A multivariate regression model to predict the GHG emission in urban logistics

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dc.contributor.authorAlhindawi, Reham
dc.contributor.authorAbu Nahleh, Yousef
dc.contributor.authorKumar, Arun
dc.contributor.authorShiwakoti, Nirajan
dc.date.accessioned2025-11-17T09:24:42Z
dc.date.available2025-11-17T09:24:42Z
dc.date.issued2016
dc.descriptionAn urban area is the region surrounding a city. Most inhabitants of urban areas have non-agricultural jobs. Urban areas are very developed, meaning there is a density of human structures such as houses, commercial buildings, roads, bridges, and railways [1] .
dc.description.abstractThe greenhouse gas (GHG) emission in urban logistics is an important driver for determining future energy needs. An empirical model is developed for the greenhouse gas emissions based on multivariate linear regression to identify the main drivers of greenhouse gas emission. It has been found that the Vehicle-kilometers by Mode (VKM) and Number of Transportation Vehicle (NTV) are the most important variables that affect the gas emission. The results show that the multivariate linear regression model can be used to adequately model with coefficient of determination (R2) and adjusted R2 values of 85.1% and 83.2%, respectively. Keywords Multivariate regression, Urban logistic, Greenhouse Gas Emissions
dc.identifier.citationAlhindawi, R., Nahleh, Y. A., Kumar, A., & Shiwakoti, N. (2016). A multivariate regression model to predict the GHG emission in urban logistics. In 23rd ITS World Congress (pp. 1-10).
dc.identifier.urihttps://repository.adu.ac.ae/handle/1/7736
dc.language.isoen
dc.publisherITS World Congress
dc.titleA multivariate regression model to predict the GHG emission in urban logistics
dc.typeArticle

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