Article
Computer Science, Interdisciplinary Applications
Qiangqiang Xiong, Yaolin Liu, Peng Xie, Yiheng Wang, Yanfang Liu
Summary: This study extracts individual daily activity-travel patterns from massive mobile phone network data, revealing the complex relationship of LAMs between workdays and day-offs, and identifying the formation mechanism of correlation patterns.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Article
Engineering, Civil
Andreas Dypvik Landmark, Petter Arnesen, Carl-Johan Sodersten, Odd Andre Hjelkrem
Summary: The widespread use of personal cell phones has sparked interest in utilizing Big Data generated by mobile phones in transportation research. However, constructing OD matrices from mobile phone data faces challenges and legal restrictions. Pre-compiled OD matrices may lack reliability due to data coarseness and privacy concerns.
Article
Green & Sustainable Science & Technology
Xuesong Gao, Hui Wang, Lun Liu
Summary: The paper discusses the transformation of individual data-based mobility metrics to fit with grid-aggregate data, proposing fifteen candidate metrics measuring five indicators of mobility and selecting the most suitable one for each indicator. Future research on aggregate-level mobility data can refer to the analysis in this paper to help select suitable mobility metrics.
Article
Transportation Science & Technology
Cuauhtemoc Anda, Sergio A. Ordonez Medina, Kay W. Axhausen
Summary: Mobile phone data has the potential to improve urban transportation planning, but concerns about high dimensionality and privacy risks limit its applications. To address this, we propose a framework using user-aggregated mobile phone data to synthesize realistic daily individual mobility while complying with data privacy regulations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Computer Science, Interdisciplinary Applications
Yang Xu, Jiaying Xue, Sangwon Park, Yang Yue
Summary: The study introduces an analytical framework to gain insights into tourist mobility patterns by analyzing mobile phone trajectories of international travelers to three different cities in South Korea, utilizing nine mobility indicators to capture different facets of tourist travel behavior. The study further quantifies the behavioral heterogeneity of travelers across countries and regions, and examines spatial activity patterns of different traveler groups in each city.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Article
Urban Studies
Ling Yin, Nan Lin, Zhiyuan Zhao
Summary: This study proposes a method to mine human activity chains from large-scale mobile phone location data by integrating spatial and temporal features, finding that the frequency distribution of major activity chain patterns agrees well with patterns derived from travel surveys and advanced methods.
Article
Engineering, Multidisciplinary
Hongbo Jiang, Yu Zhang, Zhu Xiao, Ping Zhao, Arun Iyengar
Summary: This study delves into people's travel behavior using private car trajectory data, finding that private car data align better with travel habits, have lower entropy values, and can better describe travel behavior. The unique metric of dwell time in private car trajectory data further aids in understanding travel behavior. The study also introduces a fine-grained destination prediction algorithm that enhances personalized prediction of final destinations for private cars.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Civil
Yulang Huang, Dianhai Wang, Wang Xu, Zhengyi Cai, Fengjie Fu
Summary: This paper introduces an innovative map matching method for mobile phone signaling data (MSD), leveraging an incremental Hidden Markov Model (HMM) algorithm. The proposed method addresses several challenges associated with MSD, and achieves better accuracy in extracting people's travel trajectories and providing optimal paths on digital maps, compared to existing approaches.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Transportation
Younshik Chung, Sanggi Nam
Summary: This study explores the concept of travel time expenditure using mobile phone signaling data and achieves better spatiotemporal precision than traditional household survey data. However, collecting and cleaning the raw data requires tremendous effort.
TRAVEL BEHAVIOUR AND SOCIETY
(2024)
Article
Computer Science, Artificial Intelligence
Mohd Yousuf Ansari, Mainuddin, Amir Ahmad, Gopal Bhushan
Summary: Spatial technologies generate large datasets quickly and continuously. The study developed a clustering algorithm to mine spatiotemporal co-location events in trajectory datasets, dividing trajectories into line segments and grouping them based on spatial and temporal aspects. Entropy and silhouette index were adopted to validate clusters, with experiments showing the algorithm effectively discovering optimal clusters. The algorithm outperformed other existing algorithms in discovering hidden and useful clusters.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Transportation Science & Technology
Loic Bonnetain, Angelo Furno, Nour-Eddin El Faouzi, Marco Fiore, Razvan Stanica, Zbigniew Smoreda, Cezary Ziemlicki
Summary: This study introduces a novel framework called TRANSIT, capable of accurately inferring mobility phases and stationary activities from Network Signaling Data, and reconstructing fine-grained human mobility trajectories. Validation results demonstrate the superior performance of TRANSIT in identifying movement periods and trajectory estimation.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Green & Sustainable Science & Technology
Wenjing Wang, Yanyan Chen, Haodong Sun, Yusen Chen
Summary: Observing and analyzing travel behavior is crucial, and this paper proposes a method to identify travel modes using location data from an Internet company and travel data from transport department. By utilizing a multiple binary classification model, the accuracy of the model significantly improved, showing potential applications in engineering practice and decision support for transportation planners.
Article
Plant Sciences
Yong Liu, Aqing Lu, Wei Yang, Zongshun Tian
Summary: Quantifying residents' utilization of parks and understanding the driving factors behind it are crucial for optimizing parks and promoting human well-being. Based on mobile phone signaling data, this study combines visit flows and duration analysis to investigate the spatiotemporal disparities and the influence of park environmental factors on park visit behaviors in Chengdu, China. The findings highlight the significant disparities between visit flows and duration, and identify key factors such as park area, facilities, distance to the city center, and surrounding residents that affect park visit behaviors.
URBAN FORESTRY & URBAN GREENING
(2023)
Article
Geography
Zongshun Tian, Qin Wang, Yong Liu, Ziyue Wang
Summary: Understanding tourist mobility patterns of demographic subgroups is crucial for tourism planning and management. By proposing a multidimensional framework and conducting an empirical study, we revealed differences in mobility indicators, temporal patterns, and spatial patterns among different demographic subgroups.
Article
Development Studies
Marian Halas, Pavel Klapka
Summary: This paper develops the concept of a city's timescape through empirical analysis of geospatial big data. The timescape represents the temporal shape of a city, reflecting its dynamics and rhythms based on aggregated time-space behavior of individuals. The study uses rhythmanalysis and spatial interactions as theoretical foundations, and analyzes mobile phone location data and taxi trajectory data from Prague. The findings reveal distinct differences between workdays and weekends, as well as specific temporal distributions of mobility based on taxi trajectories. The research identifies two hierarchically different time waves, long and short, based on 'extra' urban flows and the present population of the city.
HABITAT INTERNATIONAL
(2023)
Article
Green & Sustainable Science & Technology
Jin-Ki Eom, Kwang-Sub Lee, Sangpil Ko, Jun Lee
Summary: Smart cities have emerged as a promising solution to address urban problems and achieve sustainable development. The Sejong smart city project, with its unique transportation design concept and central role of bus rapid transit (BRT), plays a significant role. This study analyzed mode choices of individual travelers and found associations with personal characteristics.
