A Predictive Pedestrian Crash Model Based on Artificial Intelligence Techniques
| Autor: | Irene Petrizzo, Giulia Martini, Roberto Arrighi, Monica Meocci, Valentina Branzi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Computer science QH301-705.5 QC1-999 Crash Accident analysis Pedestrian Machine learning computer.software_genre gradient boosting modelling General Materials Science Biology (General) Instrumentation QD1-999 Fluid Flow and Transfer Processes Flexibility (engineering) Risk level Potential risk business.industry Process Chemistry and Technology Physics General Engineering Engineering (General). Civil engineering (General) Computer Science Applications pedestrian crashes Chemistry Artificial intelligence Gradient boosting TA1-2040 business computer |
| Zdroj: | Applied Sciences; Volume 11; Issue 23; Pages: 11364 Applied Sciences, Vol 11, Iss 11364, p 11364 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app112311364 |
| Popis: | Every year in Italy, there are about 20,000 road accidents involving pedestrians, with a significant number of injuries and deaths. Out of these, about 30% occur at pedestrian crossings, where pedestrians should be protected the most. Here, we propose a new accident prediction model to improve pedestrian safety assessments that allows us to accurately identify the sites with the largest potential safety improvements and define the best treatments to be applied. The accident prediction model was developed using the ISTAT dataset, including information about the fatal and injurious crashes that occurred in Italy in a 5-year period. The model allowed us to estimate the risk level of a road section through a machine-learning approach. Gradient Boosting seems to be an appropriate tool to fit classification models for its flexibility that allows us to capture non-linear relationships that would be difficult to detect via a classical approach. The results show the ability of the model to perform an accurate analysis of the sites included in the dataset. The locations analyzed have been classified based on the potential risk in the following three classes: High, medium, and low. The proposed model represents a solid and reliable tool for practitioners to perform accident analysis with pedestrian involvement. |
| Databáze: | OpenAIRE |
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