Motivation
 Traffic signal schedule information : unavailable
 Drivers : Accelerate, decelerate, and undergo a complete halt
 Causes increased fuel consumption, air pollution, and even road
accidents.
Speed-advisory driving system
 Map-independent and infrastructureless, only runs on smartphones and a
server
 Traffic signal schedule is inferred
and calibrated dynamically by the
Internet server when there are vehicles
accelerating as the light turns green
 Maximize the probability
that drivers do not have to
undergo a complete halt at
intersections
Smartphone capabilities
 3-axis Accelerometer: measures the acceleration applied
to the device in device’s body coordinate system, including
the force of gravity.
We need Acc. to infer intersection location and traffic
signal phase transition.
 3-axis Magnetometer: measures the geomagnetic field in
device’s body coordinate system.
We need Mag. to serve as a reference to transform
acceleration into different coordinate systems.
 Global Positioning System: average accuracy of position
is about 4-11 meters; devices’ position (longitude and
latitude), speed (m/s), bearing (heading direction, in
degrees), UTC time (in milliseconds since January 1, 1970),
accuracy (in meters)
Coordinate Systems
 Body Coordinate
System:
 Local North-EastDown Coordinate
System:
(Local NED coordinate
system)
Transformation from body coordinate system
to local NED coordinate system
Rotation Matrix
 Transform acceleration vector from body coordinate
system to Local NED coordinate system:
Transformed Acceleration
in local NED coordinate system
Acc. in
Local
NED
Coor.
Sys.
Time (s)
Constructing Road-intersection Topology
 Volunteers : transmit acceleration data and GPS traces
to the central server when wifi is available.
 Acceleration from zeros speed mostly happens at the
intersection with traffic lights.
 Volunteers should try to avoid using this application in
parking lots or residential areas so as not to produce false
positives.
 Allow noise and acceleration on road segments.
Acceleration location vectors
Magenta vectors
are valid acc.
Black vectors are
invalid acc.
Mean shift to locate possible intersections
We use a
method
similar to
mean-shift to
locate
candidate
intersections.
Find possible intersections
Bad ones: filtered away
It may be parking lots or
residential areas
Valid ones:
It may be intersections
Road segment construction
Raw GPS
data sent by
individual
smartphone.
Shown in
different
colors.
Road segment construction
Use anchor points
(red circle with
arrow) to simplify
map data.
Anchor points
generated similar
to mean-shift.
Road segment construction B-Spline
Use generated
anchor points to
reconstruct road
segment. Here is
an example.
Road segment construction
Ground truth
from Google
Map.
Traffic signal schedule inference
 Smartphones transmit traffic phase transition
signal to the server.
 Message:
{intersection ID, index of in/out branch, time interval
from acceleration to transmission, zero-speed state time interval,
vehicle ID}
 Assume the popularity of this app.

Assume drivers obey traffic rules
Phases in intersections
Phase
Phase
Phase
Phase
Phase length and sequence inference
 Smartphone sends its phase Si
(i=0,1,2,3), acceleration time tsi
(i=0,1,2,3) to the server.
 Find the min{ΔTsi} as the traffic
signal cycle length T.
 Find the closest phase following each
phase Si, so (tsj-tsi) is the duration
of Si, denoted as dtsi.
 Sequence of phase {S0, S1, S2, S3}
and the phase length {dts0, dts1,
dts2, dts3} is determined, although
not accurate.
Traffic signal timing calibration

Upon the arrival of the starting time (tsi) of
phase Si, (i=0,1,2,3) the server judges whether
the prediction is ahead of real time or is lagging
behind, and then record the event time (tsi) and
the number of times (nsi) each phase occurred.
Into
vectors:
ts
ns0 ns1
ns2
ns3
Traffic signal timing calibration
 To find the calibrated starting time (t0), and phase length (dtsi), we
minimize the Mean Square Error.
 Target function:
 Taking partial derivative of each variable and
let it equal to zero:
Server Smartphone communication
 Routing: When the driver launches the system,
the destination is specified and sent to server, the
server calculates the route that takes least time,
using Dijkstra algorithm.
Server Smartphone communication

Traffic signal schedule downloading & Optimal speed
calculation: as the vehicle goes, the timing information of the two
intersections ahead is provided by the server. Using the timing
information and the GPS and map data, the smartphone calculates
the best speed and advise the driver.
Other Applications of this system
 Commercial map revision and refinement.
We can provide a considerable amount of trace data that can be
used to extract lane information and revise some inaccuracy of
road and intersection position in some commercial maps such
as Google Map.
 Traffic signal planning advisory service.
The central server collects average vehicle speed of a particular
road. If the average speed is much lower than a threshold, the
system can infer road congestions and provide traffic signal
adjusting suggestion to government agencies.
 Driving behavior and road condition estimation.
Our system calculates and records vehicle acceleration. With
combined information about vehicle travel direction, we can
infer driver’s behavior and suggests better commuting safety.
In addition, the bumps and potholes can be detected by the
smartphone sensors and thus proper road maintenance advice
can be obtained.
Results of Matlab Simulation
The number of times encountering a green phase or a
red phase is compared.
Results of Matlab Simulation
The time spent waiting for green phase is compared.
Results of Matlab Simulation
The distance traveled per vehicle is compared.
Results of Real-world Experiments
Comparison of speed-acceleration histogram with and
without CityDrive
The End
Results show that with our speedadvisory service throughout the travel,
58:8%of kinetic energy can be saved!
Imagine a world with such intelligent
transportation system!
Thank you!