Abstract
An assessment of road crash statistics of Nagpur city revealed that the annual number of road crashes ranges between 1000 and 1100, with heavy vehicles (buses) accounting for 8–14% of the total. Many of these road crashes are caused by large vehicles like buses and trucks, creating several blind spots for the drivers. In this study, an Artificial Intelligence (AI) powered Collision Alert System (CAS) was deployed. The study revealed that CAS offers the driver many visual and audio alerts when the vehicle encounters various obstacles while driving on the road. Further, Vienna Test was conducted to understand ground decisions by the drivers, i.e., referred to as “subjects” henceforth regarding these alerts, and a detailed questionnaire survey (DQS) was also performed to collect the basic attributes of these subjects. Using these data, namely, CAS alerts, Vienna results, and driver attributes of 33 drivers, correlation analysis was performed, and a linear regression model was developed to establish the Road Safety Index (RSI) based on their driving experience, eye condition, and generated alerts. This index can be used as a significant indicator for the evaluation of drivers, potentially leading to a reduction in road crashes involving buses and assisting in the allocation of duties to bus drivers on various bus routes.
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References
Accident research cell (2018, 2019, 2020, 2021) Road accidents in Maharashtra. Mumbai: Accident research cell
Aksjonov A, Nedoma P, Vodovozov V, Petlenkov E, Herrmann M (2019) Detection and evaluation of driver distraction using machine learning and fuzzy logic. IEEE Trans Intell Transp Syst 20(6): 2048–2059. doi:https://doi.org/10.1109/TITS.2018.2857222.
Alkinani MH, Khan WZ, IEEE S, Arsad Q (2020) Detecting human driver inattentive and aggressive driving behavior using deep learning: Recent advances, requirements and open challenges. IEEE Access 8(8): 105008–105030. doi:https://doi.org/10.1109/ACCESS.2020.2999829
Azman NS, Hashmin HH, Shabadin A, Abd ghani MR, Abu kassim KA, Mohammad radzi M (2021) A study of driver behavioral adaptation to advanced driver assistance system (adas) in Malaysia. J Adv Veh Syst 11(1): 1–12. Retrieved from http://www.akademiabaru.com/submit/
Bhalerao J, Kadam A, Shinde A, Mugalikar V, Bhan H (2020) Proposed design on driver behavioral analysis. Int J Eng Res & Technol (IJERT) 9(5): 1–5. Retrieved from http://www.ijert.org
Farooq D, Moslem S, Tufail RF, Ghorbanzadeh O, Duleba S, Maqsoom A, Blaschke T (2020) Analyzing the importance of driver behavior criteria related to road safety for different driving criteria. Int J Environ Res Public Health 17(6): 1893. doi :https://doi.org/10.3390/ijerph17061893
IBM Corp. (n.d.) IBM SPSS statistics for windows, Version 22.0. (IBM Corp.) Retrieved June 17, 2013, from https://www.ibm.com/support/
Mishra A, Bajaj P (2015) Driver behaviour monitoring on urban roads of a tier 2 city in India. 7th Int Conf Emerg Trends Eng & Technol 15: 134–140. doi:https://doi.org/10.1109/ICETET.2015.13
Mobileye-8-connect (2021) Products. Retrieved from Mobileye: https://www.mobileye.com/us/fleets/products/mobileye-8-connect/
Nagpur metropolitan region development authority (2017) NMRDA. About us. Retrieved from nmrda: http://www.nmrda.org/aboutNMR.aspx
Schuhfried G (2011) Wiener test system (Vienna Test System) modelling. Schuhfried, Austria
Urban mass transit company limited (2018) Comprehensive mobility plan for Nagpur. Nagpur Improvement Trust, Nagpur
Voinea GD, Postelnicu CC, Duguleana M, Mogan GL, Socianu R (2020) Driver performance and technology acceptance evaluation in a real traffic of smartphone based driver assistance system. Int J Environ Res Public Health 17. doi: https://doi.org/10.3390/ijerph17197098
Ye W, Xu Y, Zhou F, Shi X, Ye Z (2021) Investigation of bus drivers’ reaction to ADAS warning system: application of the Gaussian mixed model. MDPI, Sustainability 13(8759): 13–16. doi: https://doi.org/10.3390/su13168759
Acknowledgements
This study was carried out as part of Project iRASTE (Intelligent Solutions for Road Safety through Technology and Engineering) by the consortium of CSIR-CRRI, Intel India, Mahindra & Mahindra Group, INAI Centre & iHUB-data at IIIT-Hyderabad, and Nagpur Municipal Corporation. Authors are thankful to the data collection team consisting of Ms. Kamini Gupta, Mr. Mohammad Akil, Mr. Abhi Mandal, and Mr. Sikandar.
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Thakur, D.S., Advani, M., Velmurugan, S., Subramanian, A., Chakrabarty, N., Goel, A. (2024). Artificial Intelligence (AI)-Based Assessment of Behavior of Bus Drivers in Nagpur City (India): A Case Study. In: Dhamaniya, A., Chand, S., Ghosh, I. (eds) Recent Advances in Traffic Engineering. RATE 2022. Lecture Notes in Civil Engineering, vol 377. Springer, Singapore. https://doi.org/10.1007/978-981-99-4464-4_6
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