Forecasting Traffic Generation in Public Housing Estates

Abstract

The development of public housing has major traffic impacts on road networks, particularly in already congested cities. Hong Kong, being one of the most congested cities in the world, has a significant amount of public housing in town. Severe traffic congestion occurs in the adjacent road network if the forecast of traffic generation is not accurate. This technical note develops a multilinear regression model to forecast traffic generation for large-scale, high-density, multistory public residential housing estates in Hong Kong. Manual traffic counts were conducted at 36 housing development sites throughout Hong Kong. Regression analysis was undertaken for model evaluation. Many variables, such as the number of apartments, population, gross floor area, parking spaces, and accessibility, were included in the analysis, and the resulting model is both qualitatively and quantitatively reasonable.