Which curves are dangerous? A network-wide analysis of traffic crash and infrastructure data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44994575%3A_____%2F19%3AN0000023" target="_blank" >RIV/44994575:_____/19:N0000023 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S096585641830819X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S096585641830819X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.tra.2019.01.001" target="_blank" >10.1016/j.tra.2019.01.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Which curves are dangerous? A network-wide analysis of traffic crash and infrastructure data

Popis výsledku v původním jazyce

We conducted spatial analyses of traffic crashes, which took place in Czechia over 2010–2016, with respect to the road geometry data. The aim of the work was to identify hazardous road sub-segments where higher than expected numbers of traffic crashes occur. The entire Czech road network (58,200 km) was segmented at intersections into 39,074 between-intersection segments of varying lengths. Each road segment was further automatically sectioned, according to its horizontal alignment, into geometry-homogenous units (horizontal curves and tangents). Overall, 257,101 curves, defined as curved sections with radii below 2100 m, and 136,388 tangents, were identified. Subsequently, traffic crashes were joined to the respective geometrical units to determine their hazardousness. The degree of hazardousness was determined relatively, on a segment-by-segment basis, in order to eliminate the lack of precise traffic exposure data. In addition, the exact binomial test and Bayesian inference were used to identify the most hazardous horizontal curves. It was found that, in general, the curves with a higher crash risk have lower radii than the other curves. We identified the geographical locations of all curves with a high crash hazard. We also ranked the curves according to the crash hazard. Approximately ten percent of road segments contained at least one hazardous horizontal curve. 6943 significantly hazardous curves were identified by the use of the exact binomial test. The Bayesian inference reduced this number to 1395 (0.31% of the entire road network) and ranked them according to the Bayes factor. The most hazardous curve was 45 m long and contained 8.7 traffic crashes per year. Its hazard rate accounted for 37.4. This state-wide analysis of primary data was conducted over an extremely short time (up to 3 days) as the result of an application of an efficient algorithm for automatic road curvature determination.

Název v anglickém jazyce

Which curves are dangerous? A network-wide analysis of traffic crash and infrastructure data

Popis výsledku anglicky

We conducted spatial analyses of traffic crashes, which took place in Czechia over 2010–2016, with respect to the road geometry data. The aim of the work was to identify hazardous road sub-segments where higher than expected numbers of traffic crashes occur. The entire Czech road network (58,200 km) was segmented at intersections into 39,074 between-intersection segments of varying lengths. Each road segment was further automatically sectioned, according to its horizontal alignment, into geometry-homogenous units (horizontal curves and tangents). Overall, 257,101 curves, defined as curved sections with radii below 2100 m, and 136,388 tangents, were identified. Subsequently, traffic crashes were joined to the respective geometrical units to determine their hazardousness. The degree of hazardousness was determined relatively, on a segment-by-segment basis, in order to eliminate the lack of precise traffic exposure data. In addition, the exact binomial test and Bayesian inference were used to identify the most hazardous horizontal curves. It was found that, in general, the curves with a higher crash risk have lower radii than the other curves. We identified the geographical locations of all curves with a high crash hazard. We also ranked the curves according to the crash hazard. Approximately ten percent of road segments contained at least one hazardous horizontal curve. 6943 significantly hazardous curves were identified by the use of the exact binomial test. The Bayesian inference reduced this number to 1395 (0.31% of the entire road network) and ranked them according to the Bayes factor. The most hazardous curve was 45 m long and contained 8.7 traffic crashes per year. Its hazard rate accounted for 37.4. This state-wide analysis of primary data was conducted over an extremely short time (up to 3 days) as the result of an application of an efficient algorithm for automatic road curvature determination.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10700 - Other natural sciences

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Transportation Research Part A: Policy and Practice

ISSN

0965-8564

e-ISSN

—

Svazek periodika

120

Číslo periodika v rámci svazku

2019

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

252-260

Kód UT WoS článku

999

EID výsledku v databázi Scopus

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