Reinforcement learning-based approach for urban road project scheduling considering alternative closure types

Abstract

Growth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re-reroute traffic or implement partial closure. Both options have significant implications for peri-construction road capacity, traveler costs, and the project duration and cost. This study presents a decision-making methodology to facilitate the choice between full road closure and partial closure. The presented decision-making methodology is a bi-level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower-level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path-loyal travelers who do not change their routes from their pre-construction routes. The bi-level mixed integer nonlinear model is solved using a reinforcement learning-based algorithm (the multi-armed bandit-guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path-loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions.