Modelling and Reasoning Techniques for Context Aware Computing in Intelligent Transportation System

TL;DR

This paper reviews how modeling and reasoning techniques, combined with IoT and machine learning, enhance context awareness in Intelligent Transportation Systems, improving traffic management and safety.

Contribution

It provides a comprehensive review of modeling and reasoning approaches for context awareness in ITS, highlighting the role of IoT and machine learning.

Findings

Increased data from IoT enhances context understanding in ITS.

Modeling techniques improve traffic management and safety.

Machine learning enables predictive analytics in transportation.

Abstract

The emergence of Internet of Things technology and recent advancement in sensor networks enabled transportation systems to a new dimension called Intelligent Transportation System. Due to increased usage of vehicles and communication among entities in road traffic scenarios, the amount of raw data generation in Intelligent Transportation System is huge. This raw data are to be processed to infer contextual information and provide new services related to different modes of road transport such as traffic signal management, accident prediction, object detection etc. To understand the importance of context, this article aims to study context awareness in the Intelligent Transportation System. We present a review on prominent applications developed in the literature concerning context awareness in the intelligent transportation system. The objective of this research paper is to highlight context and its features in ITS and to address the applicability of modelling techniques and reasoning approaches in Intelligent Transportation System. Also to shed light on impact of Internet of Things and machine learning in Intelligent Transportation System development.