1. PRIVACY AWARE
TRAJECTORY DETERMINATION
IN ROAD TRAFFIC NETWORKS
Preeti Goel, Lars Kulik,
Ramamohanarao Kotagiri
Department of Computing and Information
Systems
The University of Melbourne, Australia

2. TRAFFIC MANAGEMENT
Transportation systems collect huge amounts of vehicle data for
effective traffic management
Data Sources – Surveys, GPS, RFID, Possible
Video Cameras, Mobile Phone Logs, trajectories
Smart Cars, Loop detectors
Trade-off – Privacy and Accuracy
Full trajectory information –
High accuracy but no privacy
for vehicle drivers
Partial (incomplete)
trajectory information

3. OUR APPROACH
Record partial (incomplete) vehicle data locally for trajectory
determination
Local re-identification of vehicles to build local transition matrices
Goal
 Traffic and transportation forecasting: estimation of the number of vehicles
using a road network on a particular day/time
 Congestion control: route planning in case of a congested or broken link on
the network
 Estimation of traffic flows: applications such as the positioning of red-light
cameras

4. LOCAL TRANSITION MATRIX
(D1, Red Car) (D2,1101)
D3
MD2 = D1 n+1