Untangling the Growing Pedestrian Safety Problem on Urban Arterials
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2024-07-01
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Edition:Final Report – June 2023 to May 2024
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Abstract:Pedestrian fatalities in Tennessee are predominantly associated with nighttime, high-speed crashes (35 mph and above), and straight midblock maneuvers, like those observed on urban arterials. The existing literature primarily relies on the functional classification of roads, which can be ambiguous, especially when used for pedestrian safety and its relationship with roadway-related variables. This study introduces the concept of "high-risk crashes" to identify potential hazardous pedestrian crashes beyond state classifications. Using unsupervised learning algorithms (latent class and hierarchical clustering) and supervised learning with manually labeled data, pedestrian crashes in Tennessee were categorized into high-risk crashes and non-high-risk crashes based on road and environmental features with high accuracy. The classification revealed clusters of high-risk crashes along wide, straight streets with higher speed limits, limited pedestrian facilities, and businesses primarily catering to cars, which may not be officially classified as major arterials. Trend analysis of crash involvement and fatalities showed a steep increase in high-risk crashes, suggesting that the rise in pedestrian crash severity in Tennessee can be attributed to these crashes. Logistic regression results indicated that high-risk crashes are more likely to result in fatal injuries in dark conditions, during straight midblock maneuvers, and in non-residential areas. Vehicle size did not significantly impact the likelihood of a fatal crash in high-risk crashes. These findings call for urgent measures focusing primarily on roads, such as lowering speed limits to 35 mph in pedestrian-heavy areas, increasing safe pedestrian crossing opportunities, adopting traffic calming devices, and improving lighting to enhance pedestrian safety.
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Main Document Checksum:urn:sha-512:ff6b669d7a2eb74cdd4e84cb518e2ee30c1d7b206b16a4765462f60c3e21989f2dfe202886da7cda8ae9a32490cc9248b6d666de96d98d04de510f4ba7ad3e12
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