In this paper, we investigate the suitability of state-of-the-art representation learning methods to the analysis of behavioral similarity of moving individuals, based on CDR trajectories. The core of the contribution is a novel methodological framework, mob2vec, centered on the combined use of a recent symbolic trajectory segmentation method for the removal of noise, a novel trajectory generalization method incorporating behavioral information, and an unsupervised technique for the learning of vector representations from sequential data. Mob2vec is the result of an empirical study conducted on real CDR data through an extensive experimentation. As a result, it is shown that mob2vec generates vector representations of CDR trajectories in low dimensional spaces which preserve the similarity of the mobility behavior of individuals.
University of Southern California Information Sciences Institute（南加州大学信息科学研究所）
Multiplayer online battle arena has become a popular game genre. It also received increasing attention from our research community because they provide a wealth of information about human interactions and behaviors. A major problem is extracting meaningful patterns of activity from this type of data, in a way that is also easy to interpret. Here, we propose to exploit tensor decomposition techniques, and in particular Non-negative Tensor Factorization, to discover hidden correlated behavioral patterns of play in a popular game: League of Legends. We first collect the entire gaming history of a…
Massachusetts Institute of Technology（麻省理工学院）；The University of Hong Kong（香港大学）；Northeastern University（东北大学）
Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns. To address this issue, this study develops an activity-based modeling framework for individual mobility prediction. Specifically, an input-output hidden Markov model (IOHMM) framework is proposed to simultaneously predict the (continuous) time and (discrete) location of an individual's next trip using transit smart card data. The prediction task can be transformed into predicting the hidden activity duration a…
Extracting significant places or places of interest (POIs) using individuals' spatio-temporal data is of fundamental importance for human mobility analysis. Classical clustering methods have been used in prior work for detecting POIs, but without considering temporal constraints. Usually, the involved parameters for clustering are difficult to determine, e.g., the optimal cluster number in hierarchical clustering. Currently, researchers either choose heuristic values or use spatial distance-based optimization to determine an appropriate parameter set. We argue that existing research does not o…
University of Southern California（南加州大学）；Information Sciences Institute（信息科学研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an individual's behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual, and insightful assessments of individual performance and wellbeing? Prediction of different aspects of human behavior from these noisy, incomplete, and heterogeneous bio-behavioral temporal data is a challenging problem, beyond unsupervised discovery of latent structures. We propose…
MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility Generation
The University of Osaka（大阪大学）；The University of Tokyo（东京大学）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Science Tokyo（东京科学大学）
Georgia Institute of Technology（佐治亚理工学院）；University of Michigan（密歇根大学）
Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional logit models (e.g., multinomial logit). However, little research focuses on comparing the interpretability of machine learning with logit models. In other words, how to draw behavioral insights from the high-performance "black-box" machine-learning models remains largely unsolved…
University of Tours（图尔大学）；LABISEN（LABISEN实验室）；Yncrea Ouest（西部伊克雷亚集团）
Various institutes produce large semantic datasets containing information regarding daily activities and human mobility. The analysis and understanding of such data are crucial for urban planning, socio-psychology, political sciences, and epidemiology. However, none of the typical data mining processes have been customized for the thorough analysis of semantic mobility sequences to translate data into understandable behaviors. Based on an extended literature review, we propose a novel methodological pipeline called simba (Semantic Indicators for Mobility and Behavior Analysis), for mining and …
University of Minnesota, Twin Cities（明尼苏达大学双城分校）；University of Southern California（南加州大学）
Human mobility clustering is an important problem for understanding human mobility behaviors (e.g., work and school commutes). Existing methods typically contain two steps: choosing or learning a mobility representation and applying a clustering algorithm to the representation. However, these methods rely on strict visiting orders in trajectories and cannot take advantage of multiple types of mobility representations. This paper proposes a novel mobility clustering method for mobility behavior detection. First, the proposed method contains a permutation-equivalent operation to handle sub-traje…
MobilityGPT: Enhanced Human Mobility Modeling with a GPT model
Department of Electrical and Computer Engineering, University of California, Davis（电子与计算机工程系，加州大学戴维斯分校）；Apple Inc.（苹果公司）；Department of Civil and Environmental Engineering, University of California, Davis（土木与环境工程系，加州大学戴维斯分校）
Understanding human mobility is essential for the development of smart cities and social behavior research. Human mobility models may be used in numerous applications, including pandemic control, urban planning, and traffic management. The existing models' accuracy in predicting users' mobility patterns is less than 25%. The low accuracy may be justified by the flexible nature of the human movement. Indeed, humans are not rigid in their daily movement. In addition, the rigid mobility models may result in missing the hidden regularities in users' records. Thus, we propose a novel perspective to…
Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of predicting citywide human mobility via a simpler solution. A spatio-temporal mobility event prediction framework based on a single fully-convolutional residual network (STAR) is proposed. STAR is a highly simple, general and effective method for learning a single tensor representin…
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, China（复杂管理系统的计算与分析实验室，天津大学，中国）；College of Management and Economics, Tianjin University, China（管理与经济学院，天津大学，中国）；Faculty of Science and Engineering, University of Nottingham Ningbo China（科学与工程学院，诺丁汉大学宁波校区，中国）
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
College of Computer Science, Zhejiang University（浙江大学计算机学院）；College of Software Technology, Zhejiang University（浙江大学软件技术学院）；State Key Laboratory of Blockchain and Security, Zhejiang University（浙江大学区块链与安全国家重点实验室）
Fondazione Bruno Kessler (FBK)（布鲁诺·凯斯勒基金会（FBK））；Free University of Bolzano（博尔扎诺自由大学）；Amazon Alexa（亚马逊Alexa）；Institute of Information Science and Technologies, National Research Council (ISTI-CNR)（国家研究委员会信息科学与技术研究所（ISTI-CNR））
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides …
Are LLM Agents Behaviorally Coherent? A Latent Profiling Approach for Social Simulation
Department of Computer Science and Engineering, University of Minnesota（明尼苏达大学计算机科学与工程系）；Department of African American & African Studies, University of Minnesota（明尼苏达大学非裔美国人与非洲研究系）；Department of Sociology, University of Chicago（芝加哥大学社会学系）
Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models
School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；Beijing Key Laboratory of Traffic Data Analysis and Mining（北京市交通数据分析与挖掘重点实验室）；Department of Computer Science, Aalborg University（奥尔堡大学计算机系）
