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Traffic Accident Data Management System - The concept of a smart city is linked to a paradigm shift where interest is being directed towards proposals and technological initiatives, which seek to make cities smart so that the quality of the lives of the people living in these cities can be improved. For instance, in Europe, the European Commission has established the European Initiative on Smart Cities in 2010. This initiative seeks to address the four important elements in the life of a city: climate management systems, building, transport, and electricity.

One of the main elements that smart cities seek to contain is the issue of increasing levels of road accidents, which have resulted from increasing numbers of vehicles, leading to congestion. It has been proven that vehicle accidents are one of the main causes of fatalities with more than 100 deaths being reported in the United States on a daily basis . The same report indicates that the year 2007 experienced more than 43,000 deaths as a result of over 10 million reported accidents. Moreover, in most of the countries in the world, a major cause of accidents is poor infrastructure for managing traffic. The concept of traffic management systems refers to intelligent public transport mechanisms founded on information obtained in real-time, which helps drivers to avoid congested areas and increase safety while also ensuring that vehicles operate in a manner that is more environmentally friendly. Within the last few years, researchers, both from academic institutions and from industry have taken advantage of the progress made in wireless sensing technology to make the current traffic management systems more effective and ensure their ability to cope with the issues within the smart city discussed above.

Traffic congestion, as one of the leading problems affecting modern societies, has been given increasing attention. The delay in the arrival of emergency services at accident scenes like ambulances, the police, and fire, and rescue operations is one of the critical results of traffic congestion. This is an undesirable situation as the safety of the general public depends on these services being able to arrive at the scene of an accident in the efficient and shortest time possible. Intelligent transportation systems (ITSs) have not yet been able to resolve the challenge of traffic congestion. For example, Google Maps still uses communication networks that exist such as Wi-Fi, global positioning system (GPS), and cellular networks to plan journeys and mitigate the challenge of being stuck in traffic. However, apart from being expensive, these technologies still do not have the capacity to respond with speed when there is an emergency resulting from a road accident. These systems have limited access to real-time traffic data. Hence, it is important that these emergency services should be equipped with a system that can enhance the adaptability of route planning so information can be collected more efficiently and real-time traffic data can be further exploited to avoid traffic congestion.

Firstly, emerging vehicular ad hoc networks (VANETs) can be used to deliver on ITS system with improved communications capabilities for obtaining real-time traffic information more efficiently and in a cost-effective manner . VANETs support vehicle-to-roadside unit (V2R) and vehicle-to-vehicle communications, so that real-time updates can be transmitted from and to vehicles and roadside units (RSUs). This real-time information which will be collected can then be consequently used for route planning in individual vehicles, freeway-traffic-flow management, and vehicle localization [12–14]. Secondly, once the real-time traffic information has been obtained, various algorithms can be designed with the aim of discovering the most efficient routes that individual vehicles can take. However, if the route planning is deployed in a manner which is not coordinated, this could lead to even more congestion. While most of the available routes planning global algorithms pay attention to the improvements linked to networks, they generally overlook driver preferences, such as travel distances and times. This is important, particularly if one considers that replanning decisions are a consequence of the need to avoid traffic congestion and balancing of traffic as opposed to the discovery of optimal routes. Hence, some drivers may actually prefer to take longer routes even though this may entail higher costs. Hence, the design of algorithms should be done in a way that considers both the reduction of the average cost of running the vehicle and network traffic.