Impact of infrastructure and local environment on road unsafety

Flahaut, Benoît

doi:10.1016/j.aap.2003.12.003

Cited by 67 publications

(30 citation statements)

References 40 publications

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“…Most papers highlight the benefits of providing better quality roads, a determinant captured by road type (motorways, conventional roads, urban roads, etc. ), the number of traffic lanes, the pavement, and the median shoulder and lane width (Abdel-Aty and Essam-Radwan 2000;Flahaut, 2004;Noland, 2003;Noland and Oh, 2004;Anastasopoulos, Tarko and Mannering, 2008;Park et al 2012). Others, adhering to the Peltzman (1975) compensating effect, claim that these enhanced properties have just the opposite impact and increase the level of risk taken by drivers in the light of the better infrastructure endowment.…”

Section: Infrastructure Characteristics and Traffic Conditionsmentioning

confidence: 99%

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The road against fatalities: Infrastructure spending vs. regulation??

Albalate

Fernández

Yarygina

2013

Accident Analysis & Prevention

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe road safety literature is typified by a high degree of compartmentalization between studies that focus on infrastructure and traffic conditions and those devoted to the evaluation of public policies and regulations. As a result, few studies adopt a unified empirical framework in their attempts at evaluating the road safety performance of public interventions, thus limiting our understanding of successful strategies in this regard. This paper considers both types of determinants in an analysis of a European country that has enjoyed considerable success in reducing road fatalities. After constructing a panel data set with road safety outcomes for all Spanish provinces between 1990 and 2009, we evaluate the role of the technical characteristics of infrastructure and recent infrastructure spending together with the main regulatory changes introduced. Our results show the importance of considering both types of determinants in a unified framework. Moreover, we highlight the importance of spending in maintenance given its effectiveness in reducing fatalities and casualties in the current economic context of austerity that is having such a marked impact on investment efforts in Spain.

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Section: Infrastructure Characteristics and Traffic Conditionsmentioning

confidence: 99%

“…RV: Seat belt laws IV: Lane miles, number of different road types, number of lanes, lane widths Flahaut (2004) Infrastructure plays a substantial role in the co-occurrence of road accidents. All infrastructure variables are found to be statistically significant.…”

Section: Studymentioning

confidence: 99%

The road against fatalities: Infrastructure spending vs. regulation??

Albalate

Fernández

Yarygina

2013

Accident Analysis & Prevention

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“…However, the analysis focused on network autocorrelations at a global level within an entire dataset by using Moran's I and the associated z-score tests. Flahaut [12] introduced the use of the local indices of spatial autocorrelation (LISA) to examine the crash patterns of road networks in a Belgian province. This work explained the advantages of hotspots and further developed logistic regression models to explain traffic crash hotspots with road characteristics and local environmental conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Vehicular Crash High Risk Locations along Highways via Spatial Autocorrelation Indices and Kernel Density Estimation

Abdulhafedh¹

2017

WJET

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Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran's I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.

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“…A similar study was carried out by Moons et al (2009) where the structure of the underlying road network is taken into account by applying Moran's I to identify crash hot zones. In another study by Flahaut (2004), it was indicated that spatial autocorrelation should be integrated in the modeling process if spatial data are being studied. He concluded that spatial models in comparison to non-spatial models, do not overestimate the significance of explanatory variables; thus, spatial variation should be considered to analyze spatial data.…”

Section: Introductionmentioning

confidence: 99%