Developing Updatable Crash Prediction Model for Network Screening: A Case Study of Czech Two-Lane Rural Road Segments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44994575%3A_____%2F16%3AN0000063" target="_blank" >RIV/44994575:_____/16:N0000063 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Developing Updatable Crash Prediction Model for Network Screening: A Case Study of Czech Two-Lane Rural Road Segments

Popis výsledku v původním jazyce

(Presented at 95th Annual Meeting of the Transportation Research Board, Washington, D.C.) The case study focuses on application of crash prediction models in network screening. The two main questions were (1) What variables should be involved in the model? and (2) How long should the modeled time period be? Answers to these questions should provide guidelines to developing ʻupdatableʼ crash prediction model, i.e. a model which is both reliable and simple, so that its updating for periodical network screening is not highly demanding. To this end approximately 1,000 km (600 mi) of two-lane rural road network data from South Moravia (Czech Republic) was used. Based on 8 years of annual crash frequencies, together with exposure and geometrical variables, several variants of prediction models were developed. In order to study their quality, a series of consistency tests was applied, relative to comparison of models themselves, as well as their diagnostic performance. As a result simple crash prediction models (including traffic volume, segment length and curvature change rate) were found as sufficient for network screening. Supposing that length and curvature are not likely to change often, only traffic volume data need to be periodically updated. Based on consistency analyses this time period should be 4 years. Under these conditions, models are currently being applied in the studied region; further planned activities include extensions to intersections and also to other Czech regions.

Název v anglickém jazyce

Developing Updatable Crash Prediction Model for Network Screening: A Case Study of Czech Two-Lane Rural Road Segments

Popis výsledku anglicky

(Presented at 95th Annual Meeting of the Transportation Research Board, Washington, D.C.) The case study focuses on application of crash prediction models in network screening. The two main questions were (1) What variables should be involved in the model? and (2) How long should the modeled time period be? Answers to these questions should provide guidelines to developing ʻupdatableʼ crash prediction model, i.e. a model which is both reliable and simple, so that its updating for periodical network screening is not highly demanding. To this end approximately 1,000 km (600 mi) of two-lane rural road network data from South Moravia (Czech Republic) was used. Based on 8 years of annual crash frequencies, together with exposure and geometrical variables, several variants of prediction models were developed. In order to study their quality, a series of consistency tests was applied, relative to comparison of models themselves, as well as their diagnostic performance. As a result simple crash prediction models (including traffic volume, segment length and curvature change rate) were found as sufficient for network screening. Supposing that length and curvature are not likely to change often, only traffic volume data need to be periodically updated. Based on consistency analyses this time period should be 4 years. Under these conditions, models are currently being applied in the studied region; further planned activities include extensions to intersections and also to other Czech regions.

Druh

O - Ostatní výsledky

CEP obor

JO - Pozemní dopravní systémy a zařízení

OECD FORD obor

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Projekt

<a href="/cs/project/ED2.1.00%2F03.0064" target="_blank" >ED2.1.00/03.0064: Dopravní VaV centrum</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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