Smart traffic monitoring system using Unmanned Aerial Vehicles (UAVs)

Abstract Road traffic accidents are one of the leading causes of deaths and injuries in the word resulting in the not only loss of precious human lives but also affect the economic resources. According to the World Health Organization (WHO), over 1.35 million people are killed, and over 50 million are injured due to road accidents throughout the world. Unfortunately, as compared to other developing countries with the same ratio of vehicle possession, in Saudi Arabia, the fatalities and injuries are much higher. Every year around 7000–9000 people die, and over 39000 serious injuries occur in road accidents. There is at least one accident happens every minute in Saudi Arabia. To decrease the road traffic accidents, fatalities, and injuries caused by them, the Saudi Ministry of Interior came up with new rules, regulations, and hefty fines. Also, they introduced a new traffic system called the SAHER system. Still, due to the static nature and other limitations of the system, the drivers found loopholes and ways to deceive the system to avoid the fines and not being caught by the system. The most common violation includes excess speed, abrupt deceleration, and distracted driving. In this paper, we propose a smart traffic surveillance system based on Unmanned Aerial Vehicle (UAV) using 5G technology. This traffic monitoring system covers the existing limitations of the SAHER system deployed in KSA. By overcoming the existing limitations and loopholes of the SAHER system, it is observed that the number of accidents and fatalities can be decreased. The projected results show that those violations when to overcome, the number of accidents per year falls to 299,317 leading to 4,868 deaths and 33,199 injuries for 1 st year, and in the next five years the number of deaths and will be decreased to 3,745 and injuries to 16,600 based on the current data available. We aim the system will further reduce the number of accidents and fatalities and injuries caused by it.

[1]  Luis F Miranda-Moreno,et al.  Quantifying safety benefit of winter road maintenance: accident frequency modeling. , 2010, Accident; analysis and prevention.

[2]  J. Iqbal,et al.  INJURY PATTERNS FROM ROAD TRAFFIC ACCIDENTS , 2010 .

[3]  Fadi Al-Turjman.  A novel approach for drones positioning in mission critical applications , 2019 .

[4]  Morten Stigaard Laursen,et al.  Designing and Testing a UAV Mapping System for Agricultural Field Surveying , 2017, Sensors.

[5]  F. Marchese,et al.  UAV-based surveying in volcano-tectonics: An example from the Iceland rift , 2019, Journal of Structural Geology.

[6]  Fadi Al-Turjman,et al.  5G/IoT-enabled UAVs for multimedia delivery in industry-oriented applications , 2018, Multimedia Tools and Applications.

[7]  S. Ansari,et al.  Causes and effects of road traffic accidents in Saudi Arabia. , 2000, Public health.

[8]  Rajesh Gupta,et al.  Blockchain envisioned UAV networks: Challenges, solutions, and comparisons , 2020, Comput. Commun..

[9]  Ilsun You,et al.  Internet of Drones (IoD): Threats, Vulnerability, and Security Perspectives , 2018, Research Briefs on Information and Communication Technology Evolution.

[10]  Mohammed A.H. Dahim,et al.  Impact of vision 2030 on traffic safety in Saudi Arabia , 2018, International journal of pediatrics & adolescent medicine.

[11]  Hadas Marciano,et al.  Overt vs. covert speed cameras in combination with delayed vs. immediate feedback to the offender. , 2015, Accident; analysis and prevention.

[12]  M. Al-Naami,et al.  Trauma care systems in Saudi Arabia: an agenda for action , 2010, Annals of Saudi medicine.

[13]  Eleni I. Vlahogianni,et al.  How accurate are small drones for measuring microscopic traffic parameters? , 2019 .

[14]  Ian F. Akyildiz,et al.  Help from the Sky: Leveraging UAVs for Disaster Management , 2017, IEEE Pervasive Computing.

[15]  Fadi Al-Turjman,et al.  Applications of Artificial Intelligence and Machine learning in smart cities , 2020, Comput. Commun..

[16]  Neeraj Kumar,et al.  Tactile Internet for Autonomous Vehicles: Latency and Reliability Analysis , 2019, IEEE Wireless Communications.

[17]  Hanno Hildmann,et al.  Review: Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety , 2019, Drones.

[18]  Yousef Abdullah Al Turki,et al.  How can Saudi Arabia use the Decade of Action for Road Safety to catalyse road traffic injury prevention policy and interventions , 2014 .

[19]  Mark Stevenson,et al.  Road traffic injuries , 2008 .

[20]  Nasreddine Lagraa,et al.  GeoUAVs: A new geocast routing protocol for fleet of UAVs , 2020, Comput. Commun..

[21]  Ahmad M. Abu Abdo,et al.  Assessment of traffic safety and awareness among youth in Al-Ahsa Region, Saudi Arabia , 2011 .

[22]  S. Abu-Eshy,et al.  Analysis of 303 Road Traffic Accident Victims Seen Dead on Arrival at Emergency Room-Assir Central Hospital , 1996, Journal of family & community medicine.

[23]  Kok-Lim Alvin Yau,et al.  5G-Based Smart Healthcare Network: Architecture, Taxonomy, Challenges and Future Research Directions , 2019, IEEE Access.

[24]  Mihui Kim,et al.  Development of Cloud-Based UAV Monitoring and Management System , 2016, Sensors.

[25]  F. Midhet,et al.  Epidemiology of road traffic injuries in qassim region, saudi arabia: consistency of police and health data. , 2012, International journal of health sciences.

[26]  F. Mansuri,et al.  Road safety and road traffic accidents in Saudi Arabia , 2015, Saudi medical journal.

[27]  Emmanouil N. Barmpounakis,et al.  On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment , 2020 .

[28]  Neeraj Kumar,et al.  Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges , 2020, Comput. Commun..

[29]  Suhasini Ramisetty-Mikler,et al.  Attitudes and behaviors towards risky driving among adolescents in Saudi Arabia , 2016, International journal of pediatrics & adolescent medicine.

[30]  Tomislav Dragicevic,et al.  Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review , 2019, Energies.

[31]  Jennifer T. Wong,et al.  Investigating Driving Styles and Their Connections to Speeding and Accident Experience , 2010 .

[32]  M. Touahmia,et al.  Identification of Risk Factors Influencing Road Traffic Accidents , 2018 .

[33]  Anand Nayyar,et al.  Emerging use of UAV’s: secure communication protocol issues and challenges , 2020 .

[34]  Marthinus J. Booysen,et al.  Drone-based traffic flow estimation and tracking using computer vision , 2015 .

[35]  Davy Janssens,et al.  Unmanned Aerial Vehicle-Based Traffic Analysis: A Case Study for Shockwave Identification and Flow Parameters Estimation at Signalized Intersections , 2018, Remote. Sens..

[36]  Antonio Barrientos,et al.  Monitoring traffic in future cities with aerial swarms: Developing and optimizing a behavior-based surveillance algorithm , 2019, Cognitive Systems Research.

[37]  Fadi Al-Turjman,et al.  Optimized Unmanned Aerial Vehicles Deployment for Static and Mobile Targets' Monitoring , 2020, Comput. Commun..

[38]  Juha Plosila,et al.  Swarms of Unmanned Aerial Vehicles - A Survey , 2019, J. Ind. Inf. Integr..

[39]  Hyunbum Kim,et al.  Designing UAV Surveillance Frameworks for Smart City and Extensive Ocean with Differential Perspectives , 2018, IEEE Communications Magazine.