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 and 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 come about only more recently; consequently, its potential is less understood.
For this reason, in the paper published, a realistic test case has been studied that allows the evaluation of the effectiveness of such a solution. As part of this process,
(a) The authors 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) They implemented a realistic network-mobility simulator that allowed them to evaluate such a model; and
(c) They performed a case study that evaluates whether such a decentralized system can help drivers to minimize trip times, which is the main focus of this paper.
This study is based on traffic survey data from Portland, OR, and their results indicate that such navigation systems can indeed greatly improve traffic flow. Finally, to test the feasibility of their approach, they implemented their system and ran some real experiments at UCLA’s C-Vet test bed.