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Prof. Dr. Blanca Arenas-Ramírez
Universidad Politécnica de Madrid.

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Research Keywords & Expertise

0 Data Analysis
0 Transport Engineering
0 Road safety research
0 Vehicle environmental impact
0 Transport and road safety

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Data Analysis
Road safety research
Transport and road safety

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Short Biography

Blanca Arenas-Ramírez has earned her PhD civil engineer in 2008 at Universidad Politécnica de Madrid (UPM) in Spain. Is the Head of Transport Studies and Vehicle Environmental Impact Unit at Instituto Universitario de Investigación del Automóvil Francisco Aparicio Izquierdo of Universidad Politécnica de Madrid (INSIA-UPM). She is actively involved in the fields of road safety research and the measuring of vehicle environmental impact, and she is Associate Professor of Transportation Engineering, Scientific Safety Research, Mobility and Transport in several Master programs at Universidad Politécnica - Madrid (UPM) and other universities in South America.

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Journal article
Published: 06 August 2021 in International Journal of Environmental Research and Public Health
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Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns.

ACS Style

Paúl Narváez-Villa; Blanca Arenas-Ramírez; José Mira; Francisco Aparicio-Izquierdo. Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain. International Journal of Environmental Research and Public Health 2021, 18, 8327 .

AMA Style

Paúl Narváez-Villa, Blanca Arenas-Ramírez, José Mira, Francisco Aparicio-Izquierdo. Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain. International Journal of Environmental Research and Public Health. 2021; 18 (16):8327.

Chicago/Turabian Style

Paúl Narváez-Villa; Blanca Arenas-Ramírez; José Mira; Francisco Aparicio-Izquierdo. 2021. "Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain." International Journal of Environmental Research and Public Health 18, no. 16: 8327.

Journal article
Published: 19 July 2021 in Sustainability
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Background: Road safety is a significant public health problem because it causes negative consequences on victims and families. The objective was to analyze the most significant changes in traffic crashes in Ecuador during the period from 2000 to 2019. With data obtained from the National Institute of Statistics and Census, we performed the analysis to identify: the number of traffic crashes, the number of victims, and other study variables. Methods: Descriptive and analytical statistics and the contrast of proportions were used to analyze data from 2000 to 2019. Results: According to the ideal joinpoint analysis model, there was a significant decrease in the number of recorded traffic accidents from 2015 to 2019 of −8.54 per year, while the tendency to die increased in females (2.05 per year) and males (3.29 per year). The most common crash was a collision, and the automobile appeared as the most involved vehicle from 2015 to 2019. The hypothesis test contrast is used to determine if statistically significant differences exist between age groups by gender of the driver injured in the period 2017–2018. Conclusions: This study determines the most significant changes in the variables related to traffic crashes, where mortality due to this cause in the last four years has had a growth rate of 1.8% compared to collisions that presented a rate of −31.12%. The contrast of the hypothesis test shows significant differences in the injury level between males and female drivers, depending on the age group.

ACS Style

Fabricio Espinoza-Molina; Christian Ojeda-Romero; Henry Zumba-Paucar; Giovanny Pillajo-Quijia; Blanca Arenas-Ramírez; Francisco Aparicio-Izquierdo. Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019. Sustainability 2021, 13, 8033 .

AMA Style

Fabricio Espinoza-Molina, Christian Ojeda-Romero, Henry Zumba-Paucar, Giovanny Pillajo-Quijia, Blanca Arenas-Ramírez, Francisco Aparicio-Izquierdo. Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019. Sustainability. 2021; 13 (14):8033.

Chicago/Turabian Style

Fabricio Espinoza-Molina; Christian Ojeda-Romero; Henry Zumba-Paucar; Giovanny Pillajo-Quijia; Blanca Arenas-Ramírez; Francisco Aparicio-Izquierdo. 2021. "Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019." Sustainability 13, no. 14: 8033.

Journal article
Published: 02 March 2021 in International Journal of Environmental Research and Public Health
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Background: Although public bodies need to know drivers’ perception of road safety, in Latin America there are no valid and reliable instruments that propose an integral dimensionality. The objective of this study was to design and validate a Road Safety Perception Questionnaire (RSPQ). Methodology: The design included a review of the available evidence and expert knowledge to select the dimensional items for the instrument. A pilot test was carried out to determine possible corrections and adjustments to the questionnaire, after which a Confirmatory Factor Analysis was performed on a stratified sample of 736 Ecuadorian drivers to determine its reliability and construct validity. Results: The results suggest that the RSPQ has a clear factorial structure with high factorial weight items and good internal consistency. The results of the 41-item model grouped into six dimensions (human, vehicle, road infrastructure, regulatory framework and intervention measures, socioeconomic and driving precautions) obtained the best adjustment indexes at the absolute, incremental and parsimonious levels. Conclusions: The preliminary RSPQ evidence can be considered a valid and reliable instrument to assess drivers’ perception of road safety.

ACS Style

Fabricio Espinoza Molina; Blanca Arenas Ramirez; Francisco Aparicio Izquierdo; Diana Zúñiga Ortega. Road Safety Perception Questionnaire (RSPQ) in Latin America: A Development and Validation Study. International Journal of Environmental Research and Public Health 2021, 18, 2433 .

AMA Style

Fabricio Espinoza Molina, Blanca Arenas Ramirez, Francisco Aparicio Izquierdo, Diana Zúñiga Ortega. Road Safety Perception Questionnaire (RSPQ) in Latin America: A Development and Validation Study. International Journal of Environmental Research and Public Health. 2021; 18 (5):2433.

Chicago/Turabian Style

Fabricio Espinoza Molina; Blanca Arenas Ramirez; Francisco Aparicio Izquierdo; Diana Zúñiga Ortega. 2021. "Road Safety Perception Questionnaire (RSPQ) in Latin America: A Development and Validation Study." International Journal of Environmental Research and Public Health 18, no. 5: 2433.

Journal article
Published: 04 February 2021 in International Journal of Environmental Research and Public Health
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An accurate estimation of exposure is essential for road collision rate estimation, which is key when evaluating the impact of road safety measures. The quasi-induced exposure method was developed to estimate relative exposure for different driver groups based on its main hypothesis: the not-at-fault drivers involved in two-vehicle collisions are taken as a random sample of driver populations. Liability assignment is thus crucial in this method to identify not-at-fault drivers, but often no liability labels are given in collision records, so unsupervised analysis tools are required. To date, most researchers consider only driver and speed offences in liability assignment, but an open question is if more information could be added. To this end, in this paper, the visual clustering technique of self-organizing maps (SOM) has been applied to better understand the multivariate structure in the data, to find out the most important variables for driver liability, analyzing their influence, and to identify relevant liability patterns. The results show that alcohol/drug use could be influential on liability and further analysis is required for disability and sudden illness. More information has been used, given that a larger proportion of the data was considered. SOM thus appears as a promising tool for liability assessment.

ACS Style

Almudena Sanjurjo-De-No; Blanca Arenas-Ramírez; José Mira; Francisco Aparicio-Izquierdo. Driver Liability Assessment in Vehicle Collisions in Spain. International Journal of Environmental Research and Public Health 2021, 18, 1475 .

AMA Style

Almudena Sanjurjo-De-No, Blanca Arenas-Ramírez, José Mira, Francisco Aparicio-Izquierdo. Driver Liability Assessment in Vehicle Collisions in Spain. International Journal of Environmental Research and Public Health. 2021; 18 (4):1475.

Chicago/Turabian Style

Almudena Sanjurjo-De-No; Blanca Arenas-Ramírez; José Mira; Francisco Aparicio-Izquierdo. 2021. "Driver Liability Assessment in Vehicle Collisions in Spain." International Journal of Environmental Research and Public Health 18, no. 4: 1475.

Journal article
Published: 01 October 2020 in IEEE Access
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Extracting driver collision patterns by gender and age regarding offences, collision type and injury severity is very useful in road safety, providing a better understanding on behavior of the different driver groups. Self-Organizing Map (SOM) is the tool proposed for distributing and projecting 145,904 drivers according to 8 offence variables on a 2D map. Thus, drivers who are close in the original 8D space (one dimension per offence variable), will remain so in the projected one (2D). Multivariate driving and collision patterns are explored to support the development of future measures to improve road safety. Tests of proportions are used for shedding light on clusters where driver offence is present. Finally, the SOM results were compared for validation with those of the standard K-Means clustering technique. The results show that the characteristics of road crashes and the severity of injuries depend jointly, i.e., in multivariate (pattern) terms, on gender, age, type of collisions and offences. There are relevant multivariate driver behavior differences in both the type of collisions (and therefore their severity) and the type and number of offences with regard to gender and age of the driver. This research unveils different multivariate driver behavior patterns, providing information about their relative importance (proportion), which helps in road policy decision making in terms of development of prevention measures. The results help in decision making through a potentially better allocation of resources as carried out by road safety regulating offices such as the Spanish Traffic General Directorate (Dirección General de Tráfico, DGT).

ACS Style

Almudena Sanjurjo-De-No; Blanca Arenas-Ramirez; Jose Mira; Francisco Aparicio-Izquierdo. Driver Pattern Identification in Road Crashes in Spain. IEEE Access 2020, 8, 182014 -182025.

AMA Style

Almudena Sanjurjo-De-No, Blanca Arenas-Ramirez, Jose Mira, Francisco Aparicio-Izquierdo. Driver Pattern Identification in Road Crashes in Spain. IEEE Access. 2020; 8 (99):182014-182025.

Chicago/Turabian Style

Almudena Sanjurjo-De-No; Blanca Arenas-Ramirez; Jose Mira; Francisco Aparicio-Izquierdo. 2020. "Driver Pattern Identification in Road Crashes in Spain." IEEE Access 8, no. 99: 182014-182025.

Journal article
Published: 12 February 2020 in Sustainability
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The study of road accidents and the adoption of measures to reduce them is one of the most important targets of the Sustainable Development Goals for 2030. To further progress in the improvement of road safety, it is necessary to focus studies on specific groups, such as light trucks and vans. Since 2013 in Spain, there has been an upturn in accidents in these two categories of vehicles and a renewed interest to deepen our understanding of the causes that encourage this behavior. This paper focuses on using machine learning methods to explain driver-injury severity in run-off-roadway and rollover types of accidents. A Random Forest (RF)-classification tree (CART) approach is used to select the relevant categorical variables (driver, vehicle, infrastructure, and environmental factors) to obtain models that classify, explain, and predict the severity of such accidents with good accuracy. A support vector machine and binomial logit models were applied in order to contrast the variable importance ranking and the performance analysis, and the results are convergent with the RF+CART approach (more than 70% accuracy). The resulting models highlight the importance of using safety belts, as well as psychophysical conditions (alcohol, drugs, or sleep deprivation) and injury localization for the two accident types.

ACS Style

Giovanny Pillajo-Quijia; Blanca Arenas-Ramírez; Camino González-Fernández; Francisco Aparicio-Izquierdo. Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods. Sustainability 2020, 12, 1324 .

AMA Style

Giovanny Pillajo-Quijia, Blanca Arenas-Ramírez, Camino González-Fernández, Francisco Aparicio-Izquierdo. Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods. Sustainability. 2020; 12 (4):1324.

Chicago/Turabian Style

Giovanny Pillajo-Quijia; Blanca Arenas-Ramírez; Camino González-Fernández; Francisco Aparicio-Izquierdo. 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods." Sustainability 12, no. 4: 1324.

Review articles
Published: 04 March 2017 in Communications in Statistics - Simulation and Computation
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This article assumes the goal of proposing a simulation-based theoretical model comparison methodology with application to two time series road accident models. The model comparison exercise helps to quantify the main differences and similarities between the two models and comprises of three main stages: (1) simulation of time series through a true model with predefined properties; (2) estimation of the alternative model using the simulated data; (3) sensitivity analysis to quantify the effect of changes in the true model parameters on alternative model parameter estimates through analysis of variance, ANOVA. The proposed methodology is applied to two time series road accident models: UCM (unobserved components model) and DRAG (Demand for Road Use, Accidents and their Severity). Assuming that the real data-generating process is the UCM, new datasets approximating the road accident data are generated, and DRAG models are estimated using the simulated data. Since these two methodologies are usually assumed to be equivalent, in a sense that both models accurately capture the true effects of the regressors, we are specifically addressing the modeling of the stochastic trend, through the alternative model. Stochastic trend is the time-varying component and is one of the crucial factors in time series road accident data. Theoretically, it can be easily modeled through UCM, given its modeling properties. However, properly capturing the effect of a non-stationary component such as stochastic trend in a stationary explanatory model such as DRAG is challenging. After obtaining the parameter estimates of the alternative model (DRAG), the estimates of both true and alternative models are compared and the differences are quantified through experimental design and ANOVA techniques. It is observed that the effects of the explanatory variables used in the UCM simulation are only partially captured by the respective DRAG coefficients. This a priori, could be due to multicollinearity but the results of both simulation of UCM data and estimating of DRAG models reveal that there is no significant static correlation among regressors. Moreover, in fact, using ANOVA, it is determined that this regression coefficient estimation bias is caused by the presence of the stochastic trend present in the simulated data. Thus, the results of the methodological development suggest that the stochastic component present in the data should be treated accordingly through a preliminary, exploratory data analysis.

ACS Style

Bahar Dadashova; Blanca Arenas-Ramírez; José Mira-Mcwilliams; Camino González-Fernández; Francisco Aparicio-Izquierdo. Simulation-based model comparison methodology with application to road accident models. Communications in Statistics - Simulation and Computation 2017, 46, 5340 -5366.

AMA Style

Bahar Dadashova, Blanca Arenas-Ramírez, José Mira-Mcwilliams, Camino González-Fernández, Francisco Aparicio-Izquierdo. Simulation-based model comparison methodology with application to road accident models. Communications in Statistics - Simulation and Computation. 2017; 46 (7):5340-5366.

Chicago/Turabian Style

Bahar Dadashova; Blanca Arenas-Ramírez; José Mira-Mcwilliams; Camino González-Fernández; Francisco Aparicio-Izquierdo. 2017. "Simulation-based model comparison methodology with application to road accident models." Communications in Statistics - Simulation and Computation 46, no. 7: 5340-5366.

Journal article
Published: 01 May 2016 in Accident Analysis & Prevention
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Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000–2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures.

ACS Style

Bahar Dadashova; Blanca Arenas-Ramírez; José Mira-McWilliams; Francisco Aparicio-Izquierdo. Methodological development for selection of significant predictors explaining fatal road accidents. Accident Analysis & Prevention 2016, 90, 82 -94.

AMA Style

Bahar Dadashova, Blanca Arenas-Ramírez, José Mira-McWilliams, Francisco Aparicio-Izquierdo. Methodological development for selection of significant predictors explaining fatal road accidents. Accident Analysis & Prevention. 2016; 90 ():82-94.

Chicago/Turabian Style

Bahar Dadashova; Blanca Arenas-Ramírez; José Mira-McWilliams; Francisco Aparicio-Izquierdo. 2016. "Methodological development for selection of significant predictors explaining fatal road accidents." Accident Analysis & Prevention 90, no. : 82-94.

Journal article
Published: 01 January 2016 in Transportation Research Procedia
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This paper is focused on the effect of road geometry, and other accident causing conditions, on the binary response variable road accident severity. The data is collected from two interurban routes in Spain (Madrid-Irún and Barcelona-Almeria) and covers a 3 year period (2010-2012). Data mining techniques were applied for the treatment and combination of two databases for road accident associated factors and geometry standards respectively. The effect of the influential factors on road accident severity was estimated through a non-parametric statistical methodology, random forests. Several standards of the road geometry design were found to have a significant effect on the road accident severity.

ACS Style

Bahar Dadashova; Blanca Arenas Ramírez; José Mira McWilliams; Francisco Aparicio Izquierdo. The Identification of Patterns of Interurban Road Accident Frequency and Severity Using Road Geometry and Traffic Indicators. Transportation Research Procedia 2016, 14, 4122 -4129.

AMA Style

Bahar Dadashova, Blanca Arenas Ramírez, José Mira McWilliams, Francisco Aparicio Izquierdo. The Identification of Patterns of Interurban Road Accident Frequency and Severity Using Road Geometry and Traffic Indicators. Transportation Research Procedia. 2016; 14 ():4122-4129.

Chicago/Turabian Style

Bahar Dadashova; Blanca Arenas Ramírez; José Mira McWilliams; Francisco Aparicio Izquierdo. 2016. "The Identification of Patterns of Interurban Road Accident Frequency and Severity Using Road Geometry and Traffic Indicators." Transportation Research Procedia 14, no. : 4122-4129.

Book chapter
Published: 02 November 2012 in Lecture Notes in Electrical Engineering
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This paper belongs to “F08: Vehicle Controls on Handling and Stability” topic. It is aimed to assess the potential influence of three active safety systems—the Antilock Brake System (ABS), the Brake Assist System (BAS) and the Electronic Stability Programme (ESP)—in collisions with vans involved, through the analysis of fatal real world accidents occurred in Spain. The methodology follows during this study is based on a retrospective accident analysis of the technical reports of the Spanish Traffic Directorate (DGT). A detailed database has been compiled by the INSIA—UPM accident analysis department, comprising 254 fatal accidents with vans involved, occurred in rural roads during 2009 and 2010. In case of fatal accidents, these reports prepared by the Spanish Traffic Police show a high quality data. This information has been analysed to identify major accidents and causes of accidents (HFF method), in order to identify reference accident scenarios, which take into account the active safety systems proposed. A sample of these accidents, selected from the most representative scenarios, has been re-analyzed and re-evaluated considering the assumed effect of each specific active safety system. Although the performance of active safety systems explains basically their behaviour in test conditions, they are not sufficient to assess their success in each real world situation. Active Safety systems interact with the driver, the vehicle and the environment. A full forecast of their potential is only possible by modelling the driver-vehicle-system-environment. This methodology has already been applied by authors to evaluate the effectiveness of active safety systems in case of pedestrian accidents. This study continues the same research line based on real world accidents analysis, applied to accidents involving vans. The findings show that a number of the collisions could have been avoided by implementing these systems. Even though comprising a small number of cases, it is an invaluable resource for monitoring real world performance of active safety systems.

ACS Style

Francisco Javier Páez Ayuso; Arturo Furones Crespo; Francisco Aparicio Izquierdo; Blanca Arenas Ramirez. Evaluation of the Effectiveness of Active Safety Systems in Vans with Respect to Real World Accident Analysis. Lecture Notes in Electrical Engineering 2012, 197, 609 -617.

AMA Style

Francisco Javier Páez Ayuso, Arturo Furones Crespo, Francisco Aparicio Izquierdo, Blanca Arenas Ramirez. Evaluation of the Effectiveness of Active Safety Systems in Vans with Respect to Real World Accident Analysis. Lecture Notes in Electrical Engineering. 2012; 197 ():609-617.

Chicago/Turabian Style

Francisco Javier Páez Ayuso; Arturo Furones Crespo; Francisco Aparicio Izquierdo; Blanca Arenas Ramirez. 2012. "Evaluation of the Effectiveness of Active Safety Systems in Vans with Respect to Real World Accident Analysis." Lecture Notes in Electrical Engineering 197, no. : 609-617.

Review
Published: 31 May 2011 in Accident Analysis & Prevention
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In this work we have used ARIMA time series models to analyse the contribution of the penalty point system, the most important legislative measure for driving licences, in reducing the number of fatalities over 24h on the roads in Spain during the study period (January 1995 to June 2009). In addition, because of this long period of analysis, other control variables were introduced to model the enactment of the Reform of the Penal Code in December 2007, together with other more specific effects needed to fit the model correctly. The ARIMA intervention models methodology combines the basic features of specific times series models: it controls the trend and seasonal variation in data that is present when modelling the structure through autoregressive and moving average parameters and allows for inserting step or impulse input variables for checking and evaluating the effects of deterministic measures, such as legislative changes which are the object of study in this work. This paper analyses the surveillance and control measures introduced in the periods before and after the implementation of the penalty point system and helps to partly explain its apparent endurance over time. The results show that the introduction of the penalty point system in Spain had a very positive effect in reducing the number of fatalities (over 24h) on the road, and that this effect has endured up to the present time. This success may be due to the continuing increase in surveillance measures and fines as well the significantly growing interest shown by the news media in road safety since the measures were introduced. All this has led to positive changes in driver behaviour. It is, therefore, a combination of three factors: the penalty point system, the gradual stepping up of surveillance measures and sanctions, and the publicity given to road safety issues in the mass media would appear to be the key to success. The absence of any of these three factors would have predictably led to a far less positive evolution of the accident rate on Spanish roads.

ACS Style

F. Aparicio Izquierdo; Blanca Arenas Ramirez; J.M. Mira McWilliams; J. Páez Ayuso. The endurance of the effects of the penalty point system in Spain three years after. Main influencing factors. Accident Analysis & Prevention 2011, 43, 911 -922.

AMA Style

F. Aparicio Izquierdo, Blanca Arenas Ramirez, J.M. Mira McWilliams, J. Páez Ayuso. The endurance of the effects of the penalty point system in Spain three years after. Main influencing factors. Accident Analysis & Prevention. 2011; 43 (3):911-922.

Chicago/Turabian Style

F. Aparicio Izquierdo; Blanca Arenas Ramirez; J.M. Mira McWilliams; J. Páez Ayuso. 2011. "The endurance of the effects of the penalty point system in Spain three years after. Main influencing factors." Accident Analysis & Prevention 43, no. 3: 911-922.

Journal article
Published: 01 November 2010 in Accident Analysis & Prevention
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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.

ACS Style

Álvaro Gómez Méndez; Francisco Aparicio Izquierdo; Blanca Arenas Ramirez. Evolution of the crashworthiness and aggressivity of the Spanish car fleet. Accident Analysis & Prevention 2010, 42, 1621 -1631.

AMA Style

Álvaro Gómez Méndez, Francisco Aparicio Izquierdo, Blanca Arenas Ramirez. Evolution of the crashworthiness and aggressivity of the Spanish car fleet. Accident Analysis & Prevention. 2010; 42 (6):1621-1631.

Chicago/Turabian Style

Álvaro Gómez Méndez; Francisco Aparicio Izquierdo; Blanca Arenas Ramirez. 2010. "Evolution of the crashworthiness and aggressivity of the Spanish car fleet." Accident Analysis & Prevention 42, no. 6: 1621-1631.

Proceedings article
Published: 15 April 2010 in URBAN TRANSPORT 2010
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ACS Style

J. Lumbreras; D. Ballarín; J. M. Lopez; R. Villimar; B. Arenas; F. Aparicio; E. Rodriguez. A model to estimate road transport emissions from the entire life cycle. URBAN TRANSPORT 2010 2010, 111, 171 -180.

AMA Style

J. Lumbreras, D. Ballarín, J. M. Lopez, R. Villimar, B. Arenas, F. Aparicio, E. Rodriguez. A model to estimate road transport emissions from the entire life cycle. URBAN TRANSPORT 2010. 2010; 111 ():171-180.

Chicago/Turabian Style

J. Lumbreras; D. Ballarín; J. M. Lopez; R. Villimar; B. Arenas; F. Aparicio; E. Rodriguez. 2010. "A model to estimate road transport emissions from the entire life cycle." URBAN TRANSPORT 2010 111, no. : 171-180.

Journal article
Published: 03 June 2009 in Securitas Vialis
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En este trabajo se presentan los principales resultados obtenidos de los modelos de accidentes con heridos y accidentes mortales ocurridos en carreteras interurbanas españolas, en el período 1990–2004, desarrollados en base a la metodología de modelos econométricos DRAG (Demand Demande Routière, Accidents et Gravité). Las 19 variables utilizadas para el ajuste de los modelos DRAG–España (I-DE 1), son de distinta naturaleza y corresponden a los factores de exposición, infraestructura, climatología, conductores, económicos, parque de vehículos, vigilancia, velocidad y medidas legislativas. En este trabajo se han incorporado nuevas variables, no tratadas con anterioridad en la familia de modelos DRAG, como las características tecnológicas (ABS) y de antigüedad del parque, así como variables de vigilancia y control de la seguridad del tráfico: el número de controles aleatorios de alcoholemia, el número de agentes de la agrupación de tráfico de la guardia civil, y el número de suspensiones y privaciones del permiso de conducir.

ACS Style

F. Aparicio; B. Arenas; E. Bernardos; A. Gómez. El modelo DRAG–España (I-DE 1): Análisis de los principales factores de influencia en el número de accidentes en las carreteras Españolas. Securitas Vialis 2009, 1, 59 -64.

AMA Style

F. Aparicio, B. Arenas, E. Bernardos, A. Gómez. El modelo DRAG–España (I-DE 1): Análisis de los principales factores de influencia en el número de accidentes en las carreteras Españolas. Securitas Vialis. 2009; 1 (2):59-64.

Chicago/Turabian Style

F. Aparicio; B. Arenas; E. Bernardos; A. Gómez. 2009. "El modelo DRAG–España (I-DE 1): Análisis de los principales factores de influencia en el número de accidentes en las carreteras Españolas." Securitas Vialis 1, no. 2: 59-64.

Journal article
Published: 01 January 2009 in Accident Analysis & Prevention
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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

ACS Style

Blanca Arenas Ramirez; F. Aparicio Izquierdo; C. González Fernández; A. Gómez Méndez. The influence of heavy goods vehicle traffic on accidents on different types of Spanish interurban roads. Accident Analysis & Prevention 2009, 41, 15 -24.

AMA Style

Blanca Arenas Ramirez, F. Aparicio Izquierdo, C. González Fernández, A. Gómez Méndez. The influence of heavy goods vehicle traffic on accidents on different types of Spanish interurban roads. Accident Analysis & Prevention. 2009; 41 (1):15-24.

Chicago/Turabian Style

Blanca Arenas Ramirez; F. Aparicio Izquierdo; C. González Fernández; A. Gómez Méndez. 2009. "The influence of heavy goods vehicle traffic on accidents on different types of Spanish interurban roads." Accident Analysis & Prevention 41, no. 1: 15-24.