## 2 The Concern of Traffic Management in Smart Cities and Urban Environments
A model is proposed to develop a system that can handle a large volume of city data and provide guidelines to the local authorities. The system implementation involves several stages, including data generation and gathering, aggregation, filtering, classification, preprocessing, computing, and decision making. The gathered data from smart components within the city is processed in real-time to achieve a smart service using Hadoop under Apache Spark. Data generated by smart homes, smart parking areas, weather systems, pollution monitoring sensors, and vehicle networks are used for analysis and testing. In the work of [PERSON] and [PERSON] [16], a study on the effect of path selection in emission-oriented urban vehicle routing is presented, which is composed of a heterogeneous road network with regard to speed and acceleration frequency. An algorithm to determine all means of emission minimization for pairs of origin and destination nodes given a specific vehicle is proposed and tested using the road network from the city of Berlin. In [PERSON] et al. [17], two data-driven approaches for determining time-dependent emission minimizing paths in urban environments are proposed. Their performances are compared with respect to computing efficiency and solution quality. On average, emissions-minimizing methods can reduce emissions by approximately 3.5% compared to distance-minimizing paths, and 5.0% with regard to minimum time-dependent paths. The emissions-optimized path is roughly 4% longer than the paths generated by distance-based methods, and around 6% longer with regard to travel time compared to travel-time optimized paths. In the work of [PERSON] [18], attention is paid on the so-called pollution routing problem (PRP), which tries to minimize the fuel consumption or pollutants emission of trucks. The final goal is to develop a decision support system of pollution vehicle routing for eco-friendly enterprises. The work identifies, by analyzing the state-of-the-art PRP and gathering expert opinions from carrier managers, a practical PRP model that uses a minimal subset of the main factors affecting fuel consumption, and then develops a solution for this model. In [PERSON] et al. [19], research is focused on the problem of minimizing CO\({}_{2}\) emissions in the routing of a fleet of capacitated vehicles in urban environments. A local search procedure, named the tabu search heuristic, is adapted to solve the problem. The research uses instances from a real road network dataset and 230 million speed observations. In [PERSON] et al. [20], a metaheuristic approach, called ILS-SOA-SP, is proposed to solve the PRP; this method integrates iterated local search (ILS) with a set partitioning (SP) procedure and a speed optimization algorithm (SOA). The proposed method has the benefit of performing combined route and speed optimization within several local search and integer programming components. As a summary, a wide variety of research has been carried out with regard to the improvement of quality of life in smart cities in terms of traffic management and, consequently, the reduction of fuel consumption, street noise, and pollution.