Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO₂ emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario.

The continuous transfer of messages in vehicular ad hoc networks leads to a heavy network traffic load. This causes congestion in the wireless channel which degrades the reliability of the network and significantly affects the Quality of Service (QoS) parameters such as packet loss, throughput and average delay. Therefore, it is vital to adapt the transmitting data rates in a way that ensure that acceptable performance is achieved and that there is reliable communication of information between vehicles in smart cities. This means the information will be delivered in a timely manner to the drivers, which in turn allows implementation of efficient solutions for improved mobility and comfort in intelligent transportation systems. In this paper, congestion control in the communication channel has been formulated as a non-cooperative game approach and the vehicles act as players in the game to request a high data rate in a selfish way. The solution of the optimal game is presented by using Karush-Kuhn-Tucker conditions and Lagrange multipliers. Simulation results show that the proposed method improves network efficiency in the presence of congestion by an overall average of 50.40%, 49.37%, 58.39% and 36.66% in terms of throughput, average delay, number of lost packets and total channel busy time as compared to Carrier-Sense Multiple Access with Collision Avoidance mechanism.

Communications between the Internet of Vehicles in smart cities helps increase the awareness and safety among drivers. However, the channel congestion problem is considered as a key challenge for the communication networks due to continuing collection and exchange of traffic information in dense environments. The channel congestion problem degrades the efficiency and reliability of the ad hoc network. Therefore, the adaptation of the data rate and power control is considered as one of the effective solutions to mitigate channel congestion. A new hybrid game transmission rate and power channel congestion control approach on the Internet of Vehicle networks where the nodes play as greedy opponents demanding high information rates with the maximum power level are developed. Furthermore, the existence of a Nash equilibrium, which is the optimal information rate and power transmission for every vehicle, is established. Simulation results demonstrate that the proposed approach enhances the network performance by an overall percentage of 42.27%, 43.94% and 47.66% regarding channel busy time, messages loss and data collision as compared with others. This increases the awareness and performance of the vehicular communication network.