Advancing Bike Sharing Security with SVM and IoT-Enabled Tracking and Anti-Theft Measures

Bike-sharing systems have become increasingly common in recent years, providing practical mobility for residents, visitors, and commuters. These systems face a major threat to their longevity and safety because of the common occurrence of bike theft. The use of Support Vector Machine (SVM) algorithms and anti-theft features offered by the Internet of Things (IoT) presents a new method to improve the safety of bike-sharing systems in this research. It first examines data on bike thefts that have already occurred to find weaknesses and trends in current bike-sharing systems. Our prediction model considers variables like location and time of day and uses habits to forecast and reduce the potential of theft events. SVM is utilized for this purpose. It equips bikes with tracking devices that can be accessed via the IoT and can monitor them in real time. Conducting several simulations and field testing shows that our strategy can decrease bike theft rates and improve system security in general. To protect bike-sharing infrastructure against theft and abuse, our results highlight the significance of preventative measures guided by data-driven insights and smart technology. A huge step forward in bike-sharing security and provides real solutions to make urban transportation networks more dependable and secure. Implementing effective real-time monitoring, optimizing SVM parameters, providing low-latency connections for the IoT, and preparing data for analysis are all challenges.