Strategic planning support for road safety measures based on accident data mining
When actions and measures to increase road safety are to be planned by the police and local authorities, it is necessary to consider the specific accident circumstances as well as their historical, current, and predicted course. In particular, combinations of accident circumstances not contained in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S038611122200036X |
| _version_ | 1811226342350389248 |
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| author | Katherina Meißner Julia Rieck |
| author_facet | Katherina Meißner Julia Rieck |
| author_sort | Katherina Meißner |
| collection | DOAJ |
| description | When actions and measures to increase road safety are to be planned by the police and local authorities, it is necessary to consider the specific accident circumstances as well as their historical, current, and predicted course. In particular, combinations of accident circumstances not contained in existing police statistics are often neglected, but may nevertheless be relevant, e. g., due to an increasing frequency. In order to identify these undiscovered interesting combinations, we propose a framework to support strategic planning of road safety measures based on several consecutive data mining stages. The scope, type, and location of road safety measures must be planned at a strategic level several months in advance to be fully effective. Therefore, it is essential to investigate and predict the accident circumstances and the temporal changes in their frequency comprehensively. Only with the knowledge, e. g., about the temporal pattern, locations, conditions of roads or speeds, meaningful actions can be derived. The embedded data mining approaches, i. e., frequent itemset mining, time series clustering, time series classification, forecasting, and scoring, are carefully selected, coordinated, and aligned. As a result, the framework provides police users with information about circumstances of accidents that are of interest in the future and presents their previous temporal and local patterns in a dashboard. In this study, the framework is applied in four different geographical regions. Thereby, default parameter settings for all approaches are found that are particularly suitable for the framework to investigate novel geographic regions. |
| first_indexed | 2024-04-12T09:22:25Z |
| format | Article |
| id | doaj.art-47fc4d313833469782a3f2f89ec9637c |
| institution | Directory Open Access Journal |
| issn | 0386-1112 |
| language | English |
| last_indexed | 2024-04-12T09:22:25Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj.art-47fc4d313833469782a3f2f89ec9637c2022-12-22T03:38:34ZengElsevierIATSS Research0386-11122022-10-01463427440Strategic planning support for road safety measures based on accident data miningKatherina Meißner0Julia Rieck1Corresponding author.; University of Hildesheim, Universitätsplatz 1, 31141 Hildesheim, GermanyUniversity of Hildesheim, Universitätsplatz 1, 31141 Hildesheim, GermanyWhen actions and measures to increase road safety are to be planned by the police and local authorities, it is necessary to consider the specific accident circumstances as well as their historical, current, and predicted course. In particular, combinations of accident circumstances not contained in existing police statistics are often neglected, but may nevertheless be relevant, e. g., due to an increasing frequency. In order to identify these undiscovered interesting combinations, we propose a framework to support strategic planning of road safety measures based on several consecutive data mining stages. The scope, type, and location of road safety measures must be planned at a strategic level several months in advance to be fully effective. Therefore, it is essential to investigate and predict the accident circumstances and the temporal changes in their frequency comprehensively. Only with the knowledge, e. g., about the temporal pattern, locations, conditions of roads or speeds, meaningful actions can be derived. The embedded data mining approaches, i. e., frequent itemset mining, time series clustering, time series classification, forecasting, and scoring, are carefully selected, coordinated, and aligned. As a result, the framework provides police users with information about circumstances of accidents that are of interest in the future and presents their previous temporal and local patterns in a dashboard. In this study, the framework is applied in four different geographical regions. Thereby, default parameter settings for all approaches are found that are particularly suitable for the framework to investigate novel geographic regions.http://www.sciencedirect.com/science/article/pii/S038611122200036XAccident AnalysisTime Series ClusteringTime Series ForecastingDecision Support SystemStrategic PlanningDashboard |
| spellingShingle | Katherina Meißner Julia Rieck Strategic planning support for road safety measures based on accident data mining IATSS Research Accident Analysis Time Series Clustering Time Series Forecasting Decision Support System Strategic Planning Dashboard |
| title | Strategic planning support for road safety measures based on accident data mining |
| title_full | Strategic planning support for road safety measures based on accident data mining |
| title_fullStr | Strategic planning support for road safety measures based on accident data mining |
| title_full_unstemmed | Strategic planning support for road safety measures based on accident data mining |
| title_short | Strategic planning support for road safety measures based on accident data mining |
| title_sort | strategic planning support for road safety measures based on accident data mining |
| topic | Accident Analysis Time Series Clustering Time Series Forecasting Decision Support System Strategic Planning Dashboard |
| url | http://www.sciencedirect.com/science/article/pii/S038611122200036X |
| work_keys_str_mv | AT katherinameißner strategicplanningsupportforroadsafetymeasuresbasedonaccidentdatamining AT juliarieck strategicplanningsupportforroadsafetymeasuresbasedonaccidentdatamining |