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

arXiv: https://arxiv.org/abs/2107.14374 · HTML: https://mlsanity.com/paper/2107.14374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks](https://mlsanity.com/paper/1803.11115/) (2018)
- [Context Aware Computing for The Internet of Things: A Survey](https://mlsanity.com/paper/1305.0982/) (2013)
- [Artificial Neural Networks Applied to Taxi Destination Prediction](https://mlsanity.com/paper/1508.00021/) (2015)
- [TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks](https://mlsanity.com/paper/1705.02636/) (2017)
- [Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining](https://mlsanity.com/paper/1811.04345/) (2018)

## Abstract (arXiv)

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.