Exploring an Interaction Model for Land Used Intensity-traffic Congestion

Abstract

Traffic flow is a result of the connection between the derived demand and land use. The derived demand varies in space and time, this study explores how traffic congestion is correlated with land use patterns. Historically, statistical models were used to predict and analyze these patterns. The methodology of this study is to investigate this interaction by statistical methods such as linear regression modeling. This analysis was performed using various land use types that could influence the demand. From the regression analysis, the best influence variables that affect the model are land use variables. The strong statistical parameter is commercial land use, which affects traffic volume, and causes the highest traffic congestion. In addition, correlation values are negative, meaning that as commercial land use increases, traffic flow increases and road capacity decreases. When modeling with the commercial land use variable, we conclude the value of R-Squared = 0.87 and that the relationship is an inverse strong relationship between traffic volumes and commercial land use. Mostly, land use govern traffic demand.