In this project we study the feasibility of allowing vehicles to crowd-source traffic information and distribute it in a decentralised manner.
Road congestion results in a huge waste of time and productivity for millions of people. A possible way to deal with this problem is to have transportation authorities distribute traffic information to drivers, which in turn can decide (or be aided by a navigator) to route around congested areas. Such traffic information can be gathered by relying on static sensors placed at specific road locations (e.g., induction loops, video cameras) or by having single vehicles report their location, speed and travel time.
While the former approach has been widely exploited, the latter has seen birth only more recently, and, consequently, its potential is less understood. For this reason, in this project we study a realistic test case that allows to evaluate the effectiveness of such a solution.
As part of this process: a) we designed a system that allows vehicles to crowd-source traffic information in an Ad-Hoc manner, allowing them to dynamically reroute based on individually collected traffic information, b) we implemented a realistic network-mobility simulator that allowed us to evaluate such a model, and c) the main focus of this project: we performed a case study that evaluates whether such a decentralized system can help drivers to minimize trip times. This study is based on traffic survey data from Portland, Oregon and our results indicate that such navigation systems can indeed greatly improve traffic flow.
Finally, to test the feasibility of our approach we implemented our system and run some real experiments at UCLA’s C-Vet test-bed.
Ilias Leontiadis, Gustavo Marfia, David Mack, Giovanni Pau, Cecilia Mascolo, Mario Gerla.
"On the Effectiveness of an Opportunistic Traffic Management System for Vehicular Networks "
In IEEE Transactions on Intelligent Transportation Systems. To Appear.