A Passengers Safety Assistance System during a Transport Riding Event Using Machine Learning
Автор: Uchhas Dewan, Mahfuzulhoq Chowdhury
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 3 vol.16, 2024 года.
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The rise in popularity of ride-sharing services and ride-booking systems has created new opportunities and challenges for security and safety. A useful system for passenger safety assistance using machine learning and mobile applications is missing from the existing work. This paper develops a data set regarding suspicious activity detection using a questionnaire. This paper selects a suitable machine learning model for suspicious activity prediction during a transport ride by examining support vector classifiers (SVC), random forest, MLP classifiers, decision trees, KNN, logistic regression, and Gaussian naive Bayes classifiers. The results showed that the SVC is most suitable, with 97% accuracy, for classifying suspicious activity predictions during transport riding. This paper provides a passenger safety mobile application with passenger and driver verification, application rating, suspicious activity prediction, suggestions regarding safety, location mapping, and trip booking features. The application evaluation results based on users’ comments showed that more than 55 percent of users supported the application's usability and effectiveness nature.
Ride-sharing services, ride booking, machine learning, labeled dataset, Smartphone applications, suspicious activity prediction, and passenger safety
Короткий адрес: https://sciup.org/15019416
IDR: 15019416 | DOI: 10.5815/ijieeb.2024.03.03
Список литературы A Passengers Safety Assistance System during a Transport Riding Event Using Machine Learning
- I. T. Forum, “Road safety annual report 2022,” https://www.itf-oecd.org/road-safety-annual-report2022, last accessed on July, 2023.
- R. Chun, “Rough ride-share: Why drivers are also at risk of violence, ” https://www.theguardian.com/usnews/2020/feb/06/uber-rideshare-lyft-safety-crime, last accessed on december, 2020.
- S. Tasnim Cynthia et al., “Security concerns of ridesharing services in bangladesh, ” in 2019 2nd International Conference on Applied Information Technology and Innovation (ICAITI), Denpasar, Indonesia, pp. 44-50, 2019.
- I. Cojocaru and P.-S. Popescu, Building a driving behaviour dataset, in RoCHI, 2022, pp. 101–107.
- M. E. Hossain et al., “Manifesting a mobile application on safety which ascertains women salus in bangladesh, ”International Journal of Electrical and Computer Engineering, vol. 9, no. 5, pp. 4355-4363, 2019.
- H. Aghayari et al., “Mobile applications for road traffic health and safety in the mirror of the haddon’s matrix, ” BMC medical informatics and decision making, vol. 21, no. 1, pp. 1–12, 2021.
- T. Wang et al., “Walksafe: A pedestrian safety app for mobile phone users who walk and talk while crossing roads, ” in Proceedings of the twelfth workshop on mobile computing systems and applications, 2012, pp. 1–6.
- J. Trager et al., “Warning apps for road safety: A technological and economical perspective for autonomous driving– the warning task in the transition from human driver to automated driving,”International Journal of Human–Computer Interaction, vol. 37, no. 4, pp. 363–377, 2021.
- S. Group, “Sitata travel safe(3.5.3), ”https://www.sitata.app/en/, last accessed on July 2023.
- I. Hitch Technologies, “Hitch — City-toCity Rideshare App for Long-distance Rides, ” https://www.ridehitch.com/, last accessed on July 2023.
- Bridj company, “BRIDJ – Demand Responsive Transport Solutions, ” https://www.bridj.com/, last accessed on July 2023.
- J.-L. Martin et al., “ Cannabis, alcohol and fatal road accidents, ” PLoS one, vol. 12, no. 11, pp. 1-10, 2017.
- R. Khan et al., “Security-aware passwords and services usage in developing countries: A case study of bangladesh, ” in International Conference on Services Computing, Springer, pp. 67–84, 2018.
- P. Wawage et al., “Smartphone sensor dataset for driver behavior analysis, ” Data in Brief, vol. 41, no. xx, pp. 1-10, 2022.
- A. Carballo et al., “Libre: The multiple 3d lidar dataset, ” 2020 IEEE Intelligent Vehicles Symposium (IV), las vegas, USA, pp. 1094-1101, 2020.
- E. Romera et al., “Need data for driver behaviour analysis? presenting the public uah-driveset, ” in 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), Rio de Janeiro, Brazil, 2016, pp. 387-392
- H. Wang et al., “A recognition method of aggressive driving behavior based on ensemble learning, ” Sensors, vol. 22, no. 2, pp. 1-24, 2022.
- A. B. Ellison et al., “Driver behaviour profiles for road safety analysis, ” Accident Analysis and Prevention, vol. 76, pp. 118-132, 2015.
- V. Ramanishka et al., “Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning, ” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7699–7707, 2018.
- U. of Michigan, “Division of Public Safety and Security, Report a crime or concern suspicious behavior, ”http://dpss.umich.edu/content/services/report-acrime/suspicious-behavior/, pp. 1-10, 2023.
- SecuriGroup, “Understanding the challenges of cctv monitoring, ” https://www.securigroup.co.uk/news/understandingthe-challenges-of-cctv-monitoring, last accessed on july 2023.
- Tennessee govt., “Tennessee Suspicious Activity Reporting, ” https://www.tn.gov/safety/homelandsecurity/report-suspicious-activity.html, last accessed on july 2023.
- N. Tilley and G. Farrell, “The crime drop and the security hypothesis, ” Journal of Crime Science, vol. 4, no. 1, pp. 1–17, 2015.
- L. Goel et al., “Transfer Learning-based Driver Distraction Detection,” ICSCDS conference, India, 2023, pp. 58-63.
- M. Almoqbel and E. Alyami, “Am I Safe? Understanding Saudi Women Rideshare Drivers Safety Perspectives,” 3rd ICCIT, Saudi Arabia, 2023, pp. 618-621.
- S. S. Jacob et al., “IoT based Driver Assist System with Application of OpenCV and Python,” 3rd ICESC, India, 2022, pp. 481-488.
- S. Shilaskar et al., "Driver Safety System using Microcontroller and Image Processing," 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2023, pp. 340-344.
- M. V. P. R. Meneses et al., "Driver Drowsiness Classification using Machine Learning and Heart Rate Variability," 2023 11th International Symposium on Digital Forensics and Security (ISDFS), USA, 2023, pp. 1-6.
- M. Guria et al., "IoT-Enabled Driver Drowsiness Detection Using Machine Learning," Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), India, 2022, pp. 519-524.
- S. Rathod et al., "RealD3: A Real-time Driver Drowsiness Detection Scheme Using Machine Learning," IEEE Wireless Antenna and Microwave Symposium (WAMS), India, 2023, pp. 1-5.
