IOT-Driven accident detection and notification system with smart speed cameras for traffic signal optimization in vehicular environments

Abstract

In our rapidly urbanizing world, the movement of goods, people, and services is greatly facilitated by transportation infrastructure. Each year, lost resources, higher fuel consumption, and increased pollution result in billion-dollar costs associated with traffic congestion, accidents on the roads, and inefficiencies in the transportation systems. The road network is under pressure due to the increase in the urban population, and innovative solutions are needed to increase road safety. To improve road safety but also to increase the smooth flow of traffic, automatic accident detection systems as well as the traffic light optimization system must be implemented. Both systems use the latest technology, artificial intelligence (AI), machine learning, as well as the Internet of Things (IoT). The automatic accident detection system focuses on real-time accident detection to inform emergency services as quickly as possible to minimize injuries and deaths, while the traffic light optimization system focuses on improving traffic flow, so that there is unimpeded passage of emergency vehicles, which are directed to the incident location. This technology not only saves lives but also helps the overall performance of urban and intercity transportation systems. This thesis studies how these two systems can be implemented in the future to increase road safety and reduce emergency response times, as so far, the times are quite high. In this way, this thesis offers several contributions to the field of traffic management as well as the management of a traffic accident. The research presented in this thesis tries to highlight the problem using real evidence from the Aegean Motorway and focuses on creating a system that will try to close various gaps from previous attempts to create a similar system. The research highlights the importance of integrating emerging technologies such as IoT, AI, and ML into traffic control systems in a bid to mitigate growing issues such as traffic congestion, accidents, and ineffective transit. Automatic Accident Detection System (AADS) utilizes sensors, cameras, and intelligent algorithms to detect accidents in real time and alert rescue teams in a timely fashion. By making accurate accident data available to first responders, this rapid response can also minimize the severity of injuries and fatalities. The Traffic Light Optimization System (TLOS), on the other hand, optimizes the timing of traffic lights according to traffic flow to minimize congestion and maximize the overall efficiency of the city's transportation networks. Although AADS and TLOS can and do provide many advantages, there are some challenges associated with their adoption, as highlighted by the study. High cost, privacy and data concerns, and the need for robust communication infrastructure are significant drawbacks that must be overcome. Public acceptance is also a key factor in the implementation of these technologies. While people know the possible benefits of AADS and TLOS in reducing traffic congestion and improving road safety, they also have concerns regarding the efficiency, cost, and privacy of the systems. Public education and resolution of these concerns are very important steps towards gaining more acceptance of the technologies. The study also highlights the environmental benefits of TLOS as it can be utilized for the reduction of car emissions and fuel usage as well as traffic flow optimization. The study includes an analysis at the Agia interchange, which illustrates how the application of TLOS can reduce travel time and waiting time in traffic. For AADS and TLOS to be effective, several stakeholders, including city or state citizens, policymakers, and industry professionals, must be involved in the implementation of the systems to address the challenges and build trust. By continuously improving such systems, it is possible to move towards more sustainable, efficient, and safe transportation solutions. First, in this specific research, a literature review was conducted so that we could understand exactly how these systems work, as well as find implementations of systems around the world. It was also very important to highlight the pros and cons of these systems. Then, a microscopic analysis was carried out through the VISSIM, in a specific area of the highway, to determine the problems that may be created by the existence of traffic lights, such as e.g. the increased travel times as well as the increased queues that can also lead to traffic accidents. This specific analysis could not be missing from a survey that aimed to understand how familiar drivers are with these systems, as well as their perception of them. Finaly, to complete this specific research, we tried to create an application as well as a system that would have the ability to inform all parties involved in the event of an accident and would have the ability to provide information that was previously unknown and was a deterrent in dealing with traffic accidents. Regarding the results, this specific research came to fill the gaps that had been found during the literature review. The existence of an application that contains all the necessary information for the most appropriate response to a traffic accident is now a fact. This system can in the future become a "car black box" which, with the appropriate connection to the car, can even provide information about the weather.