Destination choice modeling is a critical task in human mobility analysis, serving as the foundation for applications from urban planning to personalized travel services. Existing approaches often rely on individual trajectories or static surveys, limiting their adaptability and interpretability in complex urban settings. To address these issues, we propose KnowDest, a knowledge graph-based recommendation framework for destination choice modeling. Experiments on real-world data from Susono, Japan show that KnowDest improves Recall@10, Precision@10, and NDCG@10 by over 30% on average compared to baselines. Moreover, it offers structural and attribute-level explanations by explicitly identifying influential factors such as POI distributions or regional properties that influence travel decisions. Our approach presents a novel perspective on high-precision human mobility prediction, highlighting the potential of knowledge graph to support scalable, interpretable, and transferable modeling across varied urban contexts.

Knowledge Graphs (KGs) have recently emerged as a powerful tool for extracting directed multi-relational "knowledge" from structured facts within massive urban mobility data, supporting various urban application scenarios. However, current methods for constructing urban knowledge graphs (UrbanKGs), which rely on static data such as points of interest (POI) and road networks, fail to capture the dynamic relations that evolve over time. In this paper, we address this limitation by constructing a dynamic urban knowledge graph using heterogeneous spatio-temporal urban data to uncover deeper "knowledge". First, spatial features and their geographical relationships are extracted as entities and relations to build the UrbanKG. Next, timestamps are integrated from regional origin-destination (OD) data to form a dynamic UrbanKG. Finally, experimental results demonstrate that the dynamic models significantly outperform the static models in the knowledge graph completion (KGC) task. This confirms that incorporating temporal dynamics into UrbanKGs provides a more robust framework for capturing the complexities of urban transportation and improving urban management strategies.

Urban daily activity patterns play an important role in fields such as urban planning and traffic management, while the powerful data generation and reasoning capabilities of LLMs (Large Language Models) have sparked a surge of interest in recent years, with applications in various domains. Inspired by their natural language processing and pattern recognition functionalities, we attempted to utilize LLMs to simulate the activity patterns of people of different ages and occupations in metropolitan areas (Tokyo). By leveraging pre-processed Person Trip data, we employed 3 methods to test the ability of LLMs to generate urban daily activity data. The results were evaluated based on metrics such as rationality, diversity, and error rates. Results indicate that the fine-tuned LLaMA-3 model is capable of accurately simulating the activity distribution patterns of metropolitan populations. Among the prompt-based strategies, the few-shot approach yielded the best performance. Although designing the instruction for prompts and post-processing the data required considerable time, the few-shot prompt strategy proved to be an effective option for this task.

Large-scale commuting flow prediction is an essential task to estimate the commuting origin-destination (OD) demand within within a prefecture or the whole nation using multiple auxiliary data. Considering ranked structures of metropolitan areas and increased number of geographical units that need to be maintained, we develop a heterogeneous graph-based model to generate meaningful region embeddings at multiple spatial resolutions for predicting different types of inter-level OD flows. To demonstrate the effectiveness of the proposed method, extensive experiments were conducted using real-world aggregated mobile phone datasets collected from Shizuoka Prefecture, Japan. The results indicate that our proposed model outperforms existing models in terms of a uniform urban structure. We extend the understanding of predicted results using reasonable explanations to enhance the credibility of the model.

Human mobility generation plays a critical role in urban transportation planning. Existing human mobility generation models often fall short of understanding travelers' demographics and integrating multimodal information, including activity purposes, destination choices and transport mode preferences. Recently, mobility generation models leveraging Large Language Models (LLMs) have gained significant attention, while they are limited in directly reproducing spatial information in human mobility profiles. To address these challenges, this paper proposes the Mobility Generative Language Model (MobGLM), a novel approach for generating synthetic human mobility data to support urban planning, transport management, energy consumption and epidemic control. MobGLM addresses these limitations by capturing the complex relationships between agents' mobility patterns and individual demographics. By incorporating personal information, activity types, locations and traffic modes as encoders, MobGLM uniquely identifies and replicates features of human mobility. Our framework is evaluated using a large, real-world mobility dataset and benchmarked against state-of-the-art personal mobility generation techniques. The results demonstrate the effectiveness of MobGLM in producing accurate and reliable synthetic mobility data, highlighting its potential applications in various urban mobility contexts.

Origin–destination (OD) flow d ata a re c ritical for urban planning and traffic system design. Such data are suitable for describing movement at the macroscopic level. However, collecting them on a large scale, such as in a city, is challenging. Their form incompatibility makes using them for other tasks difficult. Therefore, we propose a deep model to learn meaningful OD information on grids within a city to address these problems. We collected multimodal characteristics of regions, such as road network densities and facility distributions, from several open-source datasets and used them as grid signals. We then constructed a spatial attention-based deep graph network to generate grid embeddings and used them to predict the OD volumes. The proposed method was evaluated against a set of baseline approaches using a real-world dataset in Japan. The analysis indicated that our model can extract more accurate latent topographical information from OD graphs and produce reasonable grid embeddings; these representations apply to other downstream tasks.

With the rapid trend of developing Smart Cities and Digital Twins, a better understanding of how humans move and perform a daily routine in the city area is vital. Benefiting from the recent rapidly growing location acquisition techniques, existing deep learning approaches can effectively model and predict human mobility with human mobility big data. However, it is still challenging to simulate human mobility at the population level because only the individuals with plentiful historical data can be well modeled. Moreover, the differences in complex city layouts and functions prevent us from applying the trained models to different cities. Therefore, in this study, we propose an alternative deep learning framework focusing on the destination choice to reconstruct nationwide human mobility at the population level. We design a new embedding mechanism for handling the traveler’s demographics, mobility characteristics, travel intention, and context information of locations to enrich the representations for the model prediction. Then a neural network is trained with the People Flow Dataset. We evaluated our approach based on multiple urban areas using different training data and demonstrated the advantages of our method compared with other baseline approaches.

A real-world traffic simulation can help better understand the need for infrastructure facilities in a region. Such simulations require digitized infrastructure information, well-represented people movement data, and efficient computing resources. Advances in computing resources and agent-based traffic simulators have made it possible to simulate real-world traffic conditions in a simulated environment. Digitized infrastructure datasets of roads, buildings, and other infrastructure facilities have enabled comprehensive visualization of infrastructure. However, the availability and accessibility of well-represented people’s mobility data are still part of some research. Additionally, due to resource limitations, traffic simulations are typically limited to geographic regions and use samples of traffic data rather than the entire population.This paper uses a novel mobility dataset, open Pseudo-PFLOW, which is a whole representation of the entire population of Japan, for a full-scale traffic modeling of Chiba prefecture. We used Multi-Agent Traffic Simulation (MATSim) tool, as an agent-based simulator. This has previously proven efficient for simulating large scenarios. The main research question focuses on improving agent trajectory data from the Pseudo-PFLOW dataset by agent-based modeling and validating the effectiveness of MATSim on network congestion and resource requirements of full-scale simulations.

In recent years, due to various factors, including population decline, aging, and the promotion of compact cities, the social and lifestyle changes significantly impact people’s daily travel behavior. Although existing survey data and mobile phone data can reveal these phenomena, only aggregate-level results can be open to the public from the viewpoint of personal privacy. On the other hand, the conventional four-step travel demand estimation approach and human mobility models are limited to the low degree of freedom models, and hard to estimate the ever-changing traffic flow and the inability to capture the continuous behavior of people. This study develops a deep reinforcement learning-based agent model to tackle this problem and simulate the intercity people flow in the metropolitan area.

Human mobility datasets collected from various sources are indispensable for analyzing, predicting, and solving emerging urbanization and population issues. However, such datasets are only available to the public after aggregation and anonymous processing. In recent years, agent-based modeling approaches have addressed this problem by reproducing synthetic human mobility data through simulation. However, the development of such agent models typically requires a large amount of personal location histories as training data for parameter learning, leading to cost and privacy concerns. To overcome this disadvantage, we attempted to explore optimal parameters using a particle filter to alleviate the strict requirement of the data. We tested our method in a local city in Japan using aggregated real-time observation data collected from mobile phone service companies. The results show that the proposed model can achieve satisfactory accuracy using low-resolution data and can therefore be easily used by local governments for municipal applications.

Agent-based simulations, combined with large scale mobility data, have been an effective method for understanding urban scale human dynamics. However, collecting such large scale human mobility datasets are especially difficult during rare events (e.g., natural disasters), reducing the performance of agent-based simulations. To tackle this problem, we develop an agent-based model that can simulate urban dynamics during rare events by learning from other cities using inverse reinforcement learning. More specifically, in our framework, agents imitate real human-beings' travel behavior from areas where rare events have occurred in the past (source area) and produce synthetic people movement in different cities where such rare events have never occurred (target area). Our framework contains three main stages: 1) recovering the reward function, where the people's travel patterns and preferences are learned from the source areas; 2) transferring the model of the source area to the target areas; 3) simulating the people movement based on learned model in the target area. We apply our approach in various cities for both normal and rare situations using real-world GPS data collected from more than 1 million people in Japan, and show higher simulation performance than previous models.

This paper is the first work to replicate and simulate urban dynamics by learning individuals' decision-making processes and creating human-like agents from GPS data. We develop a novel agent model by learning from historical data via reinforcement learning techniques. We test our methodology in different scenarios at the citywide level using real world smartphone GPS data. Simulation results show that our agents can successfully learn and generate human-like travel activities. Furthermore, the performance of synthetic urban dynamics significantly outperforms existing methods.