, eleva-
tion variability, average speed, and average distance between
stops (e.g., stop signs) lead to changes in the amount of fuel
consumed making fuel-efficient routes potentially different
from shortest or fastest routes, and a function of vehicle type.
Our experimental study answers two questions related to the
viability of the new service. First, how much fuel can it save?
Second, can it survive conditions of sparse deployment? The
main challenge under such conditions is to generalize from
relatively sparse measurements on a subset of streets to es-
timates of measurements of an entire city. Through exten-
sive experimental data collection and evaluation, conducted
over the duration of a month across several different cars and
drivers, we show that significant savings can be achieved by
choosing the right route. We also provide extensive results
pertaining to the accuracy of models that are used for predic-
tion of fuel consumption values.
1 Introduction
An emerging category of sensor network applications
[8, 1, 19, 10, 18] rely on data collection by individuals and
sharing of this data within a community for common pur-
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poses such as mapping of physical phenomena or comput-
ing community wide statistics. In this paper, we develop
a novel participatory sensing application, called Fueoogle,
that computes fuel efficient routes from one point to another.
Fueoogle relies on data collected by individuals from their
vehicles as well as on the mathematical models that we de-
velop in this paper to compute fuel efficient routes.
Vehicles that have been sold in the United States af-
ter 1996 are mandatorily equipped with a sensing subsys-
tem called the On-Board Diagnostic (OBD-II) system. The
OBD-II is a diagnostic system that monitors the health of the
automobile using sensors that measure approximately 100
different engine parameters. Examples of monitored mea-
surements include fuel consumption, engine RPM, coolant
temperature, and vehicle speed. A comprehensive list of
measured parameters can be obtained from standard speci-
fications as well as manufacturers of OBD-II scanners [3].
Several commercial OBD-II scanner tools are available [3,
4, 2, 5], that can read and record these sensor values.
Fueoogle utilizes a vehicle’s OBD-II system and a typ-
ical scanner tool in conjunction with a participatory sens-
ing framework to develop a novel application that enables
the reduction of fuel consumption of vehicles in every-
day use (by computing fuel efficient routes). Compared to
traditional mapping tools, such as Google maps [15] and
MapQuest [22], which provide either the fastest or the short-
est route between two points, Fueoogle collects the necessary
information to compute and answer queries on the most fuel-
efficient route. The most fuel-efficient route between two
points may be different from the shortest and fastest routes.
For example, a fastest route that uses a freeway may consume
more fuel than the most fuel-efficient route because fuel con-
sumption increases non-linearly with speed or because it is
longer. Similarly, the shortest route that traverses busy city
streets may be suboptimal because of downtown traffic. The
optimal route might therefore be neither shortest nor fastest.
Indeed, we will show, in this paper, examples where the most
fuel-efficient route is different from both the shortest and the
fastest routes.
The motivation for Fueoogle does not need elaboration.
Fueoogle users might be driven by benefits such as saving
on fuel or reducing CO2emissions and the carbon footprint.
With the increase in the use of bluetooth devices (e.g., cell-
phones) and in-vehicle Wi-Fi, Fueoogle can be easily sup-
ported by inexpensive OBD-II-to-bluetooth or OBD-II-to-
WiFi adaptors that can upload OBD-II measurements op-
portunistically, for example, to applications running on the
driver’s cell phone. It can also be supported by scanning
tools that read and store OBD-II measurements on storage
media such as SD cards. At the time of writing, OBD-II
Bluetooth adaptors, such as the ELM327 Bluetooth OBD-
II Wireless Transceiver Dongle, are available for approxi-
mately $50, together with software that interfaces them to
phones and handhelds. Individuals who own OBD-II adap-
tors or scanning tools may record sensor measurements from
their daily commutes. These recorded sensor values are
then shared within a community, in a privacy-preserving
fashion, using a participatory sensing framework, called
PoolView [13]. Fueoogle does not require all city streets to
be driven by all types of vehicles in order to estimate the fuel
efficiency of different vehicle types on different streets. In-
stead, Fueoogle utilizes models, we develop in this paper, to
estimate fuel consumption for different streets and car types,
for which no direct OBD-II measurements are present, using
previously collected data on other streets and car models.
Fueoogle supports two types of users; members and
non-members. Members are those who contribute data
to the Fueoogle repository from OBD-II sensors as de-
scribed above. They have Fueoogle accounts and can ben-
efit from more accurate estimates of route fuel-efficiency,
customized to the performance of their individual vehicles.
Non-members can use Fueoogle to query for fuel-efficient
routes as well. Since Fueoogle does not have measurements
from their specific vehicles, it answers queries based on the
average estimated performance for their vehicle’s make and
model. In addition to being a look-up service such as Google
Maps, the authors envision Fueoogle to be integrated as an
option in future “green” GPS services that would give di-
rections based of the most fuel-efficient (as opposed to the
fastest or shortest) route.
In summary, the main contributions of this paper are two-
fold. First, we develop a fuel-saving service and analyze the
amount of fuel savings that are achieved using Fueoogle. An
experimental study is performed over the course of a month
using seven different cars with different drivers in order to
estimate fuel savings. The second contribution, and the main
challenge addressed in this paper, is whether we can use
a sparse deployment to estimate the fuel consumption on
streets and car types for which OBD-II measurements are not
yet available. We develop several mathematical models, us-
ing the datasets obtained over the course of our experimental
study, to correlate fuel efficiency with observable parameters
such as street speed limits, presence and number of traffic
lights, congestion information, and the type of car for which
the route is computed (e.g. SUV, small sedan).
The rest of this paper is divided into six sections. Sec-
tion 2 presents a feasibility study that investigates the amount
of fuel savings that can be achieved by using Fueoogle
and by following the fuel-efficient routes. The details of
Fueoogle system are described in Section 3. Models for es-
timating fuel consumption on streets lacking such measure-
ments are presented in Section 4. Evaluation results are pre-
sented in Section 5. Related work is presented in Section 6.
Finally, we conclude with directions for future work in Sec-
tion 7.
2 A Feasibility Study
In this Section, we present a feasibility study that pro-
vides the reader with an estimate of fuel savings that can be
achieved by driving on the most fuel efficient routes.
We compute fuel consumption between landmarks (in the
city where the authors reside1) and compare these values
across multiple routes between the same pairs of landmarks.
The landmarks chosen were frequently visited destinations
such as the work place of the authors, a major shopping cen-
ter, and a football stadium. Figure 1 shows the routes used in
the experiments. Each experiment was performed indepen-
dently from the other experiments using multiple cars and
different users. For the purposes of Experiment 1 and Exper-
iment 3, we used data collected from a Pontiac Grand AM,
1997. For Experiment 2, the car used to collect data was a
Honda Civic, 2002. The shortest and fastest paths are ob-
tained using commonly available mapping services such as
Google maps [15] and MapQuest [22] 2. We plot the fuel
consumption for the shortest path, the fastest path, and the
path that consumes the least fuel for these three experiments
in Figure 2.
We observe, from Figure 2, that in the first experiment,
the fastest path is also the most fuel efficient path. Whereas,
in the second experiment, the shortest path consumes the
least amount of fuel. In the third experiment, the most fuel-
efficient route is different from both the shortest and the
fastest routes (which happen to be the same). We conclude
from the above observations that simply choosing the short-
est or the fastest path will not necessarily result in the most
fuel-efficient path.
The most conservative estimate of fuel savings obtained
from the experiments shown in Figure 2 is about 10% (and
the average is 15% across all the three experiments). At the
current national average gas price (which is about $2), this
would be equivalent to a savings of at least 20 cents per gal-
lon at the pump, which is not bad for “cash back”.
To estimate the amount of savings that can be achieved on
a global scale, we provide back of the envelope calculations
based on data from the Environmental Protection Agency
(EPA) [11]. An estimated 200 million light vehicles (pas-
senger cars and light trucks) are on the road in the US. Each
of them is driven, on an average, 12000 miles in a year.
The average mile-per-gallon (mpg) rating for light vehicles
is 20.3 mpg. Even if 5% of these vehicles adopted Fueoogle
and the 10% fuel savings were achieved on only a quarter
of the routes traveled by each of these vehicles, the amount
of overall fuel savings is nearly 148 million gallons of fuel
((12000 ∗0.25)/20.3∗(0.05 ∗200M)∗0.1). This translates
into about one third of a billion dollars in savings at the pump
(based on the current national average pump prices for a gal-
lon of gasoline). The authors consider the above prospective
savings acceptable. The rest of the paper presents details of
the Fueoogle service.
1City name is removed for anonymity
2Google maps provides only the shortest path, MapQuest pro-
vides both fastest and shortest paths, hence we use MapQuest to get
route information
route
Most fuel efficient
Fastest route
Shortest route
(a) Figure showing the driving routes used in Experiment
1 (from point A to point B) and Experiment 2 (from
point C to point D). Routes with dashed and dotted lines
are shortest-path routes while routes with dash lines are
fastest routes provided from MapQuest system. The most
fuel efficient route is marked by solid lines. Experiment 1
was conducted using a Honda Civic, 2002 and Experiment
2 using a Pontiac Grand AM, 1997.
Most fuel efficient
route
Fastest route
Shortest route
(b) Figure showing the example of the most fuel-efficient
route that is different from the fastest and shortest-path route.
The route with solid lines is the fastest and shortest-path route
from point A to point B provided by MapQuest system. The
route with dash lines is the most fuel-efficient route. The car
used for collecting data was a Pontiac Grand AM, 1997.
Figure 1. Maps showing the experiments performed for the feasibility study
3 The Fueoogle System
The service provided by Fueoogle is similar to a regu-
lar map application, such as Google maps [15] or MapQuest
[22]. Google maps and MapQuest provide the shortest or
fastest routes between two points, whereas Fueoogle com-
putes the most fuel-efficient route. A snapshot of the Web-
based Fueoogle’s user interface is shown in Figure 3 along
with the most fuel efficient route between two points for a
0
0.05
0.1
0.15
0.2
0.25
3 2 1
Fuel consumed (gallons)
Experiment number
Shortest path
Fastest path
Fuel-efficient path
Figure 2. Figure showing fuel consumption for multiple
routes between multiple selected landmarks for different
cars and drivers
user with a Pontiac Grand AM, 1997. In the following sub-
sections, we will discuss the Fueoogle concept, then present
the participatory sensing framework that we utilize for data
collection and data sharing and the specifics of the hardware
used for the purpose of data collection.
Figure 3. Figure showing the user interface of Fueoogle
with the most fuel efficient route between two points on
the map for a Pontiac Grand AM, 1997 car model
3.1 The Fueoogle Concept
Individuals who want to compute the most fuel-efficient
route between two points enter the source and destination
address via the interface provided by Fueoogle. Members of
Fueoogle (i.e., those individuals who contributed participa-
tory data) can register their vehicles that were used for data
collection. Hence, Fueoogle can compute the route specifi-
cally for the registered vehicle. Other users may enter their
vehicle’s make, model, and year of manufacture, as well as
average mpg and tire diameter, if known (for better accu-
racy). Alternatively, those last two parameters can be looked
up from the model data. Since different vehicles have dif-
ferent fuel consumption characteristics, these car details are
used to compute the most fuel-efficient route for the given
vehicle brand. The advantage for the users who contribute
data is that the system provides better estimates of the most
fuel-efficient routes to these individuals, thus allowing them
to have higher savings. This is because the prediction models
(Section 4) will be more accurate for those individuals who
contribute data on their specific cars.
It is impractical to assume that Fueoogle members will
measure all city streets and cover all vehicle types. Instead
measurements of Fueoogle members are used to calibrate
generalized fuel-efficiency prediction models. These mod-
els, discussed in Section 4, show that the fuel consumption
on an arbitrary street can be predicted accurately from set
of static street parameters (e.g., the speed limit, the num-
ber of traffic lights, and the number of stop signs) and a set
of dynamic street parameters (such as the average speed on
the street or the average congestion level), plus of course the
vehicle type (specifically, its mpg rating and some known pa-
rameters such as wheel diameter and weight). It is the math-
ematical model describing the relation between these general
parameters and fuel-efficiency that gets estimated from par-
ticipant data. Hence, the larger and more diverse is the set of
participants, the better the generalized model.
For most streets, static street parameters can be readily
obtained from traffic databases. For example, the number of
traffic lights, the number of stop signs, and the speed limits of
streets can be obtained from the red light database [16]. Dy-
namically changing parameters such as the congestion levels
or average speed are more tricky to obtain. In larger cities,
real-time traffic monitoring services can supply these param-
eters [25]. Many GPS device vendors, such as TomTom, also
collect and provide congestion information. Finally, partic-
ipatory sensing applications, such as Traffic Analyzer [13]
and CarTel [19], have been described in prior literature that
have the potential to provide congestion and speed data. In
this paper, we use historic per-street-block traffic speed aver-
ages computed by the Traffic Analyzer participatory sensing
service. Fueoogle utilizes this service for (historic) average
congestion level information. The Traffic Analyzer archives
these averages for different city blocks as a function of the
time of day and day of the week, based on GPS data collected
from individuals with GPS devices (that are much more com-
mon than the OBD-II scanners). Hence, while Fueoogle is
not yet responsive to real-time conditions, such as accidents
on the road, it can still provide information on which of mul-
tiple routes is the most fuel-efficient on average at a given
time and on a given day.
3.2 A Participatory Sensing Framework
We utilize a participatory sensing framework, called
PoolView [13], to implement Fueoogle. Briefly, PoolView
is a set of infrastructure tools and protocols that enable indi-
viduals to set up new participatory sensing services and col-
lect data for them. PoolView consists of four layers; namely
sensing,storage,privacy, and aggregation. The sensing
layer encompasses participants’ sensors. It includes drivers
that allow them to upload data to the user’s private archive.
The private storage layer maintains the archive of sensory
data collected by the user. The privacy firewall layer imple-
ments various privacy policies to sanitize data by masking or
perturbing appropriate fields prior to sharing with external
participatory sensing services. The aggregation layer imple-
ments such services that aggregate sanitized data and com-
pute service-specific statistics. The communication between
these layers is achieved using an extension to the HTTP pro-
tocol.
We implemented Fueoogle as a participatory sensing ser-
vice (i.e., an aggregation server) in PoolView. An individ-
ual who wants to share their OBD-II sensor data can thus
download the client side software of PoolView, and use it to
upload their data to the Fueoogle aggregation server. The
aggregation server uses these data to calibrate models that
relate street and vehicle parameters to fuel-efficiency and of-
fers the Fueoogle query interface for fuel-efficient routes.
Individuals who wish to contribute OBD-II data to
Fueoogle can install, in their vehicle, any commercial OBD-
II scanner along with a GPS unit. In our deployments, we use
one such off-the-shelf device for data collection purposes.
Our hardware setup consists of an OBD-II scanner connected
to a GPS unit, as shown in Figure 4. We use DashDyno’s
OBD-II scanner [3] for collecting sensor data from a car and
a Garmin eTrex Legend GPS [14] to get location data. Dash-
Dyno has a GPS port allowing the Garmin to be plugged in.
The DashDyno records trip data (including Garmin’s GPS
location) on an SD card that the user later uploads to the
Fueoogle server.
Figure 4. Hardware setup used for data collection
A total of 19 parameters are obtained from the car and the
GPS, the most important of them being instantaneous vehicle
speed, total fuel consumption, rate of fuel consumption, lat-
itude, longitude, and time. The following section elaborates
on the implementation of the Fueoogle server.
3.3 Implementing the Fueoogle Server
The aggregation server provides a fuel-efficient route
computation service based on models generated from the
data collected. The aggregation server maintains a city’s
map as a directed graph, which we call the street graph, with
street intersections as nodes and streets as arcs. The graph is
directed because some streets are one-way and because fuel
efficiency may depend on the direction of motion. For ex-
ample, on uneven ground, one direction can be up-hill, while
the other down-hill, making fuel efficiency different in each
direction3. Each arc is assigned a set of parameters such as
the speed limit and whether the arc contains traffic lights or
stop signs. We call these the street parameters. A fuel con-
sumption model describes how to map these parameters to
consumed fuel.
The server allows registering user vehicles by mem-
bers. From the data uploaded for each registered vehicle
(namely, streets traversed and fuel consumed), a vehicle-
specific model is found by regression that maps street pa-
rameters to that vehicle’s fuel consumption. The purpose of
computing such a model is that it can then be used to predict
the vehicle’s fuel consumption on streets that this vehicle has
not traveled from street parameters.
In addition to computing vehicle-specific fuel consump-
tion models for individual registered vehicles, the data on
registered vehicles are aggregated into progressively larger
pools, that are classified, respectively, by (i) make, model,
and year, (ii) make and model, and (iii) make and type
(e.g., “compact”, “economy”, “midsize”, “full”, etc), and
(iv) type. A model isthen foundfor data in each pool. These
more general fuel consumption models are used to predict
fuel-efficient routes for vehicles that are not registered with
Fueoogle. Given the make, model and year of such a vehi-
cle, Fueoogle finds the most specific fuel consumption model
that matches that information, then uses it for prediction. For
example, to compute a fuel-efficient route for a Ford Taurus,
2001, that is not registered, the server first checks to see if it
has a fuel consumption model for a Ford Taurus, 2001, ob-
tained from registered vehicles; if not, then a model for Ford
Taurus in general; if not, then a model for full-size Ford ve-
hicles. If not, then a model for full-size vehicles in general.
As more vehicles register, models of narrower categories get
populated, but the generalizations are useful for early phases
of deployment.
Importantly, by parameterizing the vehicles themselves
(e.g., by mpg and tire diameter), Fueoogle is able to come up
with accurate models that estimate the fuel consumption of
one vehicle given data collected by another vehicle. These
models are especially good for accounting for finer differ-
ences between vehicles in one category (e.g., full-size ve-
hicles) but can also be applied across categories, as will be
shown in the evaluation.
When a query is posed for a fuel-efficient route from one
point to another in the city, the source and destination ad-
3While the authors appreciate the importance of accounting for
street incline as a model parameter, this study does not investigate
the effect of incline due to the flat nature of the terrain in the locale
where the study is performed.
dresses of the query are translated into nearest nodes in the
street graph. The most fuel-efficient route is the weighted
shortest path between these nodes of the street graph, where
the weights represent total fuel consumption on each arc,
computed using the arc’s street parameters and the model
used for the vehicle in question. This shortest path is com-
puted using the weighted Dijkstra’s algorithm. It is straight-
forward to extend the above algorithm when the source or
destination addresses, or both, are not nodes (i.e., not street
intersections). The fuel consumption for segments that rep-
resent partial arcs is approximated by multiplying the fuel
consumption for the arc by the ratio of the length of the seg-
ment to total arc length.
Members can upload more data on their vehicles to the
server at any time. The latitude and longitude (location)
information shared in conjunction with fuel consumption is
used to infer the street it corresponds to based on the shape-
files from the TIGER database [26]. The TIGER shapefiles
are spatial extracts from the US government’s census bureau
database which contain feature information regarding the lat-
itude and longitude of various streets/roads in a city. Apart
from these features, the database also contains railroad, river,
and points of interest information. The fuel consumption and
the street information are used to compute the model as de-
tailed below.
4 Prediction Model
One of the main contributions of this paper is to develop
a mathematical model that predicts the fuel consumption on
streets for which OBD-II measurements are unavailable. Al-
though a large number of people own cars, not many of them
have OBD-II scanner tools. The lack of widespread avail-
ability of these scanner tools implies that the data being con-
tributed by the users of our participatory sensing application
may be rather sparse. Hence, a primary research question is
whether one can derive good models for predicting fuel con-
sumption under conditions of sparse deployment. In other
words, can we use data collected by a smaller population
to build a model that is capable of predicting the fuel con-
sumption characteristics of those streets for which OBD-II
measurements are not available? In addition, the different
cars have different fuel economy factors that have great ef-
fect on the fuel efficiency. Thus the model should be able
to accurately predict the fuel consumption for different car
models.
There are several factors that affect the fuel consumption
on streets. We classify these parameters into four categories,
that are (i) static street parameters, (ii) dynamic street pa-
rameters, (iii) car specific parameters, and (iv) personal pa-
rameters. Static street parameters model the street character-
istics and do not change (or change very infrequently) over
a period of time. For example, the speed limits of streets
change very infrequently and the number of traffic lights on
the street remain more or less constant. The dynamic street
parameters are characteristics that change with time. For ex-
ample, the congestion levels on a street or the averagespeed
on a street. The static and dynamic street parameters together
determine the fuel efficiency of a particular street. Other
variations in the fuel consumption can occur due to the type
of car being driven and the nature of the person’s driving.
For example, a big car may consume more fuel than a small
sedan. Similarly, a person who is more erratic (higher ac-
celeration or hard braking) is likely to consume more fuel
than a more “careful” driver. These parameters account for
the variation in fuel consumption due to the car type and the
driver behavior.
Before we explain the details of the model, we provide
a brief description of the data collection for the purpose of
developing models.
4.1 Data collection
Our model is derived using data collected from six users
(with different cars) over the course of a month. A wide
range of cars were used in our experiments and a total of
about 90 miles were driven by the users. The details of the
car make, model, year, and the number of miles of data col-
lected for each car are summarized in Table 1.
Car make Car model Car year Miles driven
Pontiac Grand Prix 1997 24.5
Honda Civic 2002 10.55
Chevrolet Prizm 1998 15.5
Ford Taurus 2001 9.46
Mazda 626 2001 8.89
Hyundai Santa Fe 2008 21.4
Table 1. Table summarizing the cars used and the
amount of data collected
In our experiments, each user was given a DashDyno and
GPS system described in Section 3.2 and was asked to drive
around the city in which the authors reside. There were two
sets of experiments performed by us, one is a controlled set
of experiments, which enabled us to collect sufficient data
for a variety of streets. In these set of experiments, each user
was asked to drive around a specific set of major streets in
the city. Each street had various characteristics, such as the
speed limit, the congestion levels, and the number of traffic
lights. These controlled experiments captured the variables
affecting fuel consumption. The controlled experiments al-
low us to decide on the best model structure, as opposed to
estimating parameter values. They are done only once for
purposes of understanding what parameters to monitor, and
are not part of the participatory service itself. That service
will simply use the model structure we arrive at in this paper
and use participant data to estimate model parameter values.
The data from these controlled experiments were used to
build models. Parameters of these models were estimated.
The second set of experiments evaluated the efficacy of the
models in an uncontrolled setting. The users drove randomly
over several streets and collected (ground truth) fuel con-
sumption information for these streets. The fuel consump-
tion data for these streets were compared against Fueoogle
predictions and hence used to evaluate the accuracy of pre-
diction using our model.
For the purpose of modeling the streets, we note that the
data collected consists of several streets which are signifi-
cantly long. For example, many streets are as long as 1.5
miles. In order to capture the variation in the fuel consump-
tion within the longer streets (due to different traffic charac-
teristics), we divide such streets into smaller segments. Each
segment is considered as one training data point for the pre-
diction model. Note that, the collected raw data are not di-
rectly used. Instead, the model parameters (average speed,
real mpg) are extracted and used for training and testing pur-
poses.
4.2 Preliminaries
Predicting the fuel consumption using only static param-
eters of a street (e.g., number of traffic lights) or simple dy-
namic street parameters (e.g., average speed) with high ac-
curate is challenging because there are several other factors
that greatly affect the fuel consumption even if the street and
the car are fixed. For example, one might drive from home
to office every day at the same time and on the same route,
but the fuel consumption might vary greatly with the num-
ber of traffic lights encountered that were red on a giventrip
(as opposed to green). Simply counting the total number of
traffic lights, or even the average number of red lights en-
countered over a long time, does not accurately predict this
time-sensitive single-trip information. Therefore, fuel con-
sumption estimates of city traffic routes, based on any aver-
age metrics, are inherently inaccurate due to the noisy nature
of the random variable being estimated. What we hope for,
however, is to develop a prediction scheme whose residual
error has a zero mean. In other words, if the actual fuel con-
sumption is equally likely to be above or below the predic-
tion, the errors will tend to cancel out (e.g., on daily com-
mutes) and the total fuel consumption estimate over a long
time will be accurate. In other words, a scheme with a zero
mean error will still accurately predict one’s savings at the
pump, which is the basis for choosing fuel-efficient routes.
With the above in mind, we begin by plotting the variation
in the fuel efficiency across various streets and cars. We plot
the distribution of the miles per gallon (mpg) for the data
collected for all the users in Figure 5.
5 10 15 20 25 30 35 40
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Miles per gallon (mpg)
mpg distribution
Figure 5. Figure showing the real mpg distribution for all
the six users
We observe from Figure 5 that the distribution is very
wide, with the mpg values varying between 5 and 40. The
standard deviation of the mpg distribution is 7.75 mpg,
which is pretty high. We observe that the variation in the mpg
distribution is in part due to differences inherent to streets
and cars and in part to time-sensitive noise added as dis-
cussed above. Hence, it is desired to have a prediction model
that gives a prediction error with much smaller standard vari-
ation than the that of the overall mpg (7.75mpg).
The inputs to the prediction model include street param-
eters and car parameters. The goal of the prediction mod-
els is to be able to estimate the fuel consumption using only
general parameters of both streets and cars which are eas-
ily acquired (e.g., number of stop signs, car make, etc). The
lack of fine-grained parameters in the model makes it hard
to achieve a low prediction error. However, as mentioned
above, we are interested in the prediction error over a long
period of time. This error can be very small if the predic-
tion error follows a zero mean distribution. Therefore, it is
reasonable to measure the accuracy of the prediction mod-
els using the sum of signed errors instead of absolute errors.
The goal of our paper is to come up with a model with fair
absolute error but with a very low signed error. Hence, we
evaluate the developed model with both absolute and signed
errors in upcoming sections.
With that goal in mind, we consider a simple linear model
to predict the mpg for individual street segments. The mpg
of streets is modeled as a function of the various street and
car parameters described above. It is straightforward to com-
pute the fuel used from the mpg as distance of the streets
is known. We shall show that the model, developed in this
paper, achieves an absolute error of about 11.28%, on av-
erage, while the signed error is less than 2%, which is ac-
curate, considering that we are contemplating savings in the
10%-20% range as discussed in the feasibility section. We
also show that the total prediction accuracy for long routes is
much higher than the accuracy for the individual route seg-
ments, confirming the cancellation of noise.
Our linear model estimates the fuel consumption as the
weighted linear combination of the parameters. The system
needs to estimate the coefficient vector from the OBD-II data
shared by users in order to minimize the least squared mpg
error. Another advantage of the linear model is that it is pos-
sible to have a powerful online algorithm to update the co-
efficients of the model whenever new OBD-II data arrives,
essentially using a Kalman filter.
In the rest of this section, we will incrementally build a
model that achieves the aforementioned goals and results.
We will begin by looking at simple models and slowly evolve
into more sophisticated ones until the best is found.
4.3 The Single Car Model
In this section, we first consider models developed for
Fueoogle members. These models are dedicated to their in-
dividual cars. Being fuel efficiency models of a single car,
they incorporate only street parameters that the car’s perfor-
mance might depend on. To evaluate the accuracy of single
car models in our experiments, we use data from driving the
same car on one set of streets and evaluate the accuracy of
model in predicting fuel consumption for a different street.
In other words, the errors are computed based on the leave-
one-out cross validation scheme [21].
4.3.1 Prediction with Static Street Parameters
The static street parameters under consideration include
the speed limit (SL), number of stop signs (ST), and number
of traffic lights (TL). We consider only static parameters in
this section. Even though the error for the models presented
in this section is high, we are interested in understanding the
importance of the parameters that affect the fuel consump-
tion on various streets. We use the data sets from six different
cars described in Table 1.
The simplest model is a linear model that depends on only
one of the parameters, SL, ST, or TL. The absolute prediction
error and the corresponding signed prediction error for each
car are shown in Figure 6(a) and Figure 6(b), respectively.
We see from these two figures that the error in prediction is
quite high, as much as 30% absolute error and 10% signed
error (i.e., long-term total prediction error as a fraction of
long term total consumption).
Our next step is to combine two parameters (linearly) to
observe if more parameters can predict the mpg better than
the single parameter models. We plot the absolute and signed
errors for the three combinations of the static street param-
eters, (ST, SL), (SL, TL), and (ST, TL), in Figure 7(a) and
Figure 7(b), respectively. We observe from these two fig-
ures that the error in prediction does not change much, which
means that the mpg information contained in those static pa-
rameters are similar. We further justify this observation by
considering the linear model with all three static parameters.
These results are shown in Table 2.
Car make Absolute percentage Signed percentage
error error
Pontiac 27.05 9.18
Ford 22.06 6.23
Hyundai 26.65 9.08
Mazda 15.33 1.6
Chevy 18.81 6.81
Honda 28.87 1.01
Table 2. Absolute and signed prediction errors for each
car/user when all the static street parameters are used in
the model
Both the absolute and the signed errors for all static pa-
rameters considered in the model are approximately the same
across the simpler and more complex models. This means we
have nothing to gain from using a complex model that com-
bines the above static features. Instead, we pick the static
single-parameter model that performs best (in terms of per-
centage signed error).
To pick that model, we compute the average error across
all the users for each one parameter model in Table 3. We ob-
serve from Table 3 that the static parameter that best predicts
the mpg for all the streets is the number of stop signs (ad-
mittedly, this study was performed in a small campus town).
In the rest of this section, we consider models that com-
bine other parameters with the chosen static parameter (num-
ber of stop signs).
0
5
10
15
20
25
30
35
40
HondaChevyMazdaHyundaiFordPontiac
Percentage error
Car make
Speed limit
Traffic lights
Stop signs
(a) Absolute prediction error
0
5
10
15
20
HondaChevyMazdaHyundaiFordPontiac
Signed percentage error
Car make
Speed limit
Traffic lights
Stop signs
(b) Signed prediction error
Figure 6. Prediction errors for single static street parameter model for all the users
0
5
10
15
20
25
30
35
40
HondaChevyMazdaHyundaiFordPontiac
Percentage error
Car make
Stop signs and traffic lights
Traffic lights and speed limit
Speed limit and stop signs
(a) Absolute prediction error
0
5
10
15
20
HondaChevyMazdaHyundaiFordPontiac
Signed percentage error
Car make
Stop signs and traffic lights
Traffic lights and speed limit
Speed limit and stop signs
(b) Signed prediction error
Figure 7. Prediction errors for the two static street parameter model for all the users
Features Average absolute Average signed
percentage error percentage error
Speed limit 22.82 7.31
Traffic lights 22.2 6.94
Stop signs 22.3 6.68
Table 3. Average absolute and signed prediction errors
for all the cars for each of the models using static param-
eters only
4.3.2 Prediction with Dynamic Street Parameters
The dynamic street parameters are those that vary with
time. Examples of those parameters include average speed
of the vehicles on the street and congestion level. First, we
analyze the effect of the average speed on the mpg of the ve-
hicle on various streets. Studies by the U.S. Department of
Energy [27] show that the fuel economy (mpg) strongly re-
lates to the average speed of vehicles. Moreover, we observe
from the results in [27] that the fuel economy can be approxi-
mated by a polynomial in average speed (v) of order less than
three. Now, we consider three possible models which com-
bine the number of stop signs with v,(v,v2)and (v,v2,v3).
We individually train these models using data sets for dif-
ferent cars (to estimate coefficients of the above parameters)
and evaluate them using the leave one out cross-validation
scheme. The absolute prediction errors and the signed pre-
diction errors for these three models are presented in Figure
8(a) and Figure 8(b), respectively.
We observe that both the absolute prediction errors and
the signed prediction errors are significantly lower than those
of the model with static parameters only, which suggests that
average speed strongly correlates with fuel efficiency. How-
ever, there is no model that outperforms the others. This can
be explained by the fact that the average speed and the fuel
efficiency are likely to be linearly dependent in normal city
traffic (the average speed is less than 40 mph). Hence we
choose the best model that gives least signed prediction er-
ror. Table 4 summarizes both the absolute prediction error
and the signed prediction error for the three models across
all the users.
Features Average absolute Average signed
percentage error percentage error
ST and v17.24 4.17
ST, vand v216.45 3.83
ST, v,v2and v316.53 3.56
Table 4. Average absolute and signed prediction errors
for all the cars for each of the models using number of
stop signs (ST) and average speed (v)
0
5
10
15
20
25
30
35
40
HondaChevyMazdaHyundaiFordPontiac
Percentage error
Car make
Stop signs, v
Stop signs, v, v2
Stop signs, v, v2, v3
(a) Absolute prediction error
0
5
10
15
20
HondaChevyMazdaHyundaiFordPontiac
Signed percentage error
Car make
Stop signs, v
Stop signs, v, v2
Stop signs, v, v2, v3
(b) Signed prediction error
Figure 8. Prediction errors for the model with stop signs and average speed (v,v2,v3) for all the users
The results show that the model achieving least error is
the one with number of stop signs (ST), v,v2and v3. In other
words:
mpg =aST +bv+cv2+dv3+e(1)
where a,b,c,d, and eare the coefficients derived for the
vehicle in question.
Yet another dynamic factor that can improve the predic-
tion error is the congestion level. Higher congestion levels on
streets result in lower fuel efficiency, as vehicles move slowly
in congested streets (thus consuming more fuel). Hence,
we introduce the congestion parameter that approximates
the congestion level. The congestion parameter of a certain
street is defined as the ratio of the average speed of the ve-
hicles on the street to that of the speed limit on the street.
We augment the best model achieved in Equation 1 with the
congestion parameter and evaluate the performance of this
new model using leave one out cross validation method. The
results are shown in Table 5.
Car make Absolute percentage Signed percentage
error error
Pontiac 13.79 4.60
Ford 19.31 2.88
Hyundai 23.23 6.78
Mazda 14.36 3.05
Chevy 11.65 2.76
Honda 18.97 6.03
Table 5. Prediction error for each car/user with conges-
tion level parameter
We compare the results of the model without the con-
gestion parameter (Figure 8(a) and Figure 8(b)) with that of
the results in Table 5. We observe that the model with the
congestion parameter augmented does not improve over the
model without the congestion parameters (Equation 1). This
can be explained as the average speed in the original model
also contains information about the congestion, thus adding
a scaled version of the speed does not help in improving the
model. Therefore, we choose not to add the congestion pa-
rameter into our final model.
We now consider the effect of the amount of training data
used on the accuracy of prediction. We partition the data
set of one car (the Pontiac Grand Am) into two sets. The
first data set contains the data points recorded from one spe-
cific street. The second data set contains the rest of the data
points. We train the model with the data points taken from
the second data set and test the model on the first data set.
We also vary the size of the training data to see the effect of
the number of training data points on the performance of this
model. We repeat the experiment for several training sets
and average the prediction errors. The results for absolute
prediction errors and signed prediction errors are shown in
Figure 9(a) and Figure 9(b), respectively.
The results show that this model generalizes well to dif-
ferent streets even with small number of data points. On
average, the model needs 40 data points to gives reasonably
good prediction error for the streets that don’t have any data
points. This is a good number since the number of training
data points for real participatory applications can be as big as
thousands of data points. In the next section, we explore how
models generalize across cars by incorporating car-specific
parameters into the model.
4.4 The Generalized Model
Car type is an important factor that affects the fuel effi-
ciency. In our experiments, the fuel consumption of an SUV
is higher than that of a sedan by as much as 20% on the same
street at the same time of the day. Hence, it is desired for
the model to be able to accurately predict the fuel efficiency
across multiple types of cars. This allows Fueoogle to derive
fuel efficiency of a vehicle, even when it has no prior data
collected for that type of vehicle.
In order for the model to accurately predict the fuel effi-
ciency across the different types of cars, car-specific param-
eters need to be incorporated into the prediction model.
The most important car-specific factor that affects the fuel
efficiency is the average mpg of the specific car. Using this
observation, we can use the car’s average mpg as a parameter
for the model. In the new model, the set of features do not
change, however instead of finding the coefficients to predict
the real mpg, we now find the coefficient to predict the nor-
malized mpg, defined as the ratio of average mpg on a given
0 10 20 30 40 50 60
26
28
30
32
34
36
38
40
42
Number of Training Data Points
Error
(a) Absolute prediction error
0 10 20 30 40 50 60
6
8
10
12
14
16
18
Number of Training Data Points
Signed Percentage Error
(b) Signed prediction error
Figure 9. Prediction error with increasing number of training data points for the user driving the Pontiac Grand AM,
1997
0
20
40
60
80
100
120
140
160
180
200
HondaChevyMazdaHyundaiFordPontiac
Percentage error
Car make
Standard mpg
Average mpg
(a) Absolute prediction error
0
20
40
60
80
100
120
140
160
180
200
HondaChevyMazdaHyundaiFordPontiac
Signed percentage error
Car make
Standard mpg
Average mpg
(b) Signed prediction error
Figure 10. Prediction errors when other cars’ data are used for training the models that incorporate standard mpg and
average mpg
street to the car’s rated mpg.
The long-term average mpg for a specific car can be found
in two ways: either the car owner has a sense of the average
mpg or it can be provided by the manufacturer or by EPA
[11]. The information provided by the manufacturer might
not be a good estimate of a specific car’s mpg since the av-
erage mpg of same type of car may differ as much as 10mpg
[11], which may result in poor prediction performance. In
this paper, we evaluate the model using both standard mpg
(provided by EPA) and average mpg (provided by the owner
of the car). In order to evaluate the accuracy of the model
in predicting the mpg across different cars, we use all data
points of one car as the testing set while use all other data
points of other cars as training data. Figure 10(a) and 10(b)
shows the absolute error and signed error for both models
(using standard mpg and real average mpg), respectively.
Significant difference in the error performance of the
model with different parameters can be seen from those fig-
ures. The model performs badly on the when using the stan-
dard average MPG from EPA whichmeans that those values
are pretty far from the accurate mpg of the car. On the other
hand, the prediction model using the real average of the car
performs extremely well.
In order to justify the improvement of the model after in-
corporating the car’s mpg information, we train the single-
car model described in Equation 1 across all the cars but one
and test on the other car. The prediction error is then com-
pared with the result for the multi-car model (with real car
mpg). The signed prediction error for both single-car model
and multi-car model is plotted in Figure 11.
We can see that the multi-car model outperforms the
single-car model in most cases. It means that the normal-
ized mpg is a better parameter for the prediction model.
Another car-specific parameter that is considered in the
paper is the wheel size of the car. Fuel-efficiency will slightly
drop with a smaller wheel size at a given speed because to
maintain that speed, the engine and drive train have to rotate
at a greater speed thus friction losses will be higher. We hy-
pothesize that the mpg loss can be accurately linearized so
it is proportional to the wheel size of the car. We evaluate
our hypothesis by using wheel size of the car as a parameter
to the model (which includes static parameters, dynamic pa-
rameters and car’s real mpg). The model is tested using all
the data points of one car and is trained using data points of
0
20
40
60
80
100
120
HondaChevyMazdaHyundaiFordPontiac
Percentage error
Car make
Single car model
Multi car model
Figure 11. Signed prediction error for each car/user for
the single car and multi car models
all other cars. The results is showed in Table 6.
Car make Absolute percentage Signed percentage
error error
Pontiac 12.23 2.21
Ford 9.69 1.49
Hyundai 15.58 3.15
Mazda 13.7 2.81
Chevy 11.72 1.56
Honda 5.86 0.72
Table 6. Prediction error for each car/user when the
wheel size is considered in the model
We observe from the results that the absolute error is
about 11.46% while the signed error is 1.99% which is better
than the previous model without wheel size information.
4.4.1 Driver Specific Model
In order to incorporate the human factor into our final
model, we introduce a driver specific parameter. We want
to choose a metric that captures the driving behavior of the
users. For example, an individual who is used to braking
hard or accelerating fast is likely to consume more fuel than
a person who coasts to a stop or accelerates normally. We
propose to use a parameter called the lifetime speed variance
for a single user. This metric is the speed variance computed
over the entire speed data for the given user (such a metric
can be computed only for the Fueoogle members).
We compute the absolute percentage error and the signed
percentage error for the six users using the method similar to
the one presented in Section 4.4. These results are shown in
Table 7.
We observe from Table 7 that the prediction accuracies
decrease when the driver specific parameter is introduced
into the final model. The average absolute percentage error
is 16.53% and the average signed percentage error is 3.56%.
Thus, the driver lifetime speed variance is not a useful met-
ric for our model. Hence, our final model does not have the
driver lifetime speed variance. Finally, we observe that the
Car make Absolute Signed
percentage error percentage error
Pontiac 13.72 2.52
Honda 17.59 2.12
Chevrolet 22.87 6.7
Ford 13.16 2.14
Mazda 12.86 1.86
Hyundai 18.97 6.04
Table 7. Absolute and signed prediction errors for each
car/user when the human factor is introduced into our
final model
model developed in Section 4.4 achieves an average signed
error of 1.99%, which is quite small. This error is accept-
able for our application and hence we do not explore further
parameters. We will now discuss our final model in the next
Section.
4.5 Final Model Discussion
In the previous sections, we developed a linear model that
can accurately predict the fuel consumption across city traffic
streets and car types. We will summarize this model below.
The input to the model includes:
•Static street parameters: Number of stop signs (ST)
•Dynamic street parameters: v,v2,v3where vis the av-
erage vehicle speed on a specific street.
•Car specific parameters: average mpg (mpg) and wheel
diameter (dw).
The final model is expressed as
mpgn=aST +bv +cv2+dv3+edw+f(2)
where a,b,c,d,e,fare the model coefficients that are es-
timated in the training phase. mpgnis the normalized mpg
which is computed as mpgn=mpg/mpg.
In the Fueoogle application, the static street parameters
are automatically determined from existing databases such
as the Red light database [16]. The average speed for each
street is computed from GPS data contributed by users. For
the street having no GPS information, then the average speed
is guessed by the software as the average community speed.
Car specific parameters are supplied by the users. The out-
put of the prediction model is the normalized mpg for that
car/street. Fueoogle multiplies this number with the vehicle
average mpg to get the real mpg for that car/street.
We now evaluate the overall performance of the final
model using the leave one out cross validation scheme on
the all the data set of six cars. As discussed in Section 4.2, it
is desirable for the standard deviation of the error of the final
model to be smaller than the standard deviation of the data
itself. In addition, we are only concerned about the signed
error distribution since it represent the typical error behavior
when estimating long street segments. The error distribution
for signed error of the final model is plotted in Figure 12.
The error distribution in Figure 12 resembles a Gaussian
distribution with zero mean and standard deviation of 0.1434
mpg, which is significantly smaller than the standard devia-
tion of the real data itself (7.75 mpg). Two sigma rule tells us
−0.4 −0.2 0 0.2 0.4 0.6
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Error Value (mpg)
Error Distribution
Figure 12. Distribution of the signed error value for the
general model for all the cars and streets
that the prediction error is less than 0.2868 mpg with proba-
bility of 95% which is very good.
4.6 Updating the Model
Since Fueoogle is a participatory sensing application that
provides a long term service for the community, there is a
need to update the model using new OBD-II data to prevent
inaccuracy due to out-dated model. To update the prediction
model, one solution is to augment old data with newer data
and solve the least squared optimization to find new model
coefficients. However this solution does not scale because
of the accumulation of OBD-II data over years and the com-
putational complexity to solve the least squared optimization
with huge input data. Therefore there is the need to find an
online algorithm to update the model coefficients using just a
small number of past variables and new OBD-II data. In this
paper, we present an online algorithm to update the model
coefficients based on incremental gradient method [6].
For simplicity, we denote xas the current model co-
efficient vector with m elements corresponding to m fea-
tures discussed in Section 4.5. The set of new OBD-II
data features is denoted as as C= (C1,C2,...,Cn), and Z=
(z1,z2,...,zn)is the target MPG get from the new OBD-II
training data. We compute the model coefficient by mini-
mizing the following unconstrained quadratic optimization
xi=argmin i
∑
j=1|zj−Cjx|2(3)
The incremental gradient method iteratively finds the the
optimal coefficient vector xfor each 1 ≤i≤n. The opti-
mal solution of xat step iis denoted as xi. The incremental
gradient solution for this linear curve fitting as follow
xi=xi−1+H−1
iCT
i(zi−Cixi−1)(4)
Hiis also iteratively computed using following equation
Hi=Hi−1+CT
iCi(5)
The initial condition is x0=0 and H0being arbitrary pos-
itive definite matrix. Interestingly, this equation is a real-
ization of the Kalman filter [6]. Readers are encouraged to
refer to [6] for more discussion of the incremental gradient
method for linear system.
In order to update the coefficient, we only need to store
an mxm matrix H in addition to the old coefficient which is
extremely resource efficient. One property of the Kalman fil-
ter is that the model parameters converge to a new state very
fast when the characteristic of the system change. This guar-
antees our system to be up to date when there are changes in
traffic characteristics.
5 Evaluation
In a sense, the performance of the fuel consumption mod-
els we presented has already been evaluated in the context
of deriving the best model structure. We therefore present in
this section only a small number of additional experiments
that confirm the efficacy of the winning model. We evalu-
ate how the system performs both in terms of the accuracy
of the model in predicting end-to-end fuel consumption for
long routes as well as in terms of ability to find the most fuel
efficient paths. We use the data collected from the second
set of experiments (the data collection is described in Sec-
tion 4.1) to compute these results.
For each of the considered routes, we compute the actual
fuel consumed and the predicted fuel consumption from the
Fueoogle system (which uses the final model described in the
previous Section). Figure 13 shows the routes of two differ-
ent users. These computed results along with the percentage
error for the end-to-end path are shown in Table 8.
Car
make/ Actual Predicted Percentage
Path fuel (gallons) fuel (gallons) error
Pontiac
(A-B) 0.0767 0.0757 1.3
Pontiac
(A-C) 0.0786 0.0760 3.3
Ford (D-
E) 0.0980 0.0948 3.2
Chevy
(A-B)∗0.0817 0.0897 2.3
Chevy
(A-B)∗∗ 0.0789 0.0857 8.6
Table 8. Table showing the actual and predicted fuel con-
sumption in gallons for the routes shown in Figure 13
along with the percentage error in prediction. The first
entry for Chevy (A-B)∗is for the fastest route and the sec-
ond entry (A-B)∗∗ is the most fuel efficient route chosen
by Fueoogle.
We observe from Table 8 that the percentage errors for the
end-to-end routes for most of the paths are quite small. The
average prediction error for the end-to-end path is 3.75%.
This demonstrates that Fueoogle achieves a small percentage
error in predicting the fuel consumption for the end-to-end
paths.
User 1
User 2
(a) Figure showing the routes chosen for two different
users for evaluating the goodness of our final model. User
1 drives a Pontiac Grand AM, 1997 and User 2 drives a
Ford Taurus 2001.
Fastest route
Most fuel efficient
route
(b) Figure showing two routes chosen for one user be-
tween two landmarks. The most fuel efficient route which
Fueoogle picks is marked by a solid line, whereas the
fastest route is marked by dashed lines. The user drives
a Chevrolet, Prizm, 1998.
Figure 13. Maps showing the driving routes for the purpose of evaluation
Further, we asked our participants to choose a random
route between two points and measure fuel consumption,
then ask Fueoogle and follow its directions between the same
endpoints, measuring fuel consumption again. All partici-
pants reported fuel savings. One such experiment is shown
in Figure 13(b). The user drove on two different paths be-
tween landmarks A and B. One was the fastest route from
MapQuest and the other is a route by Fueoogle. Fueoogle
picked the route that consumed less fuel, when compared
to the fastest route from MapQuest. The most fuel-efficient
route is marked by a solid line in Figure 13(b).
We conclude from the above observations that Fueoogle
predicts the fuel consumption for end-to-end routes with a
high accuracy and also chooses the most fuel efficient route.
6 Related Work
We divide this section into three parts, the first part
presents related work in participatory sensing and the sec-
ond examines fuel efficiency related literature.
6.1 Participatory Sensing
The concept of participatory sensing was introduced in
[8]; participatory sensing is where individuals are tasked
with data collection which is then shared for a common pur-
pose. A broad overview of such applications was later pro-
vided in [1]. Several early applications have been published.
Examples include CenWits [18], a participatory sensing net-
work to search and rescue hikers, CarTel [19], a vehicular
sensor network for traffic monitoring, BikeNet [10], a bik-
ers sensor network for monitoring popular cyclist routes,
and ImageScape [23], cellphone camera networks for shar-
ing diet related images. Our application, Fueoogle, intro-
duces a novel participatory sensing application that enables
individuals to obtain fuel efficient routes within a city.
6.2 Fuel Efficiency
A comprehensive study that provides optimal route
choices for lowest fuel consumption is presented in [12]. In
the paper, fuel consumption measurements are made through
the extensive deployment of sensing devices (different from
the OBD-II) in experimental cars. These fuel consumption
measurements are then used to compute the lowest fuel con-
sumption route. As opposed to the work in [12], our paper
uses a sparse deployment to build mathematical models for
predicting fuel consumption on streets that lack the real mea-
surements. In [7], the influence of driving patterns of a com-
munity on the exhaust emissions and fuel consumption were
studied. Feedback was provided to the community regarding
the driving patterns to cut back on the fuel consumption and
exhaust. A driver support tool, FEST, was developed in [9].
FEST uses sensors installed in the car along with a software
to determine the driving behavior of the driver and provide
real-time feedback to the individual for the purpose of re-
duction in fuel consumption. An extension to FEST that in-
cludes more experiments and further evaluation can befound
in [28]. A feedback control algorithm was developed in [24]
that determines speed of automobiles on highways with vary-
ing terrain which achieve minimal fuel consumption. An ex-
tension to the work in [24] was developed in [17]. In [17],
suggestions of driving style to minimize fuel consumption
were made for varying road and trip types (e.g. constant
grade road, hilly road). The problem was formulated using a
control theoretic approach.
In a separate study [20], it was shown that rising obesity
has a significant impact on the total fuel consumption of the
US. Models were developed that studied the impact of obe-
sity on the amount of fuel consumed in passenger vehicles.
In our work, we develop models that estimate fuel con-
sumption of streets based on measured parameters of the
given street (e.g. speed limit of street, number of traffic
lights). These estimates of fuel consumption of streets are
then used to compute fuel-optimal routes.
7 Conclusions and Future Work
In this paper, we developed a participatory sensing appli-
cation, called Fueoogle, that provides a service which com-
putes fuel efficient routes from one point to another in the
city where the authors reside. This service relies on OBD-
II data collected by a set of users who share their data with
Fueoogle using a previously published participatory sensing
framework, called PoolView. The paper shows that signif-
icant fuel savings can be achieved by using Fueoogle in a
larger community, which not only reduces the amount of
money spent by people on their daily gasoline consumption,
but also has a positive impact on the environment by reduc-
ing the amount of CO2emissions. We show that Fueoogle
can utilize a sparse deployment to estimate the fuel consump-
tion on streets that lack OBD-II measurements, as well as es-
timate fuel consumption of vehicles using data on other vehi-
cles. Fueoogle achieves this by using the model developed in
this paper to estimate the fuel consumption for those streets
and vehicles that lack their own OBD-II measurements. Our
future work will address the impact of privacy-preservation
mechanisms such as data perturbation on the correctness of
aggregate fuel statistics computed by Fueoogle, as well as
gain experience from a long-term deployment.
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