Smart GPS Based Vehicle Speed Limit Controller on zone identification using Geo-Fencing Algorithm

In light of growing concerns about road safety and the surge in traffic accidents, this project aspires to an Internet of Things smart GPS-based vehicle speed limit controller system. The primary objective is to elevate road safety by harnessing real-time data and automated control mechanisms to enforce speed limits, thereby diminishing incidents of speeding-related accidents. This project adopts a comprehensive approach, integrating both hardware and software components. Vehicle-mounted sensors, encompassing speed sensors and GPS receivers, continually gather real-time data on vehicle speed and location. Furthermore, road condition data is sourced from multiple outlets, such as weather stations and traffic cameras. This data is wirelessly transmitted to a central control unit, which processes and analyzes it. The control unit deploys machine learning algorithms to evaluate vehicle speed limits against predefined speed limits for specific road segments. Depending on this analysis, it transmits control signals back to the vehicle when necessary. These signals can encompass driver warnings, power limitations, or autonomous speed control. These methodologies collectively enabled the creation of an intelligent and adaptable GPS-based vehicle speed control system, offering the potential to significantly enhance road safety by dynamically enhancing road safety regulating vehicle speeds in different zones, ensuring the safety of people and drivers alike. The Smart GPS-based vehicle speed limit controller system has undergone successful development and testing, both in controlled environments and on public roads. The outcomes manifest a remarkable reduction in incidents of speeding and an enhanced adherence to speed limits, exhibiting an accuracy level exceeding 96%. In real-world scenarios, the system effectively mitigates speeding-related risks, leading to a substantial decline in traffic accidents. Its ability to adapt to changing road conditions and. Its scalability renders it a promising solution for future smart transportation systems.