Predictable motorway ramp curves are safer
Motorway safety depends largely on curve geometry and driver behaviour, a relationship that has implications for research and practice. This paper introduces a novel approach to quantifying geometric design consistency, defined as the degree to which drivers’ expectations of curve radii match actual...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Transportation Research Interdisciplinary Perspectives |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198225002015 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849229273751093248 |
|---|---|
| author | Johan Vos |
| author_facet | Johan Vos |
| author_sort | Johan Vos |
| collection | DOAJ |
| description | Motorway safety depends largely on curve geometry and driver behaviour, a relationship that has implications for research and practice. This paper introduces a novel approach to quantifying geometric design consistency, defined as the degree to which drivers’ expectations of curve radii match actual road geometries. The hypothesis is that if a driver expects a larger curve than that actually present, an accident might occur because of an excessively high approach speed. To test this hypothesis, this study uses Dutch motorway data, including ramp and curve characteristics, as well as crash frequencies. The data were employed in three steps: 1) constructing a Bayesian model that mimics drivers’ expectations, 2) testing the predictions of this model against real curve characteristics, and 3) examining the relationship between disparities in expectations, reality, and crash frequency. The results indicated a positive correlation between disparities in expectations, reality, and crash frequency. This finding suggests that the crash frequency is higher when drivers expect a larger curve than what is present. The Tree Augmented Naïve Bayesian Network (TAN) reveals the complexity of curve expectations, demonstrating that drivers anticipate larger radii in connector ramps and higher speeds with gentler curve angles. Applying this research to motorway design involves using TAN predictions and crash frequency models to assess safety in motorway curve design, which could proactively improve road safety. |
| format | Article |
| id | doaj-art-edf63bb4ca614d1bb330908fc4e4678c |
| institution | Kabale University |
| issn | 2590-1982 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj-art-edf63bb4ca614d1bb330908fc4e4678c2025-08-22T04:57:49ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822025-07-013210152210.1016/j.trip.2025.101522Predictable motorway ramp curves are saferJohan Vos0Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the NetherlandsMotorway safety depends largely on curve geometry and driver behaviour, a relationship that has implications for research and practice. This paper introduces a novel approach to quantifying geometric design consistency, defined as the degree to which drivers’ expectations of curve radii match actual road geometries. The hypothesis is that if a driver expects a larger curve than that actually present, an accident might occur because of an excessively high approach speed. To test this hypothesis, this study uses Dutch motorway data, including ramp and curve characteristics, as well as crash frequencies. The data were employed in three steps: 1) constructing a Bayesian model that mimics drivers’ expectations, 2) testing the predictions of this model against real curve characteristics, and 3) examining the relationship between disparities in expectations, reality, and crash frequency. The results indicated a positive correlation between disparities in expectations, reality, and crash frequency. This finding suggests that the crash frequency is higher when drivers expect a larger curve than what is present. The Tree Augmented Naïve Bayesian Network (TAN) reveals the complexity of curve expectations, demonstrating that drivers anticipate larger radii in connector ramps and higher speeds with gentler curve angles. Applying this research to motorway design involves using TAN predictions and crash frequency models to assess safety in motorway curve design, which could proactively improve road safety.http://www.sciencedirect.com/science/article/pii/S2590198225002015Motorway curvesRoad safetyDriver expectationsBayesian Belief ModelCrash frequency |
| spellingShingle | Johan Vos Predictable motorway ramp curves are safer Transportation Research Interdisciplinary Perspectives Motorway curves Road safety Driver expectations Bayesian Belief Model Crash frequency |
| title | Predictable motorway ramp curves are safer |
| title_full | Predictable motorway ramp curves are safer |
| title_fullStr | Predictable motorway ramp curves are safer |
| title_full_unstemmed | Predictable motorway ramp curves are safer |
| title_short | Predictable motorway ramp curves are safer |
| title_sort | predictable motorway ramp curves are safer |
| topic | Motorway curves Road safety Driver expectations Bayesian Belief Model Crash frequency |
| url | http://www.sciencedirect.com/science/article/pii/S2590198225002015 |
| work_keys_str_mv | AT johanvos predictablemotorwayrampcurvesaresafer |