## 1 Introduction  
Smart city represents a paradigm that is associated with a significant shift of interest towards developing and utilizing multiple innovative communications technologies to make today's cities more _intelligent_ and thus improve people's quality of life. Being a product of accelerated development employing the advanced information and communications technology (ICT) as well as the knowledge-based economy, smart cities [1] comprise an integration of the Internet, a telecommunications network, a broadcast network, and a wireless broadband network, where the Internet of Things (IoT) becomes a core building block - smart cities include a high degree of ICT integration and a comprehensive application of information resources. The essential components of urban development for smart cities typically contain (i) smart technology; (ii) smart industry; (iii) smart services; (iv) smart management; and (v) smart life [2; 3]. Over the past years, the European Commission (EC) has launched a European Initiative on Smart Citiesaddressing four pillars of a modern city: buildings, electricity, heating and cooling systems, and transportation [4]. With regards to the latter, the goal of the EC is to identify and support the viable forms of transportation as well as construct intelligent public transit systems based on real-time information, traffic management systems (TMSs) for congestion avoidance, and green applications (e.g., reducing fuel and/or energy consumption) [5]. It is worth mentioning that the number of vehicles that use the existing road network infrastructure in urban areas has seen a tremendous growth [6]. A major consequence of this avalanche is related to the management problems, which range from traffic congestion control to driving safety and environmental impact [7]. Over the recent years, researchers from both industry and academia have been focusing their efforts on exploiting the advances in sensing, communications, and dynamic adaptive technologies to make the deployed road TMSs more efficient with respect to the aforementioned issues within the future smart cities. For traffic management, one of the most critical consequences of road congestion is related to delaying the emergency services (i.e., police, fire, and rescue operations, or medical services). Indeed, human lives, general population safety, and financial risks in case of accidents or criminal attacks strongly depend on the efficiency and travel time of the emergency vehicles. Based on the recent road traffic statistics, an increased number of vehicle crashes was revealed-following the available data, those occur frequently in the areas around congested roads, since the drivers tend to drive faster before or after encountering a traffic jam, with the aim to compensate for the experienced time delays [6]. Today, the world's largest cities are massively suffering from the traffic congestion despite employing sophisticated mechanisms to reduce it, including the use of TMSs that are advanced congestion control mechanisms. In order to augment the past research efforts aimed at solving the traffic congestion problem (or at least reducing its impact), there is a crucial need to investigate two different types of congestion: (i) recurrent and (ii) nonrecurrent. The _recurrent_ congestion happens when a large number of vehicles utilize a certain part of the road network at the repeating time intervals (e.g., morning and afternoon peak hours on weekdays). On the other hand, the _nonrecurrent_ congestion mainly follows a random pattern of events, such as traffic accidents (e.g., car crashes or stalled vehicles on the road), bad weather conditions, or special cases including sport events, concerts, etc. According to the latest information [8], traffic congestion costs billions to the global economy. To illustrate the current situation, the corresponding losses have reached 200 E billion in Europe (2% of the GDP) and $101 billion in the USA.