Decision-Dependent Multiobjective Multiperiod Stochastic Model for Parking Location Analysis in Sustainable Cities: Evidence from a Real Case

Abstract

Unorganized city growth causes traffic congestion, and, in return, this forces governments to invest in costly buildings such as public parking lots. The demand for parking spaces has further increased due to rapid population growth, especially in metropolitan areas. One solution to this problem is to locate new public parking lots within city centers so that car owners can use these facilities instead of on-street parking. In this study, a facility location and allocation scheme was engaged to determine the optimal number of and locations for public parking lots. A multiobjective, multiperiod, decision-dependent, two-stage, stochastic mathematical model was developed to fulfill the decision makers’ aims via different objectives. In the model, three objectives were considered: minimizing total facility establishment and operational cost, minimizing average unsatisfied demand, and minimizing CO2 emissions. The proposed framework is the first in parking lot literature to address the multiobjectivity of decision-dependent demand within an uncertain environment. The weighted sum method was used to develop the multiobjective model, and two-stage stochastic programming was applied to manage the uncertainty. Hence, the proposed model satisfies critical gaps, predominantly in relation to the problem of parking lot location, and most importantly in creating sustainable cities. The results provide meaningful outcomes for decision makers in terms of optimizing unsatisfied demand, tackling CO2 emissions, and determining the optimal number and location of public parking lots. Furthermore, the robustness of the model was tested through sensitivity analyses.