4.7 Article

LTP-Net: Life-Travel Pattern Based Human Mobility Signature Identification

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Information Systems

Metagraph-Based Life Pattern Clustering With Big Human Mobility Data

Wenjing Li et al.

Summary: Life pattern clustering is crucial for capturing the characteristics and regularity of daily life patterns. This study proposes a framework that efficiently identifies groups with similar life patterns based on millions of GPS records. The proposed method retains the original features of individual life pattern data and employs a metagraph-based data structure to capture the spatial-temporal similarity and diversity between individuals. Non-negative-factorization-based dimension reduction is used and the results show that our method effectively identifies similar life pattern groups, showcasing better computation efficiency and representational capacity compared to traditional methods. The insights gained from analyzing group characteristics can aid future urban and transportation planning, service improvement, and policy-making.

IEEE TRANSACTIONS ON BIG DATA (2023)

Article Computer Science, Artificial Intelligence

Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

Pengpeng Zhao et al.

Summary: In this paper, a novel unified neural network framework called NeuNext is proposed to assist next Point-of-Interest (POI) recommendation by joint learning of POI context prediction. Experimental results show that the proposed method outperforms other approaches in terms of Accuracy and MAP for next POI recommendation.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

Article Engineering, Civil

Understanding Urban Area Attractiveness Based on Private Car Trajectory Data Using a Deep Learning Approach

Zhu Xiao et al.

Summary: This paper explores the evolution of urban area attractiveness using private car trajectory data, utilizing deep learning and distribution models to analyze point-of-stop data, enhancing the understanding of urban AA. Empirical results demonstrate that the proposed method outperforms existing approaches in predicting urban AA, showing significant advantages.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Engineering, Electrical & Electronic

Exploring Human Mobility Patterns and Travel Behavior: A Focus on Private Cars

Zhu Xiao et al.

Summary: The increasing number of private cars has significantly impacted society and urban transportation systems. Existing research mainly focuses on trajectory data from floating cars, while studies on private car mobility and travel behaviors are still lacking. Understanding mobility patterns and hot zones in urban areas from people's ASL behavior is crucial for making decisions regarding urban network improvements. Further analysis using private car trajectory data can provide new research opportunities and challenges in this field.

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2022)

Proceedings Paper Computer Science, Information Systems

TrajFormer: Efficient Trajectory Classification with Transformers

Yuxuan Liang et al.

Summary: This paper introduces a novel transformer architecture named TrajFormer, which addresses the issues of regular interval assumptions and high computational costs for transformers in trajectory modeling. By generating continuous point embeddings, using a squeeze function to speed up representation learning, and introducing an auxiliary loss to ease training difficulties, TrajFormer achieves a preferable speed-accuracy balance compared to existing approaches.

PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022 (2022)

Article Computer Science, Information Systems

Detecting Home and Work Locations from Mobile Phone Cellular Signaling Data

Yingkun Yang et al.

Summary: This study proposed a new method for detecting home and work locations based on mobile network data, validated the algorithm using ground-truth data, and demonstrated the potential of using mobile network data for urban planning. The method improved the granularity and accuracy of location detection, showing a high correlation with census data.

MOBILE INFORMATION SYSTEMS (2021)

Article Engineering, Electrical & Electronic

Who is Driving? Event-Driven Driver Identification and Impostor Detection Through Support Vector Machine

Mussadiq Abdul Rahim et al.

IEEE SENSORS JOURNAL (2020)

Proceedings Paper Computer Science, Hardware & Architecture

Driver Identification Leveraging Single-turn Behaviors via Mobile Devices

Yan Wang et al.

2020 29TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN 2020) (2020)

Article Computer Science, Information Systems

Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data

Chengming Li et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2020)

Article Transportation

Hierarchical process of travel mode imputation from GPS data in a motorcycle-dependent area

Minh Hieu Nguyen et al.

TRAVEL BEHAVIOUR AND SOCIETY (2020)

Article Computer Science, Artificial Intelligence

Trajectory fingerprint: one-shot human trajectory identification using Siamese network

Zipei Fan et al.

CCF TRANSACTIONS ON PERVASIVE COMPUTING AND INTERACTION (2020)

Article Engineering, Electrical & Electronic

Improving Driver Identification for the Next-Generation of In-Vehicle Software Systems

Abdellah H. Mekki et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019)

Article Computer Science, Artificial Intelligence

Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder

Jie Chen et al.

APPLIED SOFT COMPUTING (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Spatio-Temporal GRU for Trajectory Classification

Hong-Bin Liu et al.

2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019) (2019)

Proceedings Paper Computer Science, Information Systems

Deep Multiple Instance Learning for Human Trajectory Identification

Zipei Fan et al.

27TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2019) (2019)

Article Engineering, Civil

Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier

Bao Wang et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)

Article Engineering, Civil

Investigations on Driver Unique Identification from Smartphone's GPS Data Alone

Arijit Chowdhury et al.

JOURNAL OF ADVANCED TRANSPORTATION (2018)

Article Computer Science, Artificial Intelligence

Recent advances in convolutional neural networks

Jiuxiang Gu et al.

PATTERN RECOGNITION (2018)

Article Psychology, Applied

Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks

Jasper S. Wijnands et al.

TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR (2018)

Article Engineering, Civil

On Developing a Driver Identification Methodology Using In-Vehicle Data Recorders

Luis Moreira-Matias et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2017)

Article Thermodynamics

Travel mode detection method based on big smartphone global positioning system tracking data

Chaoran Zhou et al.

ADVANCES IN MECHANICAL ENGINEERING (2017)

Article Computer Science, Information Systems

Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

Shan Jiang et al.

IEEE TRANSACTIONS ON BIG DATA (2017)

Proceedings Paper Computer Science, Information Systems

Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data

Tianran Hu et al.

CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT (2016)

Article Engineering, Civil

Discovering urban activity patterns in cell phone data

Peter Widhalm et al.

TRANSPORTATION (2015)

Article Transportation Science & Technology

Origin-destination trips by purpose and time of day inferred from mobile phone data

Lauren Alexander et al.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2015)

Article Computer Science, Information Systems

Understanding tourist behavior using large-scale mobile sensing approach: A case study of mobile phone users in Japan

Santi Phithakkitnukoon et al.

PERVASIVE AND MOBILE COMPUTING (2015)