Xiaojie Yang

Selected publications

1. CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events

   Xiaojie Yang , Hangli Ge, Jiawei Wang, Zipei Fan, Renhe Jiang, Ryosuke Shibasaki, and Noboru Koshizuka

   Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1 2025 | [ arXiv ]

   Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these patterns, they are often affected by nonperiodic public events, such as disasters and occasional celebrations. Since regular human mobility patterns are affected by these events, estimating their causal effects is critical to accurate mobility predictions. News articles provide unique perspectives on these events, though processing them is a challenge. In this study, we propose a causality based prediction model, CausalMob, to analyze the causal effects of public events. We first utilize large language models (LLMs) to extract human intentions from news and transform them into features that act as causal treatments. Next, the model learns representations of spatio-temporal regional covariates from multiple data sources to serve as confounders for causal inference. Finally, we present a causal effect estimation framework to ensure that event features remain independent of confounders during prediction. Based on large-scale real-world data, the experimental results show that the proposed model excels in human mobility prediction, outperforming state-of-the-art models.

2. FRTP: Federating Route Search Records to Enhance Long-term Traffic Prediction

   Hangli Ge, Xiaojie Yang , Itsuki Matsunaga, Dizhi Huang, and Noboru Koshizuka

   2024 IEEE International Conference on Big Data (BigData) 2024 | [ arXiv ]
3. Causality-Aware Next Location Prediction Framework based on Human Mobility Stratification

   Xiaojie Yang , Zipei Fan, Hangli Ge, Takashi Michikata, Ryosuke Shibasaki, and Noboru Koshizuka

   2024 IEEE Smart World Congress (SWC) 2024 | [ arXiv ]
4. LLeCaT: LLM Enhanced Causality-Aware Traffic Accidents Post-Effects Prediction

   Xiaojie Yang , Yicheng Tao, Hangli Ge, Zipei Fan, Rajendra Akerkar, and Noboru Koshizuka

   IEEE Transactions on Intelligent Transportation Systems 2025 | [ ]
5. Online trajectory prediction for metropolitan scale mobility digital twin

   Zipei Fan, Xiaojie Yang , Wei Yuan, Renhe Jiang, Quanjun Chen, Xuan Song, and Ryosuke Shibasaki

   Proceedings of the 30th International Conference on Advances in Geographic Information Systems 2022 | [ arXiv ]

   Knowing "what is happening" and "what will happen" of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that makes a virtual replication of human mobility and predicting or simulating the fine-grained movements of the subjects in a virtual space at a metropolitan scale in near real-time has shown its great potential in modern urban intelligent systems. However, few studies have provided practical solutions. The main difficulties are four-folds: 1) the daily variation of human mobility is hard to model and predict; 2) the transportation network enforces a complex constraints on human mobility; 3) generating a rational fine-grained human trajectory is challenging for existing machine learning models; and 4) making a fine-grained prediction incurs high computational costs, which is challenging for an online system. Bearing these difficulties in mind, in this paper we propose a two-stage human mobility predictor that stratifies the coarse and fine-grained level predictions. In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level. In the second stage, the coarse predictions are resolved to a fine-grained level via a probabilistic trajectory retrieval method, which offloads most of the heavy computations to the offline phase. We tested our method using a real-world mobile phone GPS dataset in the Kanto area in Japan, and achieved good prediction accuracy and a time efficiency of about 2 min in predicting future 1h movements of about 220K mobile phone users on a single machine to support more higher-level analysis of mobility prediction.