Context-aware multi-modal traffic management in ITS: A Q-learning based algorithm

Abstract

Multi-modal traffic management in IntelligentTransportation Systems (ITS) aims to provide a more efficient traffic regulation to passengers and reduce congestion and obstruction in the roads. In spite of the outstanding progress made in this research filed; traffic management still a very challenging problem regarding the multiple factors that have to be taken into account in any proposed solution. To tackle this problem, this paper introduces a collaborative model based context awareness multi-modal traffic management aiming at providing an efficient way to manage the traffic inside a transportation station. In this model (Multi-Layers Stations: the stations that have different intersections for different means of transport), the traffic management is based on a Q-learning technique that takes into account the context awareness parameters to provide more potent decisions. The learning technique offers the opportunity to the system (transportation station) to adapt dynamically its decision (choice of the best transportation mean) based on feedbacks provided by the passengers traveling from that specified station and thus optimize their journey through the transportation network. The efficacy of our proposed technique is validated through extensive simulations for different layers of transport means like metros, trains, and buses. Our proposal holds for any ITS system decisions provided the availability of real-time traces about the passengers passing by any station.

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

“…Additionally, addressing user preferences, considering interoperability, and implementing adaptive algorithms that can accommodate the dynamic property of multi-modal transportation are essential features of this research avenue. Attaining a smoothly integrated multi-modal transportation system not only promotes a more sustainable and accessible transportation network but also demands groundbreaking approaches at the intersection of DL and transportation design [261].…”

Q-Learning versus SVM Study for Green Context-Aware Multimodal ITS Stations

“…Through the work, we improved the previous implementations about advanced ITS traffic flows management [2]. The aim of the previous paper was to choose among different transportation means the best one.…”

“…The selection is carried out by classifying some collected ITS traces about all the possible routes including combinations between different means of transportation at any time (t). Moreover, we considered the influence of new parameters that are missed in the previous work [2] like:…”