The growing size of cities and increasing population mobility have determined a rapid increase in the number of vehicles on the roads, which has resulted in many challenges for road traffic management authorities in relation to traffic congestion, accidents and air pollution. Over the recent years, researchers from both industry and academia were focusing their efforts on exploiting the advances in sensing, communication and dynamic adaptive technologies to make the existing road Traffic Management Systems (TMS) more efficient to cope with the above issues in future smart cities. However, these efforts are still insufficient to build a reliable and secure TMS that can handle the foreseeable rise of population and vehicles in smart cities. In this survey, we present an up to date review of the different technologies used in the different phases involved in a TMS, and discuss the potential use of smart cars and social media to enable fast and more accurate traffic congestion detection and mitigation. We also provide a thorough study of the security threats that may jeopardize the efficiency of the TMS and endanger drivers' lives. Furthermore, the most significant and recent European and worldwide projects dealing with traffic congestion issues are briefly discussed to highlight their contribution to the advancement of smart transportation. Finally, we discuss some open challenges and present our own vision to develop robust TMSs for future smart cities.

Abstract-There are interdependent increases in vehicle numbers, vehicular traffic congestion, and carbon emissions that cause major problems worldwide. These problems include direct negative influences on people's health, adverse economic effects, negative social impacts, local environmental damage, and risk of catastrophic global climate change. There is a drastic need to develop ways to reduce these emissions and EcoTrec, presented in this paper, is one of these innovative approaches. EcoTrec is a vehicular ad hoc network-based vehicle routing solution designed to reduce vehicle carbon emissions without significantly affecting the travel times of vehicles. The vehicles exchange messages related to traffic and road conditions, such as average speed on the road, road gradient, and surface condition. This information is used to build a fuel efficiency model of the routes, based on which the vehicles are recommended to take more efficient routes. By routing vehicles more efficiently, the greenhouse emissions are reduced while also maintaining low traffic congestion levels. This paper presents results of extensive simulations, which show how EcoTrec outperforms other state-of-the-art solutions with different number of vehicles, vehicle penetration, and compliance rates, and when considering different real world road maps from Dublin and Koln.

Abstract-The lack of significant breakthroughs in terms of alternative energy sources has caused both fuel consumption and gas emissions to constantly increase. In this context, improving fuel efficiency and reducing emissions in the transportation sector is vital, as vehicles are one of the important contributors to air pollution. This paper introduces EcoTrec, a novel eco-friendly routing algorithm for vehicular traffic which considers road characteristics such as surface conditions and gradients, as well as existing traffic conditions to improve the fuel savings of vehicles and reduce gas emissions. EcoTrec makes use of the Vehicular Ad-hoc NETworks (VANET) both for collecting data from distributed vehicles and to disseminate information in aid of the routing algorithm. The algorithm calculates the fuel efficiency of various routes and then directs the vehicle to a fuel efficient route, while also avoiding flash crowding. Simulationbased tests showed that by using EcoTrec, fuel emissions were significantly reduced, when compared with existing state-of-theart vehicular routing algorithms.

1 Abstract-Most cities have special lanes dedicated to buses, however these lanes are rarely used at full capacity. At the same time governments around the world are encouraging people to buy electric vehicles. This paper proposes the creation of electric vehicle enhanced dedicated bus lanes (E-DBL), by allowing electric vehicles access to bus lanes, in order to improve the use of road capacity. By opening bus lanes to electric vehicles, traffic congestion could be eased, the range of electric vehicles could be extended, and the travel times for electric vehicle owners could be reduced significantly. The paper shows how by introducing EDBLs, the bus journey times are not significantly affected given the current uptake of electric vehicles in most developed countries. This paper presents extensive simulations based on traffic situation in the city of Dublin with regard to the effect of opening up bus lanes to electric vehicles. The results show that even with very high percentages of electric vehicles the bus journey times are not noticeably affected. Opening up bus lanes to electric vehicles can even be beneficial for other road users by reducing congestion on regular lanes, which would further reduce carbon emissions.

Abstract-Increasing amounts of time is wasted due to traffic congestion in both developed and developing countries. This has severe negative effects, including drivers stress due to increased time pressure, reduced usage efficiency of trucks and other commercial vehicles, and increased gas emissions--responsible for climate change and air pollution affecting population health in densely populated areas. As existing centralized approaches were neither efficient, nor scalable, there is a need for alternative approaches. Social insects provide many solutions for dealing with decentralized problems. For instance ants choose their routes based on pheromones left by previous ants. However, Ant Colony Optimization is not directly applicable to vehicle routing, as routing the vehicles to the same road would cause traffic congestion. Yet, the traffic is broadly similar from work-to work-day. This paper introduces an ant-colony optimizationbased algorithm called Time-Ants. Time-Ants considers that an amount of "pheromone" or a traffic rating is assigned to each road at any given time in the day. Using an innovative algorithm the vehicle's routes are chosen based on these traffic ratings, aggregated in time. After several iterations this results in a global optimum for the traffic system. Bottlenecks are identified and avoided by machine learning. Time-Ants outperforms another leading algorithm by up to 19% in terms of percentage of vehicles to reach the destination within a given time-frame.