Human-Flow Digital Twin for Predicting the Effects of Mobility Introduction on Visitor Circulation

TL;DR

This paper introduces a human-flow digital twin framework utilizing a multi-agent simulator to predict how mobility measures affect visitor circulation, validated with real-world data from Wakayama Castle Park.

Contribution

It presents a novel digital twin approach that models visitor decision-making and predicts flow changes due to mobility interventions using machine learning.

Findings

Cosine similarity of spatial distribution exceeded 0.7 in simulations

The framework accurately reproduces flow changes caused by mobility measures

The multi-agent model effectively captures visitor destination choices

Abstract

We propose a framework for predicting the effects of mobility introduction measures using a human-flow digital twin. This digital twin incorporates a multi-agent simulator that can represent how visitors choose destinations depending on factors such as their current location and the attractiveness of spots. We extract data on how visitors selected destinations with respect to measured pre-intervention human-flow data, inter-spot distances, spot attractiveness, and travel volumes, and use these data to train each agent's decision model of this simulator. The trained decision model is a function that takes a visitor's current state and surrounding environmental information as input and outputs which spot the visitor will move toward next. By expressing mobility introduction measures as changes to inter-point distances or to spot attractiveness, the framework can reproduce human flows with mobility introduction in the multi-agent simulator and thereby quantify effects such as changes in visitor counts and circulation. We evaluated the proposed method using human-flow data measured with and without introducing mobility within Wakayama Castle Park in Japan. When reproducing flows with mobility introduction using a multi-layer perceptron decision model, the cosine similarity of the spatial population distribution exceeded 0.7, confirming that the approach can replicate the flow changes caused by the mobility introduction.