Tatsuya Mitomi

Yahya Gamal, Ricardo Colasanti, Gary Polhill et al.

This paper introduces the Multi-Activity Transport & Mobility (MATraM) Agent-Based Model (ABM), a novel framework designed to advance activity-based transport modelling by incorporating dynamic activity adaptation. Traditional transport models simulate system performance using varying levels of abstraction, including flow-based, queue-based, and interaction-based mobility representations. While these approaches differ in their treatment of movement and congestion, they typically rely on pre-defined trip patterns that limit responsiveness to changing conditions. In particular, conventional activity-based models generate trips from fixed daily schedules, constraining their ability to capture behavioural flexibility and uncertainty. MATraM addresses this limitation by enabling agents to flag activities modification requests in response to sub-optimal travel conditions, such as increased travel times. By coupling with an activity scheduling and modification framework, the model integrates adaptive decision-making into the generation and execution of daily activity schedules. This allows for a more realistic representation of how individuals adjust their behaviour in response to transport system dynamics, leading to emergent mobility and congestion patterns. The ABM is presented following the ODD protocol, outlining its purpose, structure, and implementation. MATraM includes detailed representations of agents, their activity schedules, and the transport network, alongside submodels governing routing, scheduling, and behavioural adaptation. By bridging activity-based modelling with interaction-based mobility simulation, MATraM provides a flexible and extensible platform for exploring transport dynamics under uncertainty. This work contributes to the development of next-generation transport models capable of capturing the complex interplay between individual behaviour and system-level outcomes.

Fumiyasu Makinoshima, Tatsuya Mitomi, Fumiya Makihara et al.

Discrete choice models are essential for modelling various decision-making processes in human behaviour. However, the specification of these models has depended heavily on domain knowledge from experts, and the fully automated but interpretable modelling of complex human behaviours has been a long-standing challenge. In this paper, we introduce the differentiable discrete choice model (Diff-DCM), a fully data-driven method for the interpretable modelling, learning, prediction, and control of complex human behaviours, which is realised by differentiable programming. Solely from input features and choice outcomes without any prior knowledge, Diff-DCM can estimate interpretable closed-form utility functions that reproduce observed behaviours. Comprehensive experiments with both synthetic and real-world data demonstrate that Diff-DCM can be applied to various types of data and requires only a small amount of computational resources for the estimations, which can be completed within tens of seconds on a laptop without any accelerators. In these experiments, we also demonstrate that, using its differentiability, Diff-DCM can provide useful insights into human behaviours, such as an optimal intervention path for effective behavioural changes. This study provides a strong basis for the fully automated and reliable modelling, prediction, and control of human behaviours.

Tatsuya Mitomi, Fumiyasu Makinoshima, Fumiya Makihara et al.

Bike-sharing systems are emerging in various cities as a new ecofriendly transportation system. In these systems, spatiotemporally varying user demands lead to imbalanced inventory at bicycle stations, resulting in additional relocation costs. Therefore, it is essential to manage user demand through optimal dynamic pricing for the system. However, optimal pricing design for such a system is challenging because the system involves users with diverse backgrounds and their probabilistic choices. To address this problem, we develop a differentiable agent-based simulation to rapidly design dynamic pricing in bike-sharing systems, achieving balanced bicycle inventory despite spatiotemporally heterogeneous trips and probabilistic user decisions. We first validate our approach against conventional methods through numerical experiments involving 25 bicycle stations and five time slots, yielding 100 parameters. Compared to the conventional methods, our approach obtains a more accurate solution with a 73% to 78% reduction in loss while achieving more than a 100-fold increase in convergence speed. We further validate our approach on a large-scale urban bike-sharing system scenario involving 289 bicycle stations, resulting in a total of 1156 parameters. Through simulations using the obtained pricing policies, we confirm that these policies can naturally induce balanced inventory without any manual relocation. Additionally, we find that the cost of discounts to induce the balanced inventory can be minimized by setting appropriate initial conditions.