User Tools

Site Tools


Action unknown: copypageplugin__copy
projects:year6:6a.020.ul

6a.020.UL - Data Integrity of Smart City Traffic Infrastructure

Project - Summary

In Smart City settings, various sensors are used to collect traffic data (e.g., cameras, piezoelectric, acoustic, and magnetic sensors). Data obtained from these sensors are used to support various types of services including traveler information, ramp metering, incident detection, travel time prediction, and vehicle classification. The accuracy and integrity of collected data are crucial to the reliability of such services. Recent research has shown that hackers can compromise the sensors and send misleading data to the controller, potentially causing significant traffic problems and compromising the entire operation of the smart city services. This project proposes a robust anomaly detection algorithm on sensor traffic data. The algorithm runs in three complementary phases: temporal detection, spatial detection and GPS calibration.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Khalid Elgazzar PI Not available Not available UL Lafayette
Vijay Raghavan Co-PI raghavan@louisiana.edu (337) 280-8451 UL Lafayette
Taghreed Alghamdi Student txa8426@louisiana.edu Not available UL Lafayette
Sumit Shah Project Mentor Sumit.Shah@cgifederal.com (202) 309-8790 CGI

Project - Deliverables

Deliverables
1 A temporal prediction model for real-time traffic data
2 A spatial prediction model for real-time traffic data
3 An integrated ARIMA-based algorithm for traffic data integrity
4 A technique to verify traffic sensor measurements with GPS probe data in real-time

Project - Presentation Video

Project - Documents

projects/year6/6a.020.ul.txt · Last modified: 2021/06/02 16:34 by sally.johnson