In cities, where the number of vehicles continuously increases faster than the available traffic infrastructure to support them, congestion is a difficult issue to deal with and it becomes even worse in case of car accidents. This problem affects many aspects of the modern society, including economic development, traffic accidents, increase in greenhouse emissions, time spent, and health damages. In this context, modern societies can rely on traffic management system to minimize traffic congestion and its negative effects. Traffic management systems are composed of a set of application and management tools to improve the overall traffic efficiency and safety of the transportation systems. Furthermore, to overcome such issue, traffic management system gathers information from heterogeneous sources, exploits such information to identify hazards that may potentially degrade the traffic efficiency, and then provides services to control them. With this question in mind, this article presents a classification, review, challenges, and future perspectives to implement a traffic management system.

Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing services closer to vehicular users. In VEC, vehicles and communication infrastructures can form pools with computational resources to meet vehicular services with low-latency constraints. These resource pools are known as Vehicular Cloud (VC). The usage of VC resources requires a task scheduling process. In this case, depending on its complexity, a vehicular service can be divided into different tasks. An efficient task scheduling needs to orchestrate where and for how long such tasks will run, considering the available pools, the mobility of nodes, and the tasks deadline constraints. Thus, this article proposes an efficient VC task scheduler based on an approximation heuristic and resources prediction to select the best VC for each task, called MARINA. MARINA aims to analyze the behavior of vehicles that share their computational resources with the VC and make scheduling decisions based on the mobility (VC availability) of these vehicles. Simulation results under a realistic scenario demonstrate the efficiency of MARINA compared to existing state-of-the-art mechanisms in terms of the number of tasks scheduled, monetary cost, system latency, and Central Processing Unit (CPU) utilization.

Vehicular traffic re-routing is the key to provide better traffic mobility. However, taking into account just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of proper transportation management. In this way, context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most procedures are deterministic and cannot support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. So, we propose an efficient algorithm named as Better Safe Than Sorry (BSTS), based on Pareto-efficiency. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.