首页|Findings from University of Utrecht Provides New Data about Machine Learning (Bu ilt Environment Influences Commute Mode Choice In a Global South Megacity Contex t: Insights From Explainable Machine Learning Approach)
Findings from University of Utrecht Provides New Data about Machine Learning (Bu ilt Environment Influences Commute Mode Choice In a Global South Megacity Contex t: Insights From Explainable Machine Learning Approach)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting originating in Utrecht, Netherland s, by NewsRx journalists, research stated, "In this study, we aimed to investiga te the influence of the built environment (BE) on commuter mode choice using mac hine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh."The news reporters obtained a quote from the research from the University of Utr echt, "We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance of the classifiers, we i dentified that the Random Forest (RF) algorithm performed the best. Using the RF model, this study also explored the relative importance of BE factors in predic ting commute mode choice, identified nonlinear relationships between the BE fact ors and mode choice, and examined the interaction effects of these factors on mo de selection. Our results reveal that, compared to socio-demographic factors, th e BE substantially influence commuter travel behavior. The BE characteristics ha ve a specific nonlinear threshold limit at which they can have a notable impact on lowering private car use, and private car use does not display a constant ret urn of scale with BE. Their interaction effects illustrate the potential optimal combination of BE interventions to lower private car use for commuting."
UtrechtNetherlandsEuropeCyborgsE merging TechnologiesMachine LearningUniversity of Utrecht
NETL
NSTL
