Title
Why Response Matters: Simultaneous Bi-Level Optimization for Network Design Problems
Author
Snelder, M.
Smit, S.
Schadd, M.
Walraven, E.
Publication year
2023
Abstract
Network design problems such as the optimization of road capacities, public transport lines and frequencies, mobility hub locations, shared mobility services and traffic signal settings have been extensively studied in literature using sequential bi-level optimization. However, due to the computational burden, the design and response spaces that have been considered so far are rather limited, resulting in suboptimal designs. In this paper we introduce a new framework for simultaneous bi-level optimization that contributes to the existing literature by optimizing designs and responses simultaneously rather than sequentially, and by using machine learning for the design and response optimization instead of only for the design optimization, thus allowing for a much larger design and response space. Based on experiments for the city of Amsterdam in which parking lot locations and tariffs are optimized, this paper demonstrates that simultaneous design and response optimization is possible and that designs created with a small fixed response space, which is common practice, may be worse than expected, due to the fact that a population can respond in a different way than anticipated during design optimization. The proposed design and response optimization framework is able to automatically learn responses, leading to designs that are 21% to 53% better than designs created with a fixed response space depending on the indicator chosen. In future work, the framework can be extended to a multi-level optimization framework to include multiple stakeholder groups that all respond to each other by simultaneously running machine learning algorithms for each stakeholder group.
Subject
Network Design Problem
Simultaneous Optimization
Bi-level Optimization
Machine learning
User Responses
Mobility & Logistics
Urbanisation
To reference this document use:
http://resolver.tudelft.nl/uuid:79bbe6f0-fb39-4535-925b-0ddefc47f1bc
TNO identifier
981784
Source
A Proof of Concept Study. Transportation Research Board (TRB) 102nd Annual Meeting, 2023 Washington D.C., 1-14
Document type
conference paper