Presentation of a Multiple Regression Model to Determine the Distance of Primary Arterial Roads and Building Density in a Radial-Circular Network

The form of urban networks plays a crucial role in the efficiency of traffic flow. Small deficiencies in the geometric design or spatial distribution of arterial networks can disrupt traffic continuity, reduce operational efficiency, and lead to increased congestion in urban transportation systems. The distance between roads with different functions is one of the most significant parameters affecting traffic in urban networks. Therefore, in this study, a radial-circular network was designed with primary arterial roads at varying distances (۸۰۰ and ۱۲۰۰ meters) and building densities ranging from ۱۰۰% to ۲۵۰%. Using AIMSUN software, the network was simulated, and traffic parameters were extracted. Subsequently, a predictive model was developed using multiple regression analysis, with primary arterial road distance as the dependent variable and building density and traffic parameters as independent variables. The results indicated that the final model achieved a distance prediction accuracy of ۰.۹۴۹ for primary arterial roads. The variables of density (with a negative influence), stop time, operational speed, and queue length had a significantly greater impact on predicting the functional distance of primary arterial roads. Specifically, higher building density is associated with a decrease in the optimal spacing of these roads, whereas longer stop times and greater queue lengths contribute to increased spacing requirements. Moreover, an increase in land use density and the functional distance of roads leads to a higher increase in vehicle queue length, particularly in the three-ring network with a distance of ۸۰۰ meters between roads. Additionally, as similar values increase, the stop time of vehicles also rises.

کلیدواژه ها:

Building Density ، distance of arterial roads ، delay ، Flow Rate ، travel time ، Queue Length