ABSTRACT
Due to major development in urbanization and the expanding population, traffic congestion has become a critical problem in metropolitan cities. Traffic congestion happens whenever the demand exceeds the maximum capacity of a road. Therefore, to make optimum use of the road network capacity, intelligent transport systems (ITS) are often employed to cope with this situation efficiently. Using advanced sensor technologies, real-time traffic information is collected from large transportation networks and utilized in various applications, such as route guidance, congestion avoidance, traffic control and management, etc. Apart from regular instances of peak-hour congestion, the unexpected occurrence of non-recurrent traffic events such as accidents, vehicle breakdowns, and road crashes causes about 25% of traffic congestion on arterial roads and an even higher proportion for urban highways and expressways. Every year, 1.35 million people die on average as a result of road traffic crashes. In addition, 20–50 million people suffer from non-fatal injuries, sometimes leading to short-term or long-term disability. Moreover, non-recurring incidents lead to significant economic losses because of their unpredictable nature. Road accidents can cost up 176to 3% of a country’s gross domestic product. Therefore, anticipating such events in advance can be highly useful in mitigating the resultant congestion and therefore benefiting the national economy as a whole. However, since these types of incidents are non-recurrent and unplanned, the probability of occurrence of these incidents is hard to forecast. Therefore, ITSs are more concerned with minimizing the severity of congestion after the incidents have already occurred. The two integral systems of ITS are traffic information management systems (TIMS) and dynamic routing guidance systems (DRGS). Both of these systems play a vital role because TIMS is responsible for real-time data acquisition of traffic parameters, like speed, the number of vehicles passing by, weather conditions, and DRGS help commuters to dynamically choose a route by providing information on network traffic and other possible routes to be taken. The two techniques used for traffic prediction are either simulation based or data driven. In a simulation-based approach, traffic prediction models which predict the future state are designed based on some theoretical models. This approach needs some expertise to build network traffic simulation. On the other hand, data-driven models can be built for prediction with the usage of historical or real-time data sets.
Apart from the predictive solutions, there are several other new technologies to assist drivers in the occurrence of an incident. For example, traffic management authorities have installed the new age variable message signs (VMS displays) with modern technologies (such as graphics and more colors) on the roads of cities. These LED road traffic signs notify drivers about any kind of disruption in traffic, such as accidents, obstacles, and roadworks, and therefore help in rerouting vehicles. Nowadays, VMS systems form an integral part of DRGS. Therefore, traffic management authorities from several smart cities have been investing a significant amount of resources in installing VMS displays in different places. Thus, ITS are often being employed in different aspects of transportation to provide better mobility solutions for commuters and drivers. Moreover, advancements in sensor technologies have helped ITS to improve the efficiency of existing transportation infrastructure. In this chapter, we address the applications of ITS for incident management and congestion avoidance in real time.
