<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles.</p>
<p>This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.</p>
</abstract>

In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles.
This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.

A Model for Identifying Road Risk Class

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Model for Identifying Road Risk Class"}

In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is...

A Model for Identifying Road Risk Class

Pubblicato online: 15 apr 2023

Pagine: 167 - 179

DOI: https://doi.org/10.2478/ttj-2023-0015

Parole chiave
traffic, risk mapping, Surrogate Safety Measures, individual risk class, PCA, WIM

© 2023 Artur Ryguła et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Model for Identifying Road Risk Class

Pubblicato online: 15 apr 2023

Pagine: 167 - 179

DOI: https://doi.org/10.2478/ttj-2023-0015

Parole chiavetraffic, risk mapping, Surrogate Safety Measures, individual risk class, PCA, WIM

© 2023 Artur Ryguła et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
traffic, risk mapping, Surrogate Safety Measures, individual risk class, PCA, WIM