Articles | Volume 20, issue 10 
            
                
                    
            
            
            https://doi.org/10.5194/nhess-20-2857-2020
                    © Author(s) 2020. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-2857-2020
                    © Author(s) 2020. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
Predictive modeling of hourly probabilities for weather-related road accidents
                                            Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
                                        
                                    
                                            Hans-Ertel-Centre for Weather Research, Berlin, Germany
                                        
                                    Henning W. Rust
                                            Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
                                        
                                    
                                            Hans-Ertel-Centre for Weather Research, Berlin, Germany
                                        
                                    Uwe Ulbrich
                                            Institut für Meteorologie, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
                                        
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                            Cited
14 citations as recorded by crossref.
- Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents A. Aleksić et al. 10.3390/math11020479
 - The Effect of Ramp Proximity, Weather, and Time-of-Day on Freeway Accident Frequency: A Case Study on I-75 and I-24 in Hamilton County, TN E. Laflamme et al. 10.2174/18744478-v16-e2203140
 - Methodology for Optimizing Factors Affecting Road Accidents in Poland P. Gorzelanczyk & H. Tylicki 10.3390/forecast5010018
 - The Use of Machine Learning Methods in Road Safety Research in Poland A. Borucka & S. Sobczuk 10.3390/app15020861
 - Time-Series Forecasting for Peak Hour Traffic Accidents M. Shikder et al. 10.1109/OJITS.2025.3583686
 - Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data Z. Feng & K. Subramaniya 10.1051/itmconf/20257001004
 - Development of Modified Temporal Safety Performance Function Considering Various Time Flows Y. Sung et al. 10.1155/2024/7970454
 - Impact of weather conditions and road type on traffic safety P. Gorzelanczyk 10.1016/j.ets.2025.100042
 - Road Accident Analysis and Prevention Using Autonomous Vehicles with Application for Montreal M. Singh & A. Awasthi 10.3390/electronics14163329
 - Modeling hourly weather-related road traffic variations for different vehicle types in Germany N. Becker et al. 10.1186/s12544-022-00539-0
 - Investigation of the Effect of Slope and Road Surface Conditions on Traffic Accidents Occurring in Winter Months: Spatial and Machine Learning Approaches E. Kuşkapan et al. 10.3390/app142411629
 - Universal thermal climate index associations with mortality, hospital admissions, and road accidents in Bavaria W. Ghada et al. 10.1371/journal.pone.0259086
 - Weather impacts on various types of road crashes: a quantitative analysis using generalized additive models N. Becker et al. 10.1186/s12544-022-00561-2
 - Evaluation of factors affecting the number of traffic accidents P. Gorzelanczyk & P. Piątkowski 10.31648/ts.10043
 
13 citations as recorded by crossref.
- Using Machine Learning in Predicting the Impact of Meteorological Parameters on Traffic Incidents A. Aleksić et al. 10.3390/math11020479
 - The Effect of Ramp Proximity, Weather, and Time-of-Day on Freeway Accident Frequency: A Case Study on I-75 and I-24 in Hamilton County, TN E. Laflamme et al. 10.2174/18744478-v16-e2203140
 - Methodology for Optimizing Factors Affecting Road Accidents in Poland P. Gorzelanczyk & H. Tylicki 10.3390/forecast5010018
 - The Use of Machine Learning Methods in Road Safety Research in Poland A. Borucka & S. Sobczuk 10.3390/app15020861
 - Time-Series Forecasting for Peak Hour Traffic Accidents M. Shikder et al. 10.1109/OJITS.2025.3583686
 - Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data Z. Feng & K. Subramaniya 10.1051/itmconf/20257001004
 - Development of Modified Temporal Safety Performance Function Considering Various Time Flows Y. Sung et al. 10.1155/2024/7970454
 - Impact of weather conditions and road type on traffic safety P. Gorzelanczyk 10.1016/j.ets.2025.100042
 - Road Accident Analysis and Prevention Using Autonomous Vehicles with Application for Montreal M. Singh & A. Awasthi 10.3390/electronics14163329
 - Modeling hourly weather-related road traffic variations for different vehicle types in Germany N. Becker et al. 10.1186/s12544-022-00539-0
 - Investigation of the Effect of Slope and Road Surface Conditions on Traffic Accidents Occurring in Winter Months: Spatial and Machine Learning Approaches E. Kuşkapan et al. 10.3390/app142411629
 - Universal thermal climate index associations with mortality, hospital admissions, and road accidents in Bavaria W. Ghada et al. 10.1371/journal.pone.0259086
 - Weather impacts on various types of road crashes: a quantitative analysis using generalized additive models N. Becker et al. 10.1186/s12544-022-00561-2
 
1 citations as recorded by crossref.
Latest update: 03 Nov 2025
Short summary
                    A set of models is developed to forecast hourly probabilities of weather-related road accidents in Germany at the spatial scale of administrative districts. Model verification shows that using precipitation and temperature data leads to the best accident forecasts. Based on weather forecast data we show that skilful predictions of accident probabilities of up to 21 h ahead are possible. The models can be used to issue impact-based warnings, which are relevant for road users and authorities.
                    A set of models is developed to forecast hourly probabilities of weather-related road accidents...
                    
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