Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads

Pardillo-Mayora, José María; Dominguez-Lira, Carlos Arturo; Jurado-Piña, Rafael

doi:10.1016/j.aap.2010.06.012

Cited by 35 publications

(28 citation statements)

References 11 publications

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“…Therefore, this study agrees with previous research (Sohn, 1999;Karlaftis and Tarko, 1998;Ng et al, 2002;Wong et al, 2004;Depaire et al, 2008;Pardillo-Mayora et al, 2010) and shows that when analysing traffic accidents, it is worthwhile to segment the accident records to increase data homogeneity before applying other analysis techniques.…”

Section: Conclusion and Recomendationssupporting

confidence: 80%

“…K-means). Both approaches have been used in road safety (Sohn, 1999;Karlaftis and Tarko, 1998;Ng et al, 2002;Wong et al, 2004;Pardillo-Mayora et al, 2010), although the statistical properties of these methods are relatively unknown (Fraley and Raftery, 2002).…”

Section: Latent Class Clustering Analysismentioning

confidence: 99%

“…Recently, LCC have also been used by Park and Lord (2009) and Park et al (2010) in order to segment a database and analyses vehicle crash data. Finally, Pardillo-Mayora et al (2010) used CA to analyse data from run off road accidents to calibrate a roadside hazardous index for twolane roads in Spain. The four characteristics considered for the index were: roadside slope, nontraversable obstacles, safety barrier installation, and alignment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Oña

López

Mujalli

et al. 2013

Accident Analysis & Prevention

226

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One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3,229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BN) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analyzed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.

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Section: Conclusion and Recomendationssupporting

confidence: 80%

Section: Latent Class Clustering Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Oña

López

Mujalli

et al. 2013

Accident Analysis & Prevention

226

View full text Add to dashboard Cite

show abstract

“…These reports found that there were differences compared with the real situation. It was found that 95% of the reported cases had lesions as follows: 70% serious injuries, 20% minor injuries, and 5% very slight injuries [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Risk Indicators and Road Accident Analysis for the Period 2012–2016

Cioca

Ivaşcu

2017

Sustainability

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Road accidents are a major societal issue for every country. The purpose of this paper is to assess the number of traffic and road accidents depending on a series of variables (collision mode, road configuration, conditions of occurrence, road category, type of vehicle involved, personal factors, and length of time of the driving license) in Romania from 2012-2016. The analysis of the road accident trend identifies the causes of accidents, road safety performance indicators, and risk indicators. Having these identified data, a framework is proposed for improving the road safety system and reducing accidents. The Romanian Police, the National Institute of Statistics (NIS) in Romania, and the European Commission provided the data used for this analysis. The data obtained from these databases are analysed and evaluated according to a series of variables. This paper will outline an informative image of road accidents and establish a framework for reducing their effects in road transport. As a result of the analysis, we have seen that the combination of vehicles and personal factors influences the number of traffic and road accidents.

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“…Based on fact mentioned in previous studies [27][28], a cluster model with 4 clusters is selected. Further, k-modes technique is applied on the data and the four cluster obtained.…”

Section: A Cluster Analysismentioning

confidence: 99%

A Conjoint Analysis of Road Accident Data using K-modes Clustering and Bayesian Networks (Road Accident Analysis using clustering and classification)

Kumar¹,

Semwal²,

Solanki³

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-Road and oraffic accidenos are one of ohe imporoano concerns in ooday's world. Every counory receives a huge damage from road accidenos in oerms of public healoh and properoy loss. Therefore, road accideno analysis plays an imporoano role in public healoh domain. Road accideno analysis is performed in order oo idenoify ohe associaoed facoors ohao are responsible for road accidenos. Knowledge of ohese facoors would be very useful oo undersoand ohe circumsoances of road accidenos and can be used oo avoid ohe road accidenos. One of ohe problems in accideno analysis is ohao moso of ohe road accideno daoa is of biased naoure. For example, ohe crioical road accidenos are very few in comparison oo sligho/minor injury accidenos. Various soudies has focused ohao clusoering prior oo analysis can increase ohe efficiency and accuracy of classificaoion. The mooive of ohis soudy is oo perform a conjoino analysis on road accideno daoa, oo invesoigaoe improvemeno in ohe performance of classificaoion of unbiased daoa afoer clusoering.

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Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads

Cited by 35 publications

References 11 publications

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Risk Indicators and Road Accident Analysis for the Period 2012–2016

A Conjoint Analysis of Road Accident Data using K-modes Clustering and Bayesian Networks (Road Accident Analysis using clustering and classification)

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