Enhancing parking simulations using peer-designed agents

In this paper, we investigate the usefulness of peer-designed agents (PDAs) as a turn-key technology for enhancing parking simulations. The use of PDAs improves the system's ability to capture the dynamics of the interaction between individuals in the system, each theoretically exhibiting a different strategic behavior. Furthermore, since people in general are inherently rational and computation bounded, simulating this domain becomes even more challenging. The advantage of PDAs in this context lies in their ability to reliably simulate a large pool of human individuals with diverse strategies and goals. We demonstrate the efficacy of the proposed method by developing a large-scale simulation system for the parking space search domain, which plays an important role in urban transport systems. The system is based on 34 different parking search strategies. Most of these strategies are substantially different from synthetic strategies that are used in prior literature. A quantitative analysis of the PDAs indicates that they reliably capture their designers' real-life strategies. Finally, we demonstrate the usefulness of PDA-based parking space search simulation by utilizing it to evaluate four different information technologies that are of increasing use in recent years.