A novel approach to modeling urban commuting traffic demands

Abstract

The scientific prediction of urban commuting traffic demands can support rational urban planning for population distribution, enterprise placement, and the coordination of land use and transportation. This study develops an Urban Commuting Model (UCM) that integrates both spatial and temporal aspects: Spatially, changes in employment or population distribution lead to changes in commuting patterns; Temporally, the commuting patterns of the previous year form the basis for the patterns of the following year. The UCM, based on historical commuting matrix, simulates urban traffic demands under various scenarios of urban residential population and employment planning. In a case study, the proposed model was used to simulate urban traffic demands in Beijing under the construction scenario of the city's sub-center in Tongzhou. The case study demonstrates that the UCM can effectively predict urban traffic demands under different land use and transportation scenarios, providing informative policy implications at an early planning stage. This study offers a novel approach for simulating urban traffic demands and is a valuable addition to the existing literature.