What are the most commonly used methodologies for pedestrian crash analysis?
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The most commonly used methodologies for pedestrian crash analysis include econometric models such as the mixed logit model , machine learning algorithms like association rules and classification trees , latent class cluster analysis (LCA) , and the integration of LCA and mixed logit models . Passive approaches, such as crash database studies, questionnaires, and post-crash field observation data, are also commonly used . Active approaches, such as driving simulations and videography, are employed to gain a more detailed understanding of pedestrian behaviors and attitudes . Additionally, spatial analysis tools like spatial autocorrelation, space-time cube, and emerging hotspot analysis are used to identify and predict pedestrian crash hotspots . The combination of these methodologies provides insights into the contributing factors, patterns, and severity of pedestrian crashes, aiding in the development of effective countermeasures and strategies for pedestrian safety improvement .
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| The most commonly used methodologies for pedestrian crash analysis in the provided paper are the mixed logit model (econometric model) and machine learning tools such as association rules and classification trees. | |
17 Citations | The most commonly used methodologies for pedestrian crash analysis are based on crash databases, questionnaires, post-crash field observation data, driving simulations, and videography. |
6 Citations | The paper does not mention the most commonly used methodologies for pedestrian crash analysis. |
| The paper does not explicitly mention the most commonly used methodologies for pedestrian crash analysis. |
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