Multi-stage optimal design of road networks for automated vehicles with elastic multi-class demand

Multi-stage optimal design of road networks for automated vehicles with elastic multi-class demand

Madadi, B. (TU Delft Transport and Planning)
van Nes, R. (TU Delft Transport and Planning)
Snelder, M. (TU Delft Transport and Planning; TNO)
van Arem, B. (TU Delft Transport and Planning)

2021

With the advent of automated vehicles (AVs), new infrastructure planning concepts such as subnetworks of AV-ready roads have been proposed to improve the performance of transportation networks and to promote the adoption of AVs. However, these subnetworks should evolve over time in response to the growing AV demand, which necessitates a multi-stage modeling approach. In this study, we propose multi-stage deployment of AV-ready subnetworks and formulate it as a time-dependent network design problem, which is a bi-level mixed-integer programming problem. The lower level is a simultaneous travel mode and route choice equilibrium with continuous decision variables, and the upper level is a design problem including infrastructure investment decisions to determine which roads to upgrade and include in AV-ready subnetworks for mixed traffic. We use a case study of a real road network to demonstrate the concept. Since computational efficiency is a key factor for solving such large-scale problems, we develop two efficient and tailored evolutionary heuristics to solve the problem, and compare their performance to a computationally demanding Genetic-algorithm-based solution method. The results indicate that the proposed algorithms can efficiently solve this large-scale problem while satisfying constraints in all scenarios, and outperform Genetic algorithm, particularly in the scenario with larger number of stages. Moreover, in all scenarios, deployment of AV-ready subnetworks leads to improvements in network performance in terms of total travel time and cost. However, the improvements are always accompanied with increased total travel distance. The extent of changes depends on AV market penetration rate, AV-ready subnetwork density and timing of densification.

Automated vehicles
Evolutionary computations
Mixed traffic
Time-dependent network design problem
Transportation

http://resolver.tudelft.nl/uuid:fe5e725a-6548-41c7-8994-447969cae556

https://doi.org/10.1016/j.cor.2021.105483

0305-0548

Computers & Operations Research, 136

Institutional Repository

journal article

© 2021 B. Madadi, R. van Nes, M. Snelder, B. van Arem