A Crowd-Cooperative Approach for Intelligent Transportation Systems

As embedded and mobile systems grow pervasive in people lives and expand their reach, paradigms related to mobile crowdsensing (MCS) are going to play an ever more prominent role. Innovative methodologies and applications able to unlock such a huge potential are required. A domain where MCS really fits is mobility and transportation. However, for a full exploitation of this paradigm in intelligent transportation systems (ITS), distributed and self-management capabilities for the involved nodes-vehicles have to be provided. This paper is a first step in this direction, laying out an optimization system by exploiting feedback-driven patterns in a distributed-opportunistic way. In this sense, collective intelligence and stigmergic, swarm-based paradigms are adapted to an innovative decentralized MCS pattern toward new approaches in ITS. Their effectiveness is demonstrated through a traffic engineering case study, where route planning services are designed according to the proposed approach and then modeled and evaluated by Markovian agents.