Abstract:
Due to the 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 up with this situation efficiently. Using advanced sensor technologies, the 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 the regular peak hour congestion, the unexpected occurrence of non-recurrent traffic events such as accidents, vehicle breakdowns, road crashes, etc. causes about 25% of traffic congestion on the arterial roads and even a higher proportion for urban highways and expressways. Every year 1.35 million people die on an average as a result of road traffic crashes. Besides, 20-50 million people suffer from non-fatal injuries, sometimes leading to
short-term or long-term disability. Moreover, the non-recurring incidents lead to signi ficant economic losses because of their unpredictable nature. The road accidents can cost upto 3% of the countries' gross domestic product. Therefore, anticipating such events in advance can be highly useful in mitigating the resultant congestion and therefore benefit ing 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, Intelligent transport systems (ITS) are more concerned about minimizing the severity of congestion after the incidents have already happened. The two integral systems of ITS are Traffic Information Management System (TIMS) and Dynamic Routing Guidance System (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 condition of the roadway, etc., and DRGS helps the commuters to dynamically choose the routes 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 the 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 the drivers in the occurrence of an incident. For example, the 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 the cities. These LED road traffic signs notify the drivers about any kind of disruption in traffic, such as accidents, obstacles, roadworks, etc., and therefore
help in rerouting the vehicles. Nowadays, VMS system is an integral part of Dynamic Routing Guidance System. Therefore, traffic management authorities from several smart cities have been investing a
signi ficant amount of resources to install the 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.