Developing speed-related safety performance indicators from floating car data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44994575%3A_____%2F23%3A10003644" target="_blank" >RIV/44994575:_____/23:10003644 - isvavai.cz</a>

Výsledek na webu

<a href="https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/itr2.12281" target="_blank" >https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/itr2.12281</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1049/itr2.12281" target="_blank" >10.1049/itr2.12281</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Developing speed-related safety performance indicators from floating car data

Popis výsledku v původním jazyce

In the road traffic safety domain there is a need for using proactive (non-crash-based) indicators, known as safety performance indicators (SPIs). Traffic speed based on big data (floating car data [FCD]) could help develop network-wide SPIs, but related knowledge and experience are insufficient so far. The authors attempted to fill this gap by using nationwide Italian FCD to develop speed-related SPIs and validating their relationship to crashes to see their potential explanatory value. The authors calculated the coefficient of variance (CV), congestion index (CI), and the number of incidents as candidate SPIs. For validation, the authors used linear correlation, crash frequency model, and ranking consistency. Incidents turned out to be the best SPI, especially for motorways.

Název v anglickém jazyce

Developing speed-related safety performance indicators from floating car data

Popis výsledku anglicky

In the road traffic safety domain there is a need for using proactive (non-crash-based) indicators, known as safety performance indicators (SPIs). Traffic speed based on big data (floating car data [FCD]) could help develop network-wide SPIs, but related knowledge and experience are insufficient so far. The authors attempted to fill this gap by using nationwide Italian FCD to develop speed-related SPIs and validating their relationship to crashes to see their potential explanatory value. The authors calculated the coefficient of variance (CV), congestion index (CI), and the number of incidents as candidate SPIs. For validation, the authors used linear correlation, crash frequency model, and ranking consistency. Incidents turned out to be the best SPI, especially for motorways.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20104 - Transport engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IET INTELLIGENT TRANSPORT SYSTEMS

ISSN

1751-9578

e-ISSN

1751-9578

Svazek periodika

17

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

553-561

Kód UT WoS článku

000863674000001

EID výsledku v databázi Scopus

2-s2.0-85139126300