This document describes a context-aware automatic traffic notification system for cell phones that can learn a user's common destinations and routes over time using location and context data. It collects GPS and other data from users, identifies important locations through clustering, learns frequent routes between locations, and can predict a user's destination and route to then notify them of any traffic conditions. The system is implemented on a mobile phone to provide automated traffic alerts to users during their daily commutes without needing to manually enter a destination.

1. A Context Aware Automatic Traffic Notification System for Cell Phones
Keshu Zhang, Kari Torkkola, Haifeng Li
Christopher Schreiner, Harry Zhang, Mike Gardner
Intelligent Systems Lab
Motorola
Tempe, AZ 85282
keshu.zhang@motorola.com
Zheng Zhao
Computer Science and Engineering
Arizona State University,
Tempe, AZ 85282
Abstract
Adaptation of devices and applications based on contex-
tual information has a great potential to enhance usability
and mitigate the increasing complexity of mobile devices.
We have developed a context aware system that can auto-
matically notify user of the traffic conditions by predicting
their destination and route using context. In this paper, we
describe the system in detail, including data collection, user
interaction, context fusion, and learning and prediction of
user’s destination and route.
1. Introduction
With context aware capabilities, cell phones will not only
act as personal communication devices but also as gate-
ways to services in diverse environments that support per-
sonalized interactions and proactive assistance tailored to
the user preferences and behaviors [1, 2, 3]. Context aware-
ness may also enhance usability and mitigate the increasing
complexity of mobile devices as shown in this paper. Today,
personal or car navigation systems are accurate and easy
to use for navigation purposes [4, 7]. However, they are
unnecessarily complicated for displaying traffic conditions.
Note that we usually need traffic information of our famil-
iar daily commute routes, for which navigation systems are
not needed. Current navigation systems require the user to
enter the destination in order to get first the route and then
traffic information. However, it is not convenient to enter
addresses on a regular cell phone. A smarter device which
can learn and then predict user’s common destinations and
automatically notify of traffic problems would be more de-
sirable.
In response to this requirement, we have developed
an automatic traffic notification system that is able to
learn user’s important semantic locations, identify frequent
routes between semantic locations, predict user’s destina-
tions given the current context, and to inform the user of the
traffic conditions when there are problems along the route.
The rest of the paper is organized as follows. Section 2
describes data acquisition. Learning and prediction of se-
mantic locations and routes are discussed in Section 3 and
4. In Section 5, we describe the destination and route pre-
diction. The system structure is also described in this sec-
tion. Section 6 presents the conclusions along with some
directions for further research.
2. Data Acquisition
Fourteen participants have taken part in this study. These
participants come from three groups of interest: students,
office employees, and independents. The student group
consists of six undergraduate students. The office employee
group consists of five persons who worked regular business
days at a fixed location. The independents group includes
three persons who were either stay-at-home parents or busi-
ness people who did not work consistently in an office en-
vironment (e.g. realtors).
Each participant was provided with a commercially
available mobile phone equipped with Bluetooth, along
with a separate Bluetooth GPS receiver to acquire location
data. Data logging software was installed on the mobile
phone. Each participant used the phone and the GPS re-
ceiver for a two- to three-month period.
The data logger recorded GPS data, cell id, and times-
tamp every 30 seconds. The data was uploaded over the air
to our web server transparently to the user.
3. Learning of Important Locations
Based on the collected context data, we employ machine
learning techniques to determine the important locations
where user spends most of his/her time or most often is en-
gaged in some activities.
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2. Office worker. College student. Businessman.
Figure 1. Discovered important locations (represented as black spots) for different users. The size
of a black spot represents its importance.
Location learning is mainly based on the collected GPS
data. However, the GPS signal may get lost due to various
reasons, such as entering into buildings or concrete canyons
in urban areas or because GPS device powers off. To com-
plement, we also use cell id as a secondary source to re-
sample GPS data. The void GPS data is made up by repeat-
ing the last valid GPS reading, if the area where GPS signal
disappeared and reappeared, is covered by the same group
of cell ids.
Learning important locations can be formulated as a
clustering problem. We employ DBSCAN (density-based
clustering) algorithm [5] because it can automatically find
the number of clusters, identify outliers, works well for
arbitrary-shaped clusters, and is also very efficient for large
datasets [6]. The discovered clusters will be the candidates
of important locations.
In most location based services, the importance of a lo-
cation is defined as how long the user spends time there.
In terms of clusters, the size of the clusters or the number
of points in a cluster represents how long the user spends
time at those locations. However, this definition ignores lo-
cations that user frequently visits but stays for a very brief
time (e.g. dropping child off for school every day). Mean-
while, a location (e.g. travel attraction) that user visits rarely
but stays for a long time, would be misclassified as an im-
portant location. To solve these problems, we apply down-
sampling processing to location clusters before calculating
the importance of clusters. This procedure considers both
how long and how often the user visits and stays in a lo-
cation. A parameter ΔT also controls the compromise be-
tween the two factors.
Each location cluster Ci, i = 1, . . . , N, is defined as
Ci = {{Pt}
ni(k)
t=mi(k)}Ti
k=1. Thus Ci contains several pieces
of continuously sampled GPS data sets, which means loca-
tion cluster Ci has been visited Ti times, each time during a
time period of ni(k)−mi(k). With the down-sampling pro-
cedure, the number of samples in Ci is specified as the sum
of ni(k)−mi(k)
ΔT . With this procedure, the weight of each lo-
cation cluster will reflect not only the time period spent at
that location, but also the frequency that that location be
visited. For example, suppose ΔT = 1200 seconds and
the original GPS sampling period is 30 seconds. If the user
spends 4 hours in location for only one day, but 5 minutes
at B every day, then the original GPS sample size for A is
480 points and for B will be only 10 points each day. The
weight ratio of location A to B is 48 : 1. But, with down-
sampling, the sample size of A changes to 12 points while
the sample size of B changes to 1 point each day. In about
2 weeks, the sample size of location B can exceed location
A, resulting in location B becoming more important than A.
By choosing different ΔT, we can adjust how important the
visiting frequency will be in identifying the significant loca-
tions. For example, if we choose the GPS sampling rate as
ΔT, then time spent is the only factor for important location
identification. On the other hand, if we choose 1 day as ΔT,
then the visiting frequency is the only factor for significant
location discovery.
After down-sampling, the duration of the total visit as
well as the visiting frequencies are both taken into consid-
eration in determining the location cluster weight. Clusters
with the largest weights will be regarded as “important lo-
cations”. Figure 1 shows the discovered important locations
for three subjects from different groups. The sizes of the red
spots represent the importance of the discovered locations.
4. Learning and Prediction of Routes
Currently, most transportation route learning methods
[7, 8] are based on geographic information systems (GIS).
Because our system runs on cell phones with limited com-
puting and storage resources, we instead use raw GPS data
sequences to represent routes. A GPS string between an
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3. important location pair is denoted as a route. The starting
point and ending point of the route involves detecting of the
user leaving one important location boundary and entering
into another important location boundary. In fact, through
location clustering, the cluster boundaries also serve as the
important location boundaries.
Although it is easy to extract GPS data sequence between
two locations as routes, it is not straightforward to deter-
mine if two sequences represent the same routes. Note that
people often have several different daily routes for the same
location pair. The system has to distinguish them for provid-
ing traffic information by comparing GPS data sequences.
To solve the problem, we developed a MiniMax cri-
terion to compare two routes. Suppose two routes of
GPS sequences are R1 = {g1
1, g1
2, . . . , g1
n} and R2 =
{g2
1, g2
2, . . . , g2
m}. The best match of R1 in terms of R2 is
ˆR1 = {ˆg1
1, ˆg1
2, . . . , ˆg1
n}, where ˆg1
i = arg ming2
j
||g1
i − g2
j ||.
Similarly, ˆR2 = {ˆg2
1, ˆg2
2, . . . , ˆg2
m} is the best match R2 in
terms of R1, where ˆg2
j = arg ming1
i
||g1
i − g2
j ||. We re-
garded two routes as the same if max ||g1
i − ˆg1
i || < d1 and
max ||g2
j − ˆg2
j || < d2, where d1 and d2 are the largest sam-
pling intervals of R1 and R2, respectively.
5. Traffic Notification
Based on the learned important locations and routes, we
developed a software agent on cell phone to automatically
notify the user of traffic conditions as the user starts driving.
The system starts predicting the transportation route only
when the user’s driving intention is predicted. Currently,
without additional information, we relay only on the GPS
information. When the system finds from GPS location and
speed information that the user is leaving a boundary of one
of the important locations, it will start inferring the possible
transportation routes of the user. To predict the transporta-
tion route, we first predict the possible destinations that the
user may travel to. The inference is based on the conditional
probabilities of destinations given the current location and
time. Then a list of frequent routes can automatically be
selected from the time based route frequency tables.
We have implemented the algorithms and methods on a
Motorola A1200/Ming cell phone that pulls the map and
traffic information from a private internet server along the
predicted routes. When the users driving intension is pre-
dicted from current context, a pop-up window will appear
on the visual display informing the user that the applica-
tion has detected the transition and is searching for traffic
information. Soft keys will be allocated to allow the user
to edit or cancel this function. No audio or haptic feed-
back will be provided to alert the user in case the user is
not interacting with the mobile device during this time. To
adopt a user-centric approach and minimize the obtrusive-
ness, traffic history may be studied to derive traffic averages
and variances and traffic alerts are only provided when cur-
rent or predicted traffic flow exceeds the average by a cer-
tain margin (e.g., one standard deviation above the average
flow). If a traffic alert is detected that is out of the ordinary
for that route and time of day (so that if the route the user
takes is always delayed by ten minutes due to heavy traf-
fic, the user will not always be reminded of this), then the
user will receive an audio or haptic alert, depending upon
the phone’s current profile settings. In addition, a pop-up
window will appear alerting the user that there is an alert,
and offering the user the choice of ignoring the alert or ob-
taining more information. If the user selects the latter, more
detailed information on the alert (e.g. type of incident and
location) will be provided with an option for the application
to recommend a new route to the predicted location.
6. Conclusions
In this paper, we briefly described a cell phone based au-
tomatic traffic notification system. By inferring the user and
usage context, this context aware phone can recognize the
current situation and user’s needs, and predict user’s future
goals to offer proactive assistance. We envision that context
aware and personalization features will become essential in
supporting new applications and concepts that connect con-
sumers with media and services through multiple devices in
the office, car, or home.
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