Red Swarm: Smart Mobility in Cities with EAs

This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance.Ministerio de Economía y Competitividad y FEDER (TIN2011-28194

Using Ontologies and Intelligent Systems for Traffic Accident Assistance in Vehicular Environments

A pesar de que las medidas de seguridad en los sistemas de transporte cada vez son mayores, el aumento progresivo del número de vehículos que circulan por las ciudades y carreteras en todo el mundo aumenta, sin duda, la probabilidad de que ocurra un accidente. En este tipo de situaciones, el tiempo de respuesta de los servicios de emergencia es crucial, ya que está demostrado que cuanto menor sea el tiempo transcurrido entre el accidente y la atención hospitalaria de los heridos, mayores son sus probabilidades de supervivencia. Las redes vehiculares permiten la comunicación entre los vehículos, así como la comunicación entre los vehículos y la infraestructura [4], lo que da lugar a una plétora de nuevas aplicaciones y servicios en el entorno vehicular. Centrándonos en las aplicaciones relacionadas con la seguridad vial, mediante este tipo de comunicaciones, los vehículos podrían informar en caso de accidente al resto de vehículos (evitando así colisiones en cadena) y a los servicios de emergencia (dando información precisa y rápida, lo que sin duda facilitaría las tareas de rescate). Uno de los aspectos importantes a determinar sería saber qué información se debe enviar, quién será capaz de recibirla, y cómo actuar una vez recibida. Actualmente los vehículos disponen de una serie de sensores que les permiten obtener información sobre ellos mismos (velocidad, posición, estado de los sistemas de seguridad, número de ocupantes del vehículo, etc.), y sobre su entorno (información meteorológica, estado de la calzada, luminosidad, etc.). En caso de accidente, toda esa información puede ser estructurada y enviada a los servicios de emergencia para que éstos adecúen el rescate a las características específicas y la gravedad del accidente, actuando en consecuencia. Por otro lado, para que la información enviada por los vehículos accidentados pueda llegar correctamente a los servicios de emergencias, es necesario disponer de una infraestructura capaz de dar cobertura a todos los vehículos que circulan por una determinada área. Puesto que la instalación y el mantenimiento de dicha infraestructura conllevan un elevado coste, sería conveniente proponer, implementar y evaluar técnicas consistentes en dar cobertura a todos los vehículos, reduciendo el coste total de la infraestructura. Finalmente, una vez que la información ha sido recibida por las autoridades, es necesario elaborar un plan de actuación eficaz, que permita el rápido rescate de los heridos. Hay que tener en cuenta que, cuando ocurre un accidente de tráfico, el tiempo de personación de los servicios de emergencia en el lugar del accidente puede suponer la diferencia entre que los heridos sobrevivan o fallezcan. Además, es importante conocer si la calle o carretera por la que circulaban los vehículos accidentados ha dejado de ser transitable para el resto de vehículos, y en ese caso, activar los mecanismos necesarios que permitan evitar los atascos asociados. En esta Tesis, se pretende gestionar adecuadamente estas situaciones adversas, distribuyendo el tráfico de manera inteligente para reducir el tiempo de llegada de los servicios de emergencia al lugar del accidente, evitando además posibles atascos.Barrachina Villalba, J. (2014). Using Ontologies and Intelligent Systems for Traffic Accident Assistance in Vehicular Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39004TESI

Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model

Although connectivity services have been introduced already today in many of the most recent car models, the potential of vehicles serving as highly mobile sensor platform in the Internet of Things (IoT) has not been sufficiently exploited yet. The European AutoMat project has therefore defined an open Common Vehicle Information Model (CVIM) in combination with a cross-industry, cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged for the design of entirely new services even beyond traffic-related applications (such as localized weather forecasts). This paper focuses on the prediction of the achievable data rate making use of an analytical model based on empirical measurements. For an in-depth analysis, the CVIM has been integrated in a vehicle traffic simulator to produce CVIM-complaint data streams as a result of the individual behavior of each vehicle (speed, brake activity, steering activity, etc.). In a next step, a simulation of vehicle traffic in a realistically modeled, large-area street network has been used in combination with a cellular Long Term Evolution (LTE) network to determine the cumulated amount of data produced within each network cell. As a result, a new car-to-cloud communication traffic model has been derived, which quantifies the data rate of aggregated car-to-cloud data producible by vehicles depending on the current traffic situations (free flow and traffic jam). The results provide a reference for network planning and resource scheduling for car-to-cloud type services in the context of smart cities

An overview of VANET vehicular networks

Today, with the development of intercity and metropolitan roadways and with various cars moving in various directions, there is a greater need than ever for a network to coordinate commutes. Nowadays, people spend a lot of time in their vehicles. Smart automobiles have developed to make that time safer, more effective, more fun, pollution-free, and affordable. However, maintaining the optimum use of resources and addressing rising needs continues to be a challenge given the popularity of vehicle users and the growing diversity of requests for various services. As a result, VANET will require modernized working practices in the future. Modern intelligent transportation management and driver assistance systems are created using cutting-edge communication technology. Vehicular Ad-hoc networks promise to increase transportation effectiveness, accident prevention, and pedestrian comfort by allowing automobiles and road infrastructure to communicate entertainment and traffic information. By constructing thorough frameworks, workflow patterns, and update procedures, including block-chain, artificial intelligence, and SDN (Software Defined Networking), this paper addresses VANET-related technologies, future advances, and related challenges. An overview of the VANET upgrade solution is given in this document in order to handle potential future problems

Distributed Road Traffic Congestion Quantification Using Cooperative VANETs

The well-known traffic congestion problem in urban environments has negative impact on many areas including economy, environment, health and lifestyle. Recently, a number of solutions based on vehicle-to-vehicle communications were proposed for traffic congestion detection and management. In this paper we present an algorithm designed to enable each vehicle in the network to detect and quantify the level of traffic congestion in completely distributed way, independent of any supporting infrastructure and additional information such as traffic data from local authorities. Based on observations of traffic congestion by every vehicle, and by adapting the broadcast interval, it enables dissemination of the traffic information to other vehicles. The algorithm also makes every vehicle aware about the congestion level on the streets that are spatially separated from their current location by several streets. Its robustness keeps the vehicle's overall knowledge about congestion consistent, despite the short-term changes in vehicle's motion. Since the quantification of congestion is based on per-vehicle basis, the algorithm is able to operate even when only 10% of vehicles in the network are VANET enabled. Data aggregation and adaptive broadcasting are used to ensure that vehicles do not send redundant information about the traffic congestion. The simulations are conducted in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator

On the Potential of Generic Modeling for VANET Data Aggregation Protocols

In-network data aggregation is a promising communication mechanism to reduce bandwidth requirements of applications in vehicular ad-hoc networks (VANETs). Many aggregation schemes have been proposed, often with varying features. Most aggregation schemes are tailored to specific application scenarios and for specific aggregation operations. Comparative evaluation of different aggregation schemes is therefore difficult. An application centric view of aggregation does also not tap into the potential of cross application aggregation. Generic modeling may help to unlock this potential. We outline a generic modeling approach to enable improved comparability of aggregation schemes and facilitate joint optimization for different applications of aggregation schemes for VANETs. This work outlines the requirements and general concept of a generic modeling approach and identifies open challenges

Employing VANET technology to alleviate road congestion in real time

Nowadays, traffic road jams are considered one of the most serious problems facing a large group of people, where many drivers die or are exposed to serious injuries because of road accidents that occur due to road congestion, driver's behaviour by disobeying the rules and traffic lights or bad management for traffic system. The eagerness on developing the deployment traffic road information among vehicles to reduce road accident and to make the journey more safe was the main motivation behind the improvement of Vehicular Ad Hoc Networks VANETs. VANETs are a special category of Mobile Ad Hoc Networks (MANET), which considered the cornerstone for Intelligent Transportation System (ITS) that used to established communication among vehicles on a road using an infrastructure-less network. On this study, we will use graph model representation to design a novel algorithm which can deal with Instantaneous accident/incident occasions, road works and roads amendments to provide instant actions list to the transportation team to reduce congestion in shorter time, and to use wireless access technologies to send individual message to the relevant vehicles to avoid the congestion through Road Side Units (RSUs) devices. The aim of this research is to develop Intelligent Transportation System (ITS) for a city (e.g. Northampton) using VANETs technology, specifically vehicle to infrastructure communications which lead to reducing the traffic congestion or mitigation. The performance of proposed study will be evaluated via various congested scenarios using simulations software (Omnet++, SUMO and Veins) to assess the validity of the proposed algorithm