Alvaro Gómez Méndez

Universidad Politécnica de Madrid, Madrid, Madrid, Spain

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Publications (6)4.94 Total impact

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    ABSTRACT: This paper presents the results of applying DRAG methodology to the identification of the main factors of influence on the number of injury and fatal accidents occurring on Spain’s interurban network. Nineteen independent variables have been included in the model grouped together under ten categories: exposure, infrastructure, weather, drivers, economic variables, vehicle stock, surveillance, speed and legislative measures. Highly interesting conclusions can be reached from the results on the basis of the different effects of a single variable on each of the accident types according to severity. The greatest influence revealed by the results is exposure, which together with inexperienced drivers, speed and an ageing vehicle stock, have a negative effect, while the increased surveillance on roads, the improvement in the technological features of vehicles and the proportion of high capacity networks have a positive effect, since the results obtained show a significant drop in accidents.
    Research in Transportation Economics 01/2011;
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    ABSTRACT: This paper investigates the relationship between a passenger car's year of registration and its crashworthiness and aggressivity in real-world crashes. Crashworthiness is defined as the ability of a car to protect its own occupants, and has been evaluated in single and two-car crashes. Aggressivity is defined as the ability to protect users travelling in other vehicles, and has been evaluated only in two-car crashes. The dependent variable is defined as the proportion of injured drivers who are killed or seriously injured; following previous research, we refer to this magnitude as injury severity. A decrease in the injury severity of a driver is interpreted as an improvement in the crashworthiness of their car; similarly, a decrease in the injury severity of the opponent driver is regarded as an improvement in aggressivity. Data have been extracted from the Spanish Road Accident Database, which contains information on every accident registered by the police in which at least one person was injured. Two types of regression models have been used: logistic regression models in single-car crashes, and generalised estimating equations (GEE) models in two-car crashes. GEE allow to take account of the correlation between the injury severities of drivers involved in the same crash. The independent variables considered have been: year of registration of the subject car (crashworthiness component), year of registration of the opponent car (aggressivity component), and several factors related to road, driver and environment. Our models confirm that crashworthiness has largely improved in two-car crashes: when crashing into the average opponent car, drivers of cars registered before 1985 have a significantly higher probability of being killed or seriously injured than drivers of cars registered in 2000-2005 (odds ratio: 1.80; 95% confidence interval: 1.61; 2.01). In single-car crashes, the improvement in crashworthiness is very slight (odds ratio: 1.04; 95% confidence interval: 0.93; 1.16). On the other hand, we have also found a significant worsening in aggressivity in two-car crashes: the driver of the average car has a significantly lower probability of being killed or seriously injured when crashing into a car registered before 1985, than when crashing into a car registered in 2000-2005 (odds ratio: 0.52; 95% confidence interval: 0.45; 0.60). Our results are consistent with a large amount of previous research that has reported significant improvements in the protection of car occupants. They also add to some recent studies that have found a worsening in the aggressivity of modern cars. This trend may be reflecting the impact of differences in masses and travel speeds, as well as the influence of consumer choices. The precise reasons have to be investigated. Also, the causes have to be found for so large a discrepancy between crashworthiness in single and two-car crashes.
    Accident; analysis and prevention 11/2010; 42(6):1621-31. · 1.65 Impact Factor
  • Alvaro Gómez Méndez, Francisco Aparicio Izquierdo
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    ABSTRACT: The quasi-induced exposure method is widely used to estimate exposure and risks of different groups of drivers and vehicles. Essentially, this method assumes that non-at-fault or passive parties in two-vehicle collisions represent a random sample of the populations on the road. Most previous works have used the whole sample of collisions to estimate exposure. There has been some concern about possible biases in quasi-induced estimates. In this paper, we argue that (1) biases are mainly due to differences in accident avoidance abilities, speeds and injury risks, and (2) because the influence of these three factors on the probability of being non-at-fault is not the same for every crash type, differences may arise among non-at-fault populations, in which case some crash types would provide a more accurate estimate of exposure than others. We explore the direction of biases due to speed, accident avoidance ability and injury risk in four accident types: accidents between vehicles travelling on different lanes in two-way, two-lane undivided roads; accidents between vehicles travelling on different lanes on multilane roads; intersection accidents; and accidents between vehicles travelling on the same lane. Our analysis shows that more research would be needed concerning the effect of speed on head-on crashes on undivided roads, and crashes on multilane roads.
    Accident; analysis and prevention 03/2010; 42(2):582-8. · 1.65 Impact Factor
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    ABSTRACT: This paper illustrates a methodology developed to analyze the influence of traffic conditions, i.e. volume and composition on accidents on different types of interurban roads in Spain, by applying negative binomial models. The annual average daily traffic was identified as the most important variable, followed by the percentage of heavy goods vehicles, and different covariate patterns were found for each road type. The analysis of hypothetical scenarios of the reduction of heavy goods vehicles in two of the most representative freight transportation corridors, combined with hypotheses of total daily traffic mean intensity variation, produced by the existence or absence of induced traffic gives rise to several scenarios. In all cases a reduction in the total number of accidents would occur as a result of the drop in the number of heavy goods transport vehicles, However the higher traffic intensity, resulting of the induction of other vehicular traffic, reduces the effects on the number of accidents on single carriageway road segments compared with high capacity roads, due to the increase in exposure. This type of analysis provides objective elements for evaluating policies that encourage modal shifts and road safety enhancements.
    Accident; analysis and prevention 02/2009; 41(1):15-24. · 1.65 Impact Factor
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    ABSTRACT: Application of the DRAG methodology to the analysis of road safety in Spain and evaluation of the main factors of influence.
    01/2008;
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    ABSTRACT: Durante décadas, los investigadores han intentado identificar los factores más importantes que afectan a la seguridad vial, tratando además, de medir el grado de tal efecto. Este interés se ha acentuado de forma considerable en los últimos años, lo que se ha visto reflejando en el planteamiento de las nuevas políticas estratégicas, con la fijación de objetivos numéricos. Para poder analizar de forma rigurosa el efecto de los factores que influyen sobre la seguridad vial, y poder utilizar dichos resultados para establecer objetivos realistas en los planes estratégicos marcados en las políticas de seguridad vial, es imprescindible disponer de herramientas econométricas adecuadas. En este sentido, cabe destacar la metodología DRAG, la cual permite solucionar los problemas más importantes asociados al desarrollo de modelos en series temporales, tales como la heterocedasticidad, la autocorrelación o la forma funcional, a la vez que presentan una estructura multicapa que analiza las tres dimensiones principales de la seguridad vial (exposición, frecuencia de accidentes y severidad). Cada una de estas dimensiones es objeto de una ecuación propia que analiza los principales factores de influencia: variables económicas, infraestructura, climatología, conductores, características del parque, vigilancia, velocidad y medidas legislativas en seguridad, entre otros. Dado el gran éxito obtenido en la aplicación de esta metodología en diversos países y regiones, el INSIA ha adaptado la metodología DRAG a la situación de la seguridad vial española, realizando el modelo DRAG-España. Por su mayor interés, este trabajo presenta únicamente los resultados de los modelos desarrollados para accidentes, los cuales estudian los accidentes con heridos y los accidentes mortales de forma diferenciada, lo que ha permitido analizar la diferente influencia de una misma variable sobre estos distintos tipos de accidentes.