A bi‐level model to optimize road networks for a mixture of manual and automated driving: An evolutionary local search algorithm

• Published in: Computer-aided civil and infrastructure engineering Vol. 35; no. 1; pp. 80 - 96
• Main Authors: Madadi, Bahman, Nes, Rob, Snelder, Maaike, Arem, Bart
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.01.2020
• Subjects: Algorithms; Automation; Evolutionary algorithms; Optimization; Penetration; Roads & highways; Search algorithms; Vehicles
• ISSN: 1093-9687; 1467-8667; 1467-8667
• DOI: 10.1111/mice.12498

This paper presents a bi‐level model to optimize automated‐vehicle‐friendly subnetworks in urban road networks and an efficient algorithm to solve the model, which is relevant for the transition period with vehicles of different automation levels. We formulate the problem as a network design problem, define solution requirements, present an effective solution method that meets those requirements, and compare its performance with two other solution algorithms. Numerical examples for network of Delft are presented to demonstrate the concept and solution algorithm performances. Results indicate that our proposed solution outperforms competing ones in all criteria considered. Furthermore, our findings show that the optimal configuration of these subnetworks depends on the level of demand; lower penetration rates of automated vehicles call for less dense subnetworks, and thereby less investments. Nonetheless, a large proportion of benefits are already achievable with low‐density subnetworks. Denser subnetworks can deliver higher benefits with higher penetration rates.