Abstract

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39247-4_6 Road traffic is experiencing a drastic increase in recent years, thereby increasing the every day traffic congestion problems, especially in cities. Vehicle density is one of the main metrics used for assessing the road traffic conditions. Currently, most of the existing vehicle density estimation approaches, such as inductive loop detectors or traffic surveillance cameras, require infrastructure-based traffic information systems to be installed at various locations. In this paper, we present I-VDE, a solution to estimate the density of vehicles that has been specially designed for Vehicular Networks. Our proposal allows Intelligent Transportation Systems to continuously estimate the vehicular density by accounting for the number of beacons received per Road Side Unit, as well as the roadmap topology. Simulation results indicate that our approach accurately estimates the vehicular density, and therefore automatic traffic controlling systems may use it to predict traffic jams and introduce countermeasures. Barrachina Villalba, J.; Garrido Picazo, MP.; Fogue, M.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). I-VDE: A Novel Approach to Estimate Vehicular Density by Using Vehicular Networks. En Ad-hoc, Mobile, and Wireless Network. Springer. 63-74. doi:10.1007/978-3-642-39247-4_6 Senia 63 74 Document type: Part of book or chapter of book

Abstract

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works [EN] A critical issue, especially in urban areas, is the occurrence of traffic accidents, since it could generate traffic jams. Additionally, these traffic jams will negatively affect to the rescue process, increasing the emergency services arrival time, which can determine the difference between life or death for injured people involved in the accident. In this paper, we propose four different approaches addressing the traffic congestion problem, comparing them to obtain the best solution. Using V2I communications, we are able to accurately estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the emergency services arrival time, and avoiding traffic jams when an accident occurs. Specifically, we propose two approaches based on the Dijkstra algorithm, and two approaches based on Evolution Strategies. Results indicate that the Density-Based Evolution Strategy system is the best one among all the proposed solutions, since it offers the lowest emergency services travel times. This work was partially supported by the Ministerio de Ciencia e Innovacióm , Spain, under Grant TIN2011-27543-C03-01, as well as by the Fundación Universitaria Antonio Gargallo, the Obra Social de Ibercaja, the Government of Aragon, and the European Social Fund (T91 Research Group). Barrachina Villalba, J.; Garrido, P.; Fogue, M.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Using evolution strategies to reduce emergency services arrival time in case of accident. En 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. IEEE. 833-840. doi:10.1109/ICTAI.2013.127 S 833 840

Abstract

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. [EN] Quickly identifying the severity of highway acci-dents, as well as the resources required to assist the people involved in those accidents, is a basic requirement for future intelligent transportation systems. In this context, vehicular communication technologies currently being standardized are able to provide novel solutions to address this problem. In this work we study the feasibility of combining vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to deliver a video stream from the place of the accident to the traffic authorities. Our approach relies on vehicles as data relays, thus having the additional advantage of providing drivers with a clear view about the accident, thereby helping to reduce stress and improving traffic flow. An experimental analysis comparing different traffic flooding mechanisms for wireless networks show that the proposed system is viable for highways with moderate/high amounts of traffic, although highlighting the need for more efficient mechanisms specifically addressing broadcast propagation in highway envi-ronments. This work was partially supported by the Ministerio de Economía y Competitividad, Spain, under Grant TIN2011- 27543-C03-01, by the Ministerio de Educación, Spain, under the FPU program, AP2009-2415, and by the National Institute of Informatics International Internship Program. Torres Cortes, A.; Ji, Y.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2014). V2X solutions for real-time video collection. En Wireless On-demand Network Systems and Services (WONS), 2014 11th Annual Conference on. IEEE. 116-121. doi:10.1109/WONS.2014.6814731 S 116 121

Abstract

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Road traffic is experiencing a drastic increase, and vehicular traffic congestion is becoming a major problem, especially in metropolitan environments throughout the world. Additionally, in modern Intelligent Transportation Systems (ITS) communications, the high amount of information that can be generated and processed by vehicles will significantly increase message redundancy, channel contention, and message collisions, thus reducing the efficiency of message dissemination processes. In this work, we present a V2X architecture to estimate traffic density on the road that relies on the advantages of combining V2V and V2I communications. Our proposal uses both the number of beacons received per vehicle (V2V) and per RSU (V2I), as well as the roadmap topology features to estimate the vehicle density. By using our approach, modern Intelligent Transportation Systems will be able to reduce traffic congestion and also to adopt more efficient message dissemination protocols. This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01, by the Fundación Universitaria Antonio Gargallo and the Obra Social de Ibercaja, under Grant 2013/B010, as well as the Government of Aragón and the European Social Fund (T91 Research Group). Barrachina Villalba, J.; Sangüesa, JA.; Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.... (2013). V2X-d: a Vehicular Density Estimation System that combines V2V and V2I Communications. IEEE. doi:10.1109/WD.2013.6686518 S

Abstract

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Vehicle density is one of the main metrics used for assessing the road traffic conditions. In this paper, we present a solution to estimate the density of vehicles that has been specially designed for Vehicular Networks. Our proposal allows Intelligent Transportation Systems to continuously estimate the vehicular density by accounting for the number of beacons received per Road Side Unit, as well as the roadmap topology. Simulation results indicate that our approach accurately estimates the vehicular density, and therefore automatic traffic controlling systems may use it to predict traffic jams and introduce countermeasures. Index Terms—Vehicular Networks, vehicular density estimation, Road Side Unit, VANETs. This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01. Barrachina Villalba, J.; Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Assessing Vehicular Density Estimation Using Vehicle-to-Infrastructure Communications. IEEE. doi:10.1109/WoWMoM.2013.6583416 S