Optimal, centralized dynamic curbside parking space zoning
Abstract
In this paper we formulate a dynamic mixed integer program for optimally zoning curbside parking spaces subject to transportation policy-inspired constraints and regularization terms. First, we illustrate how given some objective of curb zoning valuation as a function of zone type (e.g., paid parking or bus stop), dynamically rezoning involves unrolling this optimization program over a fixed time horizon. Second, we implement two different solution methods that optimize for a given curb zoning value function. In the first method, we solve long horizon dynamic zoning problems via approximate dynamic programming. In the second method, we employ Dantzig-Wolfe decomposition to break-up the mixed-integer program into a master problem and several sub-problems that we solve in parallel; this decomposition accelerates the MIP solver considerably. We present simulation results and comparisons of the different employed techniques on vehicle arrival-rate data obtained for a neighborhood in downtown Seattle, Washington, USA
Cite
@article{arxiv.2207.10232,
title = {Optimal, centralized dynamic curbside parking space zoning},
author = {Nawaf Nazir and Shushman Choudhury and Stephen Zoepf and Ke Ma and Chase Dowling},
journal= {arXiv preprint arXiv:2207.10232},
year = {2022}
}