首页|Findings on Machine Learning Discussed by Investigators at Univer- sity of Louisiana Lafayette (Exploring Nighttime Pedestrian Crash Patterns At Intersection and Segments: Findings From the Machine Learning Algorithm)
Findings on Machine Learning Discussed by Investigators at Univer- sity of Louisiana Lafayette (Exploring Nighttime Pedestrian Crash Patterns At Intersection and Segments: Findings From the Machine Learning Algorithm)
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Elsevier
2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Machine Learning have been presented. According to news reporting from Lafayette, Louisiana, by NewsRx journalists, research stated, “Pedestrian safety at nighttime is an ongoing critical traffic safety concern. Although poor visibility is primarily associated with nighttime pedestrian crashes, other contributing factors such as humans, vehicles, roadways, and environmental factors interact with each other to cause a crash.” Financial support for this research came from Louisiana Department of Transportation and Develop- ment.
article include: LafayetteLouisianaUnited StatesNorth and Central AmericaAlgorithmsCyborgsEmerging TechnologiesMachine LearningRisk and PreventionUniversity of Louisiana Lafayette
NETL
NSTL
Elsevier
