Promoting Public Health and Safety: A Predictive Modeling Software Analysis on Perceived Road Fatality Contributory Factors

Extensive literature search was conducted to computationally analyze the relationship between key perceived road fatality factors and public health impacts, in terms of mortality and morbidity. Heterogeneous sources of data on road fatality 1970-2005 and that based on interview questionnaire on European road drivers’ perception were sourced. Computational analysis was performed on these data using the Multilayer Perceptron model within the dtreg predictive modeling software. Driver factors had the highest relative significance. Drivers played significant role as causative agents of road accidents. A good degree of correlation was also observed when compared with results obtained by previous researchers. Sweden, UK, Finland, Denmark, Germany, France, Netherlands, and Austria, where road safety targets were set and EU targets adopted, experienced a faster and sharper reduction of road fatalities. However, Belgium, Ireland, Italy, Greece and Portugal experienced slow, but little reduction in cases of road fatalities. Spain experienced an increase in road fatalities possibly due to road fatalities enhancing factors. Estonia, Slovenia, Cyprus, Hungry, Czech Republic, Slovakia and Poland experienced a fluctuating but decreasing trend. Enforcement of road safety principles and regulations are needed to decrease the incidences of fatal accidents. Adoption of the EU target of -50% reductions of fatalities in all countries will help promote public health and safety

A system for automatic notification and severity estimation of automotive accidents

© 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.New communication technologies integrated into modern vehicles offer an opportunity for better assistance to people injured in traffic accidents. Recent studies show how communication capabilities should be supported by artificial intelligence systems capable of automating many of the decisions to be taken by emergency services, thereby adapting the rescue resources to the severity of the accident and reducing assistance time. To improve the overall rescue process, a fast and accurate estimation of the severity of the accident represent a key point to help emergency services better estimate the required resources. This paper proposes a novel intelligent system which is able to automatically detect road accidents, notify them through vehicular networks, and estimate their severity based on the concept of data mining and knowledge inference. Our system considers the most relevant variables that can characterize the severity of the accidents (variables such as the vehicle speed, the type of vehicles involved, the impact speed, and the status of the airbag). Results show that a complete Knowledge Discovery in Databases (KDD) process, with an adequate selection of relevant features, allows generating estimation models that can predict the severity of new accidents. We develop a prototype of our system based on off-the-shelf devices and validate it at the Applus+ IDIADA Automotive Research Corporation facilities, showing that our system can notably reduce the time needed to alert and deploy emergency services after an accident takes place.This work was partially supported by the Ministerio de Ciencia e Innovacion, Spain, under Grant TIN2011-27543-C03- 01, and by the Diputacion General de Aragon, under Grant "subvenciones destinadas a la formacion y contratacion de personal investigador."Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2014). A system for automatic notification and severity estimation of automotive accidents. IEEE Transactions on Mobile Computing. 13(5):948-963. https://doi.org/10.1109/TMC.2013.35S94896313

Design and Evaluation of a Traffic Safety System based on Vehicular Networks for the Next Generation of Intelligent Vehicles

La integración de las tecnologías de las telecomunicaciones en el sector del automóvil permitirá a los vehículos intercambiar información mediante Redes Vehiculares, ofreciendo numerosas posibilidades. Esta tesis se centra en la mejora de la seguridad vial y la reducción de la siniestralidad mediante Sistemas Inteligentes de Transporte (ITS). El primer paso consiste en obtener una difusión eficiente de los mensajes de advertencia sobre situaciones potencialmente peligrosas. Hemos desarrollado un marco para simular el intercambio de mensajes entre vehículos, utilizado para proponer esquemas eficientes de difusión. También demostramos que la disposición de las calles tiene gran influencia sobre la eficiencia del proceso. Nuestros algoritmos de difusión son parte de una arquitectura más amplia (e-NOTIFY) capaz de detectar accidentes de tráfico e informar a los servicios de emergencia. El desarrollo y evaluación de un prototipo demostró la viabilidad del sistema y cómo podría ayudar a reducir el número de víctimas en carretera

Automatic Accident Detection: Assistance Through Communication Technologies and Vehicles

[EN] The symbiosis between communication technologies and vehicles offer a priceless opportunity to improve assistance to people injured in traffic accidents, providing information about the incident to reduce the response time of emergency services. Determining more accurately the required human and material resources for each particular accident could significantly reduce the number of victims. This paper presents our novel system prototype especially designed to detect and provide faster assistance for traffic accidents, thereby minimizing the consequences on the passengers¿ health. The proposed system requires each vehicle to be endowed with an On-Board Unit responsible for detecting and reporting accident situations to an external Control Unit that estimates its severity, allocating the necessary resources for the rescue operation. The development of our prototype based on off-the-shelf devices, and its validation at the Applus+ IDIADA Automotive Research Corporation facilities, shows that our system could notably reduce the time needed to alert and deploy the emergency services after an accident takes place.This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01, and by the Diputación General de Aragón, under Grant subvenciones destinadas a la formación y contratación de personal investigador.Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2012). Automatic Accident Detection: Assistance Through Communication Technologies and Vehicles. IEEE Vehicular Technology Magazine. 7(3):90-100. doi:10.1109/MVT.2012.2203877S901007

VEACON: A Vehicular Accident Ontology designed to improve safety on the roads

[EN] Vehicles are nowadays provided with a variety of new sensors capable of gathering information about themselves and from their surroundings. In a near future, these vehicles will also be capable of sharing all the harvested information, with the surrounding environment and among nearby vehicles over smart wireless links. They will also be able to connect with emergency services in case of accidents. Hence, distributed applications based on Vehicular Networks (VNs) will need to agree on a `common understanding¿ of context for interoperability, and, therefore, it is necessary to create a standard structure which enables data interoperability among all the different entities involved in transportation systems. In this paper, we focus on traffic safety applications; specifically, we present the VEhicular ACcident ONtology (VEACON) designed to improve traffic safety. Our ontology combines the information collected when an accident occurs, and the data available in the General Estimates System (GES) accidents database. We assess the reliability of our proposal using both realistic crash tests, held in the facilities of Applus+ IDIADA in Tarragona, Spain, and Vehicular Network simulations, based on the ns-2 simulation tool. Experimental results highlight that both nearby vehicles and infrastructure elements (RSUs) are correctly notified about an accident in just a few seconds, increasing the emergency services notification effectiveness. (c) 2012 Elsevier Ltd. All rights reserved.This work was partially supported by the Ministerio de Ciencia e Innovacion, Spain, under Grant TIN2011-27543-C03-01.Barrachina, J.; Garrido, P.; Fogue, M.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2012). VEACON: A Vehicular Accident Ontology designed to improve safety on the roads. Journal of Network and Computer Applications. 35(6):1891-1900. https://doi.org/10.1016/j.jnca.2012.07.013S1891190035

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

A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms

[EN] The development of communication technologies integrated in vehicles allows creating new protocols and applications to improve assistance in traffic accidents. Combining this technology with intelligent systems will permit to automate most of the decisions needed to generate the appropriate sanitary resource sets, thereby reducing the time from the occurrence of the accident to the stabilization and hospitalization of the injured passengers. However, generating the optimal allocation of sanitary resources is not an easy task, since there are several objectives that are mutually exclusive, such as assistance improvement, cost reduction, and balanced resource usage. In this paper, we propose a novel approach for the sanitary resources allocation in traffic accidents. Our approach is based on the use of multiobjective genetic algorithms, and it is able to generate a list of optimal solutions accounting for the most representative factors. The inputs to our model are: (i) the accident notification, which is obtained through vehicular communication systems, and (ii) the severity estimation for the accident, achieved through data mining. We evaluate our approach under a set of vehicular scenarios, and the results show that a memetic version of the NSGA-II algorithm was the most effective method at locating the optimal resource set, while maintaining enough variability in the solutions to allow applying different resource allocation policies. 2012 Elsevier Ltd. All rights reserved.This work was partially supported by the Ministerio de Ciencia e Innovacion, Spain, under Grant TIN2011-27543-C03-01, and by the Diputacion General de Aragon, under Grant "subvenciones destinadas a la formacion y contratacion de personal investigador".Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms. Expert Systems with Applications. 40(1):323-336. doi:10.1016/j.eswa.2012.07.056S32333640

Back-propagation neural networks and generalized linear mixed models to investigate vehicular flow and weather data relationships with crash severity in urban road segments

The paper deals with the identification of variables and models that can explain why a certain Severity Level (SL) may be expected in the event of a certain type of crash at a specific point of an urban road network. Two official crash records, a weather database, a traffic data source, and information on the characteristics of the investigated urban road segments of Turin (Italy) for the seven years from 2006 to 2012 were used. Examination of the full database of 47,592 crash events, including property damage only crashes, reveals 9,785 injury crashes occurring along road segments only. Of these, 1,621 were found to be associated with a dataset of traffic flows aggregated in 5 minutes for the 35 minutes across each crash event, and to weather data recorded by the official weather station of Turin. Two different approaches, a back-propagation neural network model and a generalized linear mixed model were used. Results show the impact of flow and other variables on the SL that may characterize a crash; differences in the significant variables and performance of the two modelling approaches are also commented on in the manuscript

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture