Development of Motorway Horizontal Alignment Databases for Accurate Accident Prediction Models

The safe and efficient operation of highways minimizes the environmental impact, reduces accidents, and promotes the reliability of the transportation infrastructure, all in support of sustainable transportation. The horizontal alignment of highways holds particular importance as it directly impacts driver behavior, vehicle stability, and overall road safety. In many cases, highway inventory data held by infrastructure operators may contain inaccurate or outdated information. The accuracy of the variables used in crash prediction models eliminates possible bias in the variable estimators. This research proposes a methodology to obtain accurate horizontal geometric features from digital imagery based on the analysis of the planimetry, feature geolocation and centerline azimuth sequence. The reliability of the method is verified by means of numerical and statistical procedures. This methodology is applied to 150 km of motorway segments in Portugal. Although it is found that the geometric characteristics of most of the inventory segments closely matched the extracted alignments, very significant differences are found in some sections. The results of the proposed procedure are illustrated with several examples. Finally, the propagation of error in the determination of the geometric design independent variables in the fitting of the statistical models is discussed based on the results.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2024-08-25
DOI
https://doi.org/10.3390/su16177296
Citations
1
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
0.36

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of Motorway Horizontal Alignment Databases for Accurate Accident Prediction Models

Sara Ferreira, César De Santos-Berbel, António Couto, António Lobo
1 citations
Sustainability
Traffic and Road Safety
0.36
article

Development of Motorway Horizontal Alignment Databases for Accurate Accident Prediction Models

Sara Ferreira, César De Santos-Berbel, António Couto, António Lobo
article en
1 citations

Abstract

The safe and efficient operation of highways minimizes the environmental impact, reduces accidents, and promotes the reliability of the transportation infrastructure, all in support of sustainable transportation. The horizontal alignment of highways holds particular importance as it directly impacts driver behavior, vehicle stability, and overall road safety. In many cases, highway inventory data held by infrastructure operators may contain inaccurate or outdated information. The accuracy of the variables used in crash prediction models eliminates possible bias in the variable estimators. This research proposes a methodology to obtain accurate horizontal geometric features from digital imagery based on the analysis of the planimetry, feature geolocation and centerline azimuth sequence. The reliability of the method is verified by means of numerical and statistical procedures. This methodology is applied to 150 km of motorway segments in Portugal. Although it is found that the geometric characteristics of most of the inventory segments closely matched the extracted alignments, very significant differences are found in some sections. The results of the proposed procedure are illustrated with several examples. Finally, the propagation of error in the determination of the geometric design independent variables in the fitting of the statistical models is discussed based on the results.

SustainabilityVol. 16(17)
Universidade do Porto (PT), Universidad Politécnica de Madrid (ES)
Universidad Politécnica de Madrid, Fundação para a Ciência e a Tecnologia
Industry, innovation and infrastructure
Openalex Percentile: Top 41%
Traffic and Road Safety
0.36
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Development of Motorway Horizontal Alignment Databases for Accurate Accident Prediction Models — Sara Ferreira, César De Santos-Berbel, et al. · Sustainability (2024) | TGRS Research Map | TGRS