A hybrid deep-learning-metaheuristic framework for bi-level network design problems
This paper presents a novel hybrid framework combining deep learning with metaheuristic optimization to solve bi-level network design problems. Traditional optimization approaches …
Optimizing Road Networks for Automated Vehicles with Dedicated Links, Dedicated Lanes, and Mixed-Traffic Subnetworks
This research explores different infrastructure strategies for integrating automated vehicles into existing road networks, comparing three approaches: dedicated links (entire roads …
Multi-stage optimal design of road networks for automated vehicles with elastic multi-class demand
This paper develops a multi-stage optimization framework for designing road networks that accommodate automated vehicles while considering elastic multi-class demand. Unlike …
A stochastic approximation approach to spatio-temporal anchorage planning with multiple objectives
A bi-level model to optimize road networks for a mixture of manual and automated driving: An evolutionary local search algorithm
This paper presents a bi-level optimization model and evolutionary local search algorithm for optimizing road networks during the transition period when both manual and automated …
Assessing the travel impacts of subnetworks for automated driving: An exploratory study
This exploratory study investigates the concept of subnetworks for automated driving - designated areas within urban road networks where automated vehicles can operate. Using a …