Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

CellTrans: Private Car or Public Transportation? Infer Users' Main Transportation Modes at Urban Scale with Cellular Data

Published: 09 September 2019 Publication History

Abstract

Understanding citizens' main transportation modes at urban scale is beneficial to a range of applications, such as urban planning, user profiling, transportation management, and precision marketing. Previous methods on mode inference are mostly focused on utilizing GPS data with high spatiotemporal granularity. However, due to high costs of GPS data collection, the previous work typically is in small scales. In contrast, the cellular data logging interactions between cellphone users and cell towers cover much higher population given the ubiquity of cellphones. Nevertheless, utilizing cellular data introduces new challenges given their low spatiotemporal granularity compared to GPS data. In this paper, we design CellTrans, a novel framework to survey users' main transportation modes (public transportation or private car) at urban scale with cellular data. CellTrans extracts various mobility features that are pertinent to users' main transportation modes and presents solutions for different application scenarios including when there are no labeled users in the studied cities. We evaluate CellTrans on two real-world large-scale cellular datasets covering 3 million users, among which 2,589 users are with labels. We assess our method not only quantitatively with labeled users, but also qualitatively with the whole population. The experiments show that CellTrans infers users' main transportation modes with accuracy over 80% (with a performance gain of 20% compared to state-of-the-art), and CellTrans remains effective when applied at urban scale to the whole population.

References

[1]
Lauren Alexander, Shan Jiang, Mikel Murga, and Marta C. González. 2015. Origin-destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies 58 (2015), 240--250.
[2]
Amap. 2019. Amap Open Platform. Retrieved May 11, 2019 from https://lbs.amap.com/
[3]
Francesco Calabrese, Laura Ferrari, and Vincent D. Blondel. 2014. Urban Sensing Using Mobile Phone Network Data: A Survey of Research. ACM Comput. Surv. 47, 2, Article 25 (Nov. 2014), 20 pages.
[4]
Ke-Yu Chen, Rahul C. Shah, Jonathan Huang, and Lama Nachman. 2017. Mago: Mode of Transport Inference Using the Hall-Effect Magnetic Sensor and Accelerometer. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 2, Article 8 (June 2017), 23 pages.
[5]
Sina Dabiri and Kevin Heaslip. 2018. Inferring transportation modes from GPS trajectories using a convolutional neural network. Transportation Research Part C: Emerging Technologies 86 (2018), 360--371.
[6]
M. G. Demissie, S. Phithakkitnukoon, and L. Kattan. 2018. Trip Distribution Modeling Using Mobile Phone Data: Emphasis on Intra-Zonal Trips. IEEE Transactions on Intelligent Transportation Systems (2018), 1--13.
[7]
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A Density-based Algorithm for Discovering Clusters a Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, 226--231. http://dl.acm.org/citation.cfm?id=3001460.3001507
[8]
Zhihan Fang, Fan Zhang, Ling Yin, and Desheng Zhang. 2018. MultiCell: Urban Population Modeling Based on Multiple Cellphone Networks. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3, Article 106 (Sept. 2018), 25 pages.
[9]
L. Ferrari, M. Mamei, and M. Colonna. 2012. People get together on special events: Discovering happenings in the city via cell network analysis. In 2012 IEEE International Conference on Pervasive Computing and Communications Workshops. 223--228.
[10]
H. Gjoreski, M. Ciliberto, L. Wang, F. J. Ordonez Morales, S. Mekki, S. Valentin, and D. Roggen. 2018. The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics With Mobile Devices. IEEE Access 6 (2018), 42592--42604.
[11]
Marta C. González, César A. Hidalgo, and Albert-László Barabási. 2008. Understanding individual human mobility patterns. Nature 453 (2008), 779.
[12]
Samuli Hemminki, Petteri Nurmi, and Sasu Tarkoma. 2013. Accelerometer-based Transportation Mode Detection on Smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys '13). ACM, New York, NY, USA, Article 13, 14 pages.
[13]
Sibren Isaacman, Richard Becker, Ramón Cáceres, Stephen Kobourov, Margaret Martonosi, James Rowland, and Alexander Varshavsky. 2011. Identifying Important Places in People's Lives from Cellular Network Data. In Pervasive Computing, Kent Lyons, Jeffrey Hightower, and Elaine M. Huang (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 133--151.
[14]
Jeya Vikranth Jeyakumar, Eun Sun Lee, Zhengxu Xia, Sandeep Singh Sandha, Nathan Tausik, and Mani Srivastava. 2018. Deep Convolutional Bidirectional LSTM Based Transportation Mode Recognition. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp '18). ACM, New York, NY, USA, 1606--1615.
[15]
S. Jiang, J. Ferreira, and M. C. Gonzalez. 2017. Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. IEEE Transactions on Big Data 3, 2 (June 2017), 208--219.
[16]
Yu Jin, Nick Duffield, Alexandre Gerber, Patrick Haffner, Wen-Ling Hsu, Guy Jacobson, Subhabrata Sen, Shobha Venkataraman, and Zhi-Li Zhang. 2012. Characterizing Data Usage Patterns in a Large Cellular Network. In Proceedings of the 2012 ACM SIGCOMM Workshop on Cellular Networks: Operations, Challenges, and Future Design (CellNet '12). ACM, New York, NY, USA, 7--12.
[17]
Kevin S. Kung, Kael Greco, Stanislav Sobolevsky, and Carlo Ratti. 2014. Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data. PLOS ONE 9, 6 (06 2014), 1--15.
[18]
G. Lan, W. Xu, S. Khalifa, M. Hassan, and W. Hu. 2016. Transportation mode detection using kinetic energy harvesting wearables. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). 1--4.
[19]
Guanyao Li, Chun-Jie Chen, Sheng-Yun Huang, Ai-Jou Chou, Xiaochuan Gou, Wen-Chih Peng, and Chih-Wei Yi. 2017. Public Transportation Mode Detection from Cellular Data. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM '17). ACM, New York, NY, USA, 2499--2502.
[20]
Jonathan Liono, Zahraa S. Abdallah, A. K. Qin, and Flora D. Salim. 2018. Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life. In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous '18). ACM, New York, NY, USA, 342--351.
[21]
Tongtong Liu, Zheng Yang, Yi Zhao, Chenshu Wu, Zimu Zhou, and Yunhao Liu. 2018. Temporal understanding of human mobility: A multi-time scale analysis. PLOS ONE 13, 11 (2018), e0207697.
[22]
D. Naboulsi, M. Fiore, S. Ribot, and R. Stanica. 2016. Large-Scale Mobile Traffic Analysis: A Survey. IEEE Communications Surveys Tutorials 18, 1 (Firstquarter 2016), 124--161.
[23]
Dalian Municipal Bureau of Statistics. 2017. 2016 Statistical Communique of National Economic and Social Development in Dalian. Retrieved January 26, 2019 from http://www.stats.dl.gov.cn/index.php?m=content&c=index&a=show&catid=52&id=12000
[24]
Shenyang Municipal Bureau of Statistics. 2017. 2016 Statistical Communique of National Economic and Social Development in Shenyang. Retrieved January 26, 2019 from http://www.shenyang.gov.cn/zwgk/system/2017/09/14/010193052.shtml
[25]
Dalian Xinshang Newspaper Office. 2017. There needs more than 1.04 million parking berths for Dalian's 1.52 million vehicles. Retrieved January 26, 2019 from http://dl.sina.com.cn/news/m/2017-01-10/detail-ifxzkfuh6518304.shtml
[26]
Santi Phithakkitnukoon, Zbigniew Smoreda, and Patrick Olivier. 2012. Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data. PLOS ONE 7, 6 (06 2012), 1--9.
[27]
Santi Phithakkitnukoon, Titipat Sukhvibul, Merkebe Demissie, Zbigniew Smoreda, Juggapong Natwichai, and Carlos Bento. 2017. Inferring social influence in transport mode choice using mobile phone data. EPJ Data Science 6, 1 (14 Jun 2017), 11.
[28]
Ling Qi, Yuanyuan Qiao, Fehmi Ben Abdesslem, Zhanyu Ma, and Jie Yang. 2016. Oscillation Resolution for Massive Cell Phone Traffic Data. In Proceedings of the First Workshop on Mobile Data (MobiData '16). ACM, New York, NY, USA, 25--30.
[29]
Chaoming Song, Tal Koren, Pu Wang, and Albert-László Barabási. 2010. Modelling the scaling properties of human mobility. Nature Physics 6 (2010), 818.
[30]
Xuan Song, Hiroshi Kanasugi, and Ryosuke Shibasaki. 2016. Deeptransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI'16). AAAI Press, 2618--2624. http://dl.acm.org/citation.cfm?id=3060832.3060987
[31]
Leon Stenneth, Ouri Wolfson, Philip S. Yu, and Bo Xu. 2011. Transportation Mode Detection Using Mobile Phones and GIS Information. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS '11). ACM, New York, NY, USA, 54--63.
[32]
Ritiz Tambi, Paul Li, and Jun Yang. 2018. An Efficient CNN Model for Transportation Mode Sensing. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems (SenSys '18). ACM, New York, NY, USA, 315--316.
[33]
Toan H. Vu, Le Dung, and Jia-Ching Wang. 2016. Transportation Mode Detection on Mobile Devices Using Recurrent Nets. In Proceedings of the 24th ACM International Conference on Multimedia (MM '16). ACM, New York, NY, USA, 392--396.
[34]
B. Wang, L. Gao, and Z. Juan. 2018. Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier. IEEE Transactions on Intelligent Transportation Systems 19, 5 (May 2018), 1547--1558.
[35]
H. Wang, F. Calabrese, G. Di Lorenzo, and C. Ratti. 2010. Transportation mode inference from anonymized and aggregated mobile phone call detail records. In 13th International IEEE Conference on Intelligent Transportation Systems. 318--323.
[36]
Huandong Wang, Fengli Xu, Yong Li, Pengyu Zhang, and Depeng Jin. 2015. Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment. In Proceedings of the 2015 Internet Measurement Conference (IMC '15). ACM, New York, NY, USA, 225--238.
[37]
Lin Wang, Hristijan Gjoreskia, Kazuya Murao, Tsuyoshi Okita, and Daniel Roggen. 2018. Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp '18). ACM, New York, NY, USA, 1521--1530.
[38]
X. Wang, Z. Zhou, Z. Yang, Y. Liu, and C. Peng. 2017. Spatio-temporal analysis and prediction of cellular traffic in metropolis. In 2017 IEEE 25th International Conference on Network Protocols (ICNP). 1--10.
[39]
Wikipedia. {n.d.}. Support-vector Machine. https://en.wikipedia.org/wiki/Support-vector_machine#Linear_SVM
[40]
Fang Tian Xia. 2019. Fang Tian Xia. Retrieved January 26, 2019 from https://sy.fang.com/
[41]
Dafeng Xu, Guojie Song, Peng Gao, Rongzeng Cao, Xinwei Nie, and Kunqing Xie. 2011. Transportation Modes Identification from Mobile Phone Data Using Probabilistic Models. In Advanced Data Mining and Applications, Jie Tang, Irwin King, Ling Chen, and Jianyong Wang (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 359--371.
[42]
Fengli Xu, Pengyu Zhang, and Yong Li. 2016. Context-aware Real-time Population Estimation for Metropolis. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '16). ACM, New York, NY, USA, 1064--1075.
[43]
P. Yang, T. Zhu, X. Wan, and X. Wang. 2014. Identifying Significant Places Using Multi-day Call Detail Records. In 2014 IEEE 26th International Conference on Tools with Artificial Intelligence. 360--366.
[44]
Su Yang, Minjie Wang, Wenshan Wang, Yi Sun, Jun Gao, Weishan Zhang, and Jiulong Zhang. 2017. Predicting Commercial Activeness over Urban Big Data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 119 (Sept. 2017), 20 pages.
[45]
Paul A Zandbergen. 2009. Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning. Transactions in GIS 13, s1 (2009), 5--25.
[46]
X. Zhang, Z. Yang, Y. Liu, and S. Tang. 2019. On Reliable Task Assignment for Spatial Crowdsourcing. IEEE Transactions on Emerging Topics in Computing 7, 1 (Jan 2019), 174--186.
[47]
Xinglin Zhang, Zheng Yang, Wei Sun, Yunhao Liu, Shaohua Tang, Kai Xing, and Xufei Mao. 2016. Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys & Tutorials 18, 1 (2016), 54--67.
[48]
Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Yunhao Liu, and Zheng Yang. 2019. CellTradeMap: Delineating Trade Areas for Urban Commercial Districts with Cellular Networks. In IEEE International Conference on Computer Communications (INFOCOM 2019).
[49]
Yu Zheng, Yukun Chen, Quannan Li, Xing Xie, and Wei-Ying Ma. 2010. Understanding Transportation Modes Based on GPS Data for Web Applications. ACM Trans. Web 4, 1, Article 1 (Jan. 2010), 36 pages.
[50]
Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, and Wei-Ying Ma. 2008. Understanding Mobility Based on GPS Data. In Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp '08). ACM, New York, NY, USA, 312--321.
[51]
Yu Zheng, Like Liu, Longhao Wang, and Xing Xie. 2008. Learning Transportation Mode from Raw Gps Data for Geographic Applications on the Web. In Proceedings of the 17th International Conference on World Wide Web (WWW '08). ACM, New York, NY, USA, 247--256.
[52]
Yu Zheng, Xing Xie, and Wei-Ying Ma. 2010. GeoLife: A Collaborative Social Networking Service among User, location and trajectory. IEEE Data(base) Engineering Bulletin (June 2010). https://www.microsoft.com/en-us/research/publication/geolife-a-collaborative-social-networking-service-among-user-location-and-trajectory/
[53]
Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining Interesting Locations and Travel Sequences from GPS Trajectories. In Proceedings of the 18th International Conference on World Wide Web (WWW '09). ACM, New York, NY, USA, 791--800.

Cited By

View all
  • (2025)Robust and Ubiquitous Mobility Mode Estimation Using Limited Cellular InformationIEEE Transactions on Vehicular Technology10.1109/TVT.2024.345420874:1(1310-1321)Online publication date: Jan-2025
  • (2024)Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal CharacteristicsISPRS International Journal of Geo-Information10.3390/ijgi1309031413:9(314)Online publication date: 30-Aug-2024
  • (2024)ModeSense: Ubiquitous and Accurate Transportation Mode Detection using Serving Cell Tower InformationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691250(184-195)Online publication date: 29-Oct-2024
  • Show More Cited By

Index Terms

  1. CellTrans: Private Car or Public Transportation? Infer Users' Main Transportation Modes at Urban Scale with Cellular Data

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
        Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 3, Issue 3
        September 2019
        1415 pages
        EISSN:2474-9567
        DOI:10.1145/3361560
        Issue’s Table of Contents
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 09 September 2019
        Published in IMWUT Volume 3, Issue 3

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. cellular networks
        2. human mobility
        3. main transportation mode

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)21
        • Downloads (Last 6 weeks)3
        Reflects downloads up to 16 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2025)Robust and Ubiquitous Mobility Mode Estimation Using Limited Cellular InformationIEEE Transactions on Vehicular Technology10.1109/TVT.2024.345420874:1(1310-1321)Online publication date: Jan-2025
        • (2024)Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal CharacteristicsISPRS International Journal of Geo-Information10.3390/ijgi1309031413:9(314)Online publication date: 30-Aug-2024
        • (2024)ModeSense: Ubiquitous and Accurate Transportation Mode Detection using Serving Cell Tower InformationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691250(184-195)Online publication date: 29-Oct-2024
        • (2024)Transportation Mode Detection Technology to Predict Wheelchair Users' Life Satisfaction in Seoul, South KoreaProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435068:1(1-20)Online publication date: 6-Mar-2024
        • (2023)Ubiquitous Transportation Mode Estimation using Limited Cell Tower Information2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)10.1109/VTC2023-Spring57618.2023.10200431(1-5)Online publication date: Jun-2023
        • (2023)Outdoor Position Recovery From Heterogeneous Telco Cellular DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323236135:11(11736-11750)Online publication date: 1-Nov-2023
        • (2022)Towards Dynamic Crowd Mobility Learning and Meta Model Updates for A Smart Connected CampusProceedings of the 2022 INTERNATIONAL CONFERENCE ON EMBEDDED WIRELESS SYSTEMS AND NETWORKS10.5555/3578948.3578960(126-137)Online publication date: 2-Dec-2022
        • (2021)MoverProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949975:4(1-21)Online publication date: 30-Dec-2021
        • (2021)HERMASProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34781085:3(1-21)Online publication date: 14-Sep-2021
        • (2021)CellSenseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34780875:3(1-22)Online publication date: 14-Sep-2021
        • Show More Cited By

        View Options

        Login options

        Full Access

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media