Optimal, centralized dynamic curbside parking space zoning

TL;DR

This paper develops a dynamic mixed integer programming framework for optimal curbside parking zoning, incorporating transportation policies and solution methods like approximate dynamic programming and Dantzig-Wolfe decomposition, validated through simulations in Seattle.

Contribution

It introduces a novel dynamic optimization approach for curb zoning, combining advanced solution techniques to improve computational efficiency and policy implementation.

Findings

Decomposition significantly accelerates the MIP solver.

Dynamic zoning optimization outperforms static approaches.

Simulation demonstrates practical applicability in urban settings.

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