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IoT-Based Emergency Vehicle Services in Intelligent Transportation System

journal contribution
posted on 2024-11-03, 09:28 authored by Abdullahi Chowdhury, Shahriar KaisarShahriar Kaisar, Mahbub Khoda, Ranesh Naha, Mohammad Khoshkholghi, Mahdi Aiash
Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs’ travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.

History

Related Materials

  1. 1.
    DOI - Is published in 10.3390/s23115324
  2. 2.
    ISSN - Is published in 14248220

Journal

Sensors

Volume

23

Number

5324

Issue

11

Start page

1

End page

17

Total pages

17

Publisher

MDPI AG

Place published

Switzerland

Language

English

Copyright

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Former Identifier

2006123401

Esploro creation date

2023-07-08

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