Human Mobility Modeling with Limited Information via Large Language Models

Understanding human mobility patterns has traditionally been a complex challenge in transportation modeling. Due to the difficulties in obtaining high-quality training datasets across diverse locations, conventional activity-based models and learning-based human mobility modeling algorithms are particularly limited by the availability and quality of datasets. Furthermore, current research mainly focuses on the spatial-temporal travel pattern but lacks an understanding of the semantic information between activities, which is crucial for modeling the interdependence between activities. In this paper, we propose an innovative Large Language Model (LLM) empowered human mobility modeling framework. Our proposed approach significantly reduces the reliance on detailed human mobility statistical data, utilizing basic socio-demographic information of individuals to generate their daily mobility patterns. We have validated our results using the NHTS and SCAG-ABM datasets, demonstrating the effective modeling of mobility patterns and the strong adaptability of our framework across various geographic locations.

Deploying scalable traffic prediction models for efficient management in real-world large transportation networks during hurricane evacuations

Accurate traffic prediction is vital for effective traffic management during hurricane evacuation. This paper proposes a predictive modeling system that integrates Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) models to capture both long-term congestion patterns and short-term speed patterns. Leveraging various input variables, including archived traffic data, spatial-temporal road network information, and hurricane forecast data, the framework is designed to address challenges posed by heterogeneous human behaviors, limited evacuation data, and hurricane event uncertainties. Deployed in a real-world traffic prediction system in Louisiana, the model achieved an 82% accuracy in predicting long-term congestion states over a 6-hour period during a 7-day hurricane-impacted duration. The short-term speed prediction model exhibited Mean Absolute Percentage Errors (MAPEs) ranging from 7% to 13% across evacuation horizons from 1 to 6 hours. Evaluation results underscore the model's potential to enhance traffic management during hurricane evacuations, and real-world deployment highlights its adaptability and scalability in diverse hurricane scenarios within extensive transportation networks.

Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis

Human mobility significantly impacts various aspects of society, including transportation, urban planning, and public health. The increasing availability of diverse mobility data and advancements in deep learning have revolutionized mobility modeling. Existing deep learning models, however, mainly study spatio-temporal patterns using trajectories and often fall short in capturing the underlying semantic interdependency among activities. Moreover, they are also constrained by the data source. These two factors thereby limit their realism and adaptability, respectively. Meanwhile, traditional activity-based models (ABMs) in transportation modeling rely on rigid assumptions and are costly and time-consuming to calibrate, making them difficult to adapt and scale to new regions, especially those regions with limited amount of required conventional travel data. To address these limitations, we develop a novel generative deep learning approach for human mobility modeling and synthesis, using ubiquitous and open-source data. Additionally, the model can be fine-tuned with local data, enabling adaptable and accurate representations of mobility patterns across different regions. The model is evaluated on a nationwide dataset of the United States, where it demonstrates superior performance in generating activity chains that closely follow ground truth distributions. Further tests using state- or city-specific datasets from California, Washington, and Mexico City confirm its transferability. This innovative approach offers substantial potential to advance mobility modeling research, especially in generating human activity chains as input for downstream activity-based mobility simulation models and providing enhanced tools for urban planners and policymakers.

Semantic Trajectory Data Mining with LLM-Informed POI Classification

Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of semantic information show a limitation in both efficiency and accuracy. Semantic information, such as activity types inferred from Points of Interest (POI) data, can significantly enhance the quality of trajectory mining. However, integrating these insights is challenging, as many POIs have incomplete feature information, and current learning-based POI algorithms require the integrity of datasets to do the classification. In this paper, we introduce a novel pipeline for human travel trajectory mining. Our approach first leverages the strong inferential and comprehension capabilities of large language models (LLMs) to annotate POI with activity types and then uses a Bayesian-based algorithm to infer activity for each stay point in a trajectory. In our evaluation using the OpenStreetMap (OSM) POI dataset, our approach achieves a 93.4% accuracy and a 96.1% F-1 score in POI classification, and a 91.7% accuracy with a 92.3% F-1 score in activity inference.