Accident Response Time Enhancement Using Drones: A Case Study in Najm for Insurance Services

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Author(s)

Salma M. Elhag 1,* Ghadi H. Shaheen 1 Fatmah H. Alahmadi 1

1. King Abdulaziz University/Faculty of Computing and Information Technology, Jeddah, 21589, Saudi Arabia

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2023.06.01

Received: 22 Jul. 2023 / Revised: 18 Sep. 2023 / Accepted: 21 Oct. 2023 / Published: 8 Dec. 2023

Index Terms

Accident Response, Road Traffic, Turnaround Time, Process Automation, Drones, Bizagi

Abstract

One of the main reasons for mortality among people is traffic accidents. The percentage of traffic accidents in the world has increased to become the third in the expected causes of death in 2020. In Saudi Arabia, there are more than 460,000 car accidents every year. The number of car accidents in Saudi Arabia is rising, especially during busy periods such as Ramadan and the Hajj season. The Saudi Arabia’s government is making the required efforts to lower the nations of car accident rate. This paper suggests a business process improvement for car accident reports handled by Najm in accordance with the Saudi Vision 2030. According to drone success in many fields (e.g., entertainment, monitoring, and photography), the paper proposes using drones to respond to accident reports, which will help to expedite the process and minimize turnaround time. In addition, the drone provides quick accident response and recording scenes with accurate results. The Business Process Management (BPM) methodology is followed in this proposal. The model was validated by comparing before and after simulation results which shows a significant impact on performance about 40% regarding turnaround time. Therefore, using drones can enhance the process of accident response with Najm in Saudi Arabia.

Cite This Paper

Salma M. Elhag, Ghadi H. Shaheen, Fatmah H. Alahmadi, "Accident Response Time Enhancement Using Drones: A Case Study in Najm for Insurance Services", International Journal of Information Technology and Computer Science(IJITCS), Vol.15, No.6, pp.1-14, 2023. DOI:10.5815/ijitcs.2023.06.01

Reference

[1]Ali Ahmed Mohammed, Kamarudin Ambak, Ahmed Mancy Mosa, and Deprizon Syamsunur. A review of traffic accidents and related practices worldwide. The Open Transportation Journal, 13(1), 2019.
[2]Li-Lu Sun, Dan Liu, Tian Chen, and Meng-Ting He. Road traffic safety: An analysis of the cross-effects of economic, road and population factors. Chinese journal of traumatology, 22(05):290–295, 2019.
[3]macrotrends. Saudi arabia population growth rate 1950-2022. macrotrends.net, 2022.
[4]Heidi Worley. Road traffic accidents increase dramatically worldwide. prb.org, 2006.
[5]PETE ORTIZ. 10 saudi arabia car accident statistics and facts. housegrail.com, 2022.
[6]Daeil Jo Lee, Sedam and Y. Kwon. Camera-based automatic landing of drones using artificial intelligence image recognition. International Journal of Mechanical Engineering and Robotics Research, 2022.
[7]Wojciech Wr´oblewski Tu´snio, Norbert. The efficiency of drones usage for safety and rescue operations in an open area: A case from poland. Sustainability, 2022.
[8]Mohammadjavad Khosravi, Rushiv Arora, Saeede Enayati, and Hossein Pishro-Nik. A search and detection autonomous drone system: from design to implementation. arXiv preprint arXiv:2211.15866, 2022.
[9]Stuart Hawkins. Using a drone and photogrammetry software to create orthomosaic images and 3d models of aircraft accident sites. In ISASI 2016 Seminar, pages 17–20, 2016.
[10]Anders Schmidt Kristensen, Dewan Ahsan, Saqib Mehmood, and Shakeel Ahmed. Rescue emergency drone for fast response to medical emergencies due to traffic accidents. International Journal of Health and Medical Engineering, 11(11):637–641, 2017.
[11]Anton Saveliev, Valeriia Lebedeva, Igor Lebedev, and Mikhail Uzdiaev. An approach to the automatic construction of a road accident scheme using uav and deep learning methods. Sensors, 22(13):4728, 2022.
[12]Abdurrahman Beg, Abdul Rahman Qureshi, Tarek Sheltami, and Ansar Yasar. Uav-enabled intelligent traffic policing and emergency response handling system for the smart city. Personal and Ubiquitous Computing, 25(1):33–50, 2021.
[13]Albin Libi Madana and Vinod Kumar Shukla. Conformity of accident detection using drones and vibration sensor. pages 192–197, 2020. doi:10.1109/ICRITO48877.2020.9197783.
[14]Joel Lemayian and J. M. Hamamreh. Autonomous first response drone-based smart rescue system for critical situation management in future wireless networks. RS Open Journal on Innovative Communication Technologies, 05 2020. doi:10.46470/03d8ffbd.b0ec5747.
[15]Sheldon Cheskes, Shelley L McLeod, Michael Nolan, Paul Snobelen, Christian Vaillancourt, Steven C Brooks, Katie N Dainty, Timothy CY Chan, and Ian R Drennan. Improving access to automated external defibrillators in rural and remote settings: a drone delivery feasibility study. Journal of the American Heart Association, 9(14):e016687, 2020.
[16]Joel P Lemayian and Jehad M Hamamreh. First responder drones for critical situation management. In 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), pages 1–6. IEEE, 2019.
[17]Mouna Elloumi, Riadh Dhaou, Benoit Escrig, Hanen Idoudi, and Leila Azouz Saidane. Monitoring road traffic with a uav-based system. In 2018 IEEE Wireless Communications and Networking Conference (WCNC), pages 1–6. IEEE, 2018.
[18]Aaron Pulver and Ran Wei. Optimizing the spatial location of medical drones. Applied Geography, 90:9–16, 2018.
[19]GAE Satish Kumar, J Swetha Priyanka, and AS Deepthi. Vehicle accident and alcohol detection system using iot platform. Indian Journal of Science and Technology, 15(39):2004– 2010, 2022.
[20]M Karthikeyan, VS Manesh, Lalith S Krishna, B Vijay, R Vishwabharan, and E Prabhu. Iot based accident detection and response time optimization. pages 358–363, 2021. doi:10.1109/ICCMC51019.2021.9418272.
[21]Md Adilur Rahim and Hany M Hassan. A deep learning based traffic crash severity prediction framework. Accident Analysis & Prevention, 154:106090, 2021.
[22]Mahziar Mohammadrezaei, Hamed Shahbazi Fard, Reza Pourmohammadhosein Niaky, and Behnam Soltani Taqi Dizaj. Iot-based vehicular accident detection systems. 2020.
[23]Sahriar Habib, Zawata Afnan, Sakib Anam Chowdhury, Sarah Altaf Chowdhury, and Abu SM Mohsin. Design and development of iot based accident detection and emergency response system. pages 35–42, 2020.
[24]Rajvardhan Rishi, Sofiya Yede, Keshav Kunal, and Nutan V Bansode. Automatic messaging system for vehicle tracking and accident detection. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pages 831–834. IEEE, 2020.
[25]Mubashir Murshed and Md Sanaullah Chowdhury. An iot based car accident prevention and detection system with smart brake control. In Proc. Int. Conf. Appl. Techn. Inf. Sci.(iCATIS), volume 23, 2019.
[26]Shivani Sharma and Shoney Sebastian. Iot based car accident detection and notification algorithm for general road accidents. International Journal of Electrical & Computer Engineering (2088-8708), 9(5), 2019.
[27]Wan-Jung Chang, Liang-Bi Chen, and Ke-Yu Su. Deepcrash: a deep learning-based internet of vehicles system for head-on and single-vehicle accident detection with emergency notification. IEEE Access, 7:148163–148175, 2019.
[28]Mehtab Alam, Akshay Chamoli, and Nabeela Hasan. Smart cities and internet of drones. 2022.
[29]Marie Paul, Marie Paul Nisingizwe, Pacifique Ndishimye, Katare Swaibu, Ladislas Nshimiyimana, Valentine Dushimiyimana, Jean Pierre Musabyimana, Musanabaganwa Clarisse, Nsanzimana Sabin, and Michael Law. Effect of unmanned aerial vehicle (drone) delivery on blood product delivery time and wastage in rwanda: a retrospective, cross-sectional study and time series analysis. 10:e564–e569, 03 2022. doi:10.1016/S2214-109X(22)00048-1.
[30]Shuya Zong, Sikai Chen, Majed Alinizzi, and Samuel Labi. Leveraging uav capabilities for vehicle tracking and collision risk assessment at road intersections. Sustainability, 14(7):4034, 2022.
[31]Sofia Schierbeck, Jacob Hollenberg, Anette Nord, Leif Svensson, Per Nordberg, Mattias Ringh, Sune Forsberg, Peter Lundgren, Christer Axelsson, and Andreas Claesson. Automated external defibrillators delivered by drones to patients with suspected out-of-hospital cardiac arrest. European heart journal, 43, 08 2021. doi:10.1093/eurheartj/ehab498.
[32]M Adel Serhani, Tony T. Ng, Asma Al Falasi, Meera Al Saedi, Fatima Al Nuaimi, Hamda Al Shamsi, and Al Damani Al Shamsi. Drone-assisted inspection for automated accident damage estimation: A deep learning approach. pages 682–687, 2019. doi:10.1109/ICUFN.2019.8806100.
[33]Huaizhong Zhang, Mark Liptrott, Nik Bessis, and Jianquan Cheng. Real-time traffic analysis using deep learning techniques and uav based video. In 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1–5. IEEE, 2019.
[34]Juan Antonio P´erez, Gil Rito Gon¸calves, Jos´e Manuel Galv´an Rangel, and Pedro Fuentes Ortega. Accuracy and effectiveness of orthophotos obtained from low cost uass video imagery for traffic accident scenes documentation. Advances in Engineering Software, 132:47– 54, 2019.
[35] Calculator Academy Team. Improvement percentage calculator. calculator.academy