Developing speed‐related safety performance indicators from floating car data
Abstract 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 insuffi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-03-01
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| Series: | IET Intelligent Transport Systems |
| Online Access: | https://doi.org/10.1049/itr2.12281 |
| _version_ | 1797861419196088320 |
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| author | Jiří Ambros Davide Shingo Usami Veronika Valentová |
| author_facet | Jiří Ambros Davide Shingo Usami Veronika Valentová |
| author_sort | Jiří Ambros |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T22:02:10Z |
| format | Article |
| id | doaj.art-675d950d8de04fc0820495b43c374af1 |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-09T22:02:10Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-675d950d8de04fc0820495b43c374af12023-03-23T14:21:31ZengWileyIET Intelligent Transport Systems1751-956X1751-95782023-03-0117355756510.1049/itr2.12281Developing speed‐related safety performance indicators from floating car dataJiří Ambros0Davide Shingo Usami1Veronika Valentová2CDV – Transport Research Centre Líšeňská 33a Brno Czech RepublicCentre for Transport and Logistics Sapienza University of Rome Rome ItalyCDV – Transport Research Centre Líšeňská 33a Brno Czech RepublicAbstract 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.https://doi.org/10.1049/itr2.12281 |
| spellingShingle | Jiří Ambros Davide Shingo Usami Veronika Valentová Developing speed‐related safety performance indicators from floating car data IET Intelligent Transport Systems |
| title | Developing speed‐related safety performance indicators from floating car data |
| title_full | Developing speed‐related safety performance indicators from floating car data |
| title_fullStr | Developing speed‐related safety performance indicators from floating car data |
| title_full_unstemmed | Developing speed‐related safety performance indicators from floating car data |
| title_short | Developing speed‐related safety performance indicators from floating car data |
| title_sort | developing speed related safety performance indicators from floating car data |
| url | https://doi.org/10.1049/itr2.12281 |
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