Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand

Abstract

Compact development aims to lead to dense and mixed distributions of population and employment in land use planning which may result in reduced vehicle miles traveled (VMT). However, determining an optimum or desirable configuration of population and employment distribution to achieve the compact development goal remains challenging due to numerous competitive development alternatives or possible exacerbated traffic congestion as a result of over-intensification of urban land use. To address this challenge, a systematic approach is proposed with a bi-level optimization model aiming to find out an efficient population and employment map for vehicle travel reduction. The upper level of the model is formulated to minimize VMT and vehicle hours traveled (VHT) varying with possible changes in population and employment densities. The lower level is based on a tour-based travel demand model to mechanistically represent the response of travel choices to those changes. The programming is solved by a Genetic Algorithm. The proposed method is demonstrated through a case study of Hamilton County, Ohio, U.S. The results indicate that a more compact urban form reduces VMT; however, it may cause longer VHT depending upon the density. To avoid urban overconcentration and reduce both VMT and VHT simultaneously, a compact urban development configuration with a population density < 5289 persons/mi2 and the employment density < 3282 jobs/mi2 is recommended for the area. As a negative relationship demonstrated between transit headway reduction and vehicle travel demand, strategies for improving transit provision are helpful to reducing vehicle travels in compact development scenarios.