## 1 Introduction
Regarding the travel times, the aggregate delays of over 4.8 billion hours were observed due to congestion, while 7.2 billion liters of fuel were wasted worldwide. These statistics provide a clear indication of the devastating effects that road congestion has on people, companies, and society. Unfortunately, the existing TMSs are still unable to provide detailed and accurate information to enable fine-grained and timely monitoring and management of the road traffic [9; 10]. The underlying reasons include the (i) lack of granular data collection; (ii) inability to aggregate the needed volume of data (see e.g., the Big Data paradigm [11]); and (iii) absence of the adequate management systems that report on the actual state of the road network [12]. This leads to an overall inability to effectively monitor and manage the traffic, which negatively affects road safety and fuel consumption, as well as causes increased gas emissions [8]. Presently, the implemented solutions utilized by the existing TMSs to manage the road traffic (e.g., after an accident or during the rush hours) are e.g., adapting traffic lights intervals (cycles) or dynamically closing road lanes and intersections. However, these solutions have rather limited efficiency when increasing numbers of vehicles [13] are using the road infrastructure of a fixed capacity--since efficiency of discrete solutions is limited. Therefore, new implementations and mechanisms are being proposed by the research community to improve the traffic management systems. Authors in [14] present the stochastic traffic environment utilizing a genetic algorithm that optimizes the traffic signal timing (based on a characteristic non-linear function that uses two major performance measures: (i) queuing lengths; and (ii) vehicular waiting times). In [15], the adaptive traffic control system (ATAK) is discussed, which is able to optimize traffic signal timings on signalized road network in real time. Further, simulation of proposed genetic algorithm (for management of the traffic signal timing plan) is elaborated in [16]. Another approach, where the technology able to count the number of vehicles using the video image detection is described in [17]. Aiming to integrate the simulator and the created prototype, authors in [18] introduce a solution for verification of the proposed smart traffic optimization system--the communication between vehicles was realized using the ZigBee-based technology. In this paper, we provide a systematic study of the promising solutions to be employed by the existing and future TMSs, by specifically investigating the different phases of a modern traffic management system in a smart city environment, see Figure 1. In particular, this work discusses two important phases. The first phase is named data sensing and gathering and utilizes heterogeneous road monitoring equipment that measures and communicates the traffic parameters (e.g., traffic volume, vehicle speed, road segment occupancy, etc.) to a traffic management entity (TME). Furthermore, this paper highlights the advantages of various related improvements, such as mobile sensing to improve the efficiency and accuracy of a TMS. Based on the pattern of the collected information, the second phase introduces our created environment for modular traffic modeling that allows a user to construct a road network under investigation in its full resemblance. Describing the topography of the problem at hand together with the traffic control signalization features, the resulting model exhibits qualitatively and quantitatively appropriate behavior. Our proposed environment is further examined on several low-power and low-energy (embedded) devices with respect to meeting the IoT goals, where power-constrained equipment will take over as computation units located throughout a smart city [19]. Figure 1: Communications chain of data feeds in smart transportation (parts of the traffic management system). The rest of this paper is organized as follows.