{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T18:16:16Z","timestamp":1772475376611,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cellular signaling data is widely available in mobile communications and contains abundant movement sensing information of individual travelers. Using cellular signaling data to estimate the trajectories of mobile users can benefit many location-based applications, including infectious disease tracing and screening, network flow sensing, traffic scheduling, etc. However, conventional methods rely too much on heuristic hypotheses or hardware-dependent network fingerprinting approaches. To address the above issues, NF-Track (Network-wide Fingerprinting based Tracking) is proposed to realize accurate online map-matching of cellular location sequences. In particular, neither prior assumptions such as arterial preference and less-turn preference or extra hardware-relevant parameters such as RSS and SNR are required for the proposed framework. Therefore, it has a strong generalization ability to be flexibly deployed in the cloud computing environment of telecom operators. In this architecture, a novel segment-granularity fingerprint map is put forward to provide sufficient prior knowledge. Then, a real-time trajectory estimation process is developed for precise positioning and tracking. In our experiments implemented on the urban road network, NF-Track can achieve a recall rate of 91.68% and a precision rate of 90.35% in sophisticated traffic scenes, which are superior to the state-of-the-art model-based unsupervised learning approaches.<\/jats:p>","DOI":"10.3390\/s22041605","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Online Trajectory Estimation Based on a Network-Wide Cellular Fingerprint Map"],"prefix":"10.3390","volume":"22","author":[{"given":"Langqiao","family":"Chen","sequence":"first","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Taipa, Macao 999078, China"},{"name":"State Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, Macao 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaocheng","family":"He","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7752-6420","authenticated-orcid":false,"given":"Yixian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/COMST.2018.2881008","article-title":"The sensable city: A survey on the deployment and management for smart city monitoring","volume":"21","author":"Du","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ren, Y., Wang, T., Zhang, S., and Zhang, J. (2020). An intelligent big data collection technology based on micro mobile data centers for crowdsensing vehicular sensor network. Pers. Ubiquitous Comput., 1\u201317.","DOI":"10.1007\/s00779-020-01440-0"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/JAS.2020.1003120","article-title":"Urban sensing based on mobile phone data: Approaches, applications, and challenges","volume":"7","author":"Ghahramani","year":"2020","journal-title":"IEEE-CAA J. Autom. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Goh, C.Y., Dauwels, J., Mitrovic, N., and Asif, M.T. (2012, January 16\u201319). Online map-matching based on hidden markov model for real-time traffic sensing applications. Proceedings of the 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, AK, USA.","DOI":"10.1109\/ITSC.2012.6338627"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13417","DOI":"10.1007\/s00521-021-05966-z","article-title":"Dual attentive graph neural network for metro passenger flow prediction","volume":"33","author":"Lu","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lu, Y., He, Z., and Luo, L. (2019, January 12\u201314). Learning trajectories as words: A probabilistic generative model for destination prediction. Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, New York, NY, USA.","DOI":"10.1145\/3360774.3360814"},{"key":"ref_7","unstructured":"Michael, W.J., Leo, S., and David, G.S. (1999). Combining GPS with TOA\/TDOA of Cellular Signals to Locate Terminal. (No. 5,982,324), U.S. Patent."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TVT.2011.2173771","article-title":"CellSense: An Accurate Energy-Efficient GSM Positioning System","volume":"61","author":"Ibrahim","year":"2012","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MWC.2017.1600374","article-title":"5G mmWave Positioning for Vehicular Networks","volume":"24","author":"Wymeersch","year":"2017","journal-title":"IEEE Wirel. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.1109\/COMST.2018.2855063","article-title":"A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation","volume":"20","author":"Laoudias","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"131301","DOI":"10.1007\/s11432-020-3218-8","article-title":"An Overview on Integrated Localization and Communication towards 6G","volume":"65","author":"Xiao","year":"2022","journal-title":"Sci. China Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Servick, K. (2020). Cellphone tracking could help stem the spread of coronavirus. Is privacy the price?. Science.","DOI":"10.1126\/science.abb8296"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102389","DOI":"10.1016\/j.healthplace.2020.102389","article-title":"The need for GIScience in mapping COVID-19","volume":"67","author":"Rosenkrantz","year":"2020","journal-title":"Health Place"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1109\/TCSS.2021.3073109","article-title":"Mitigating COVID-19 Transmission in Schools with Digital Contact Tracing","volume":"8","author":"Sun","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.trc.2019.09.006","article-title":"A tailored machine learning approach for urban transport network flow estimation","volume":"108","author":"Liu","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.trc.2015.05.004","article-title":"Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization","volume":"59","author":"Wu","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.trpro.2021.01.024","article-title":"Travel mode classification of intercity trips using cellular network data","volume":"52","author":"Breyer","year":"2021","journal-title":"Transp. Res. Procedia."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/JAS.2019.1911633","article-title":"Parallel distance: A new paradigm of measurement for parallel driving","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1109\/JAS.2021.1003952","article-title":"Vehicle motion prediction at intersections based on the turning intention and prior trajectories model","volume":"8","author":"Zhang","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3272","DOI":"10.1109\/TITS.2018.2873790","article-title":"Solving Traffic Signal Scheduling Problems in Heterogeneous Traffic Network by Using Meta-Heuristics","volume":"20","author":"Gao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hoteit, S., Secci, S., Sobolevsky, S., Pujolle, G., and Ratti, C. (2013, January 3\u20136). Estimating real human trajectories through mobile phone data. Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management, Milan, Italy.","DOI":"10.1109\/MDM.2013.85"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101346","DOI":"10.1016\/j.compenvurbsys.2019.101346","article-title":"Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data","volume":"77","author":"Li","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Schulze, G., Horn, G., and Kern, R. (2015, January 15\u201318). Map-matching cell phone trajectories of low spatial and temporal accuracy. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, Gran Canaria, Spain.","DOI":"10.1109\/ITSC.2015.435"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103257","DOI":"10.1016\/j.trc.2021.103257","article-title":"TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data","volume":"130","author":"Bonnetain","year":"2021","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shen, Z., Du, W., Zhao, X., and Zou, J. (2020, January 21\u201325). DMM: Fast map matching for cellular data. Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, London, UK.","DOI":"10.1145\/3372224.3421461"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TITS.2016.2591958","article-title":"Accurate Real-time Map Matching for Challenging Environments","volume":"18","author":"Mohamed","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2423","DOI":"10.1109\/TITS.2017.2647967","article-title":"Online map-matching of noisy and sparse location data with hidden Markov and route choice models","volume":"18","author":"Jagadeesh","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1177\/0361198119847472","article-title":"Can We Map-Match Individual Cellular Network Signaling Trajectories in Urban Environments? Data-Driven Study","volume":"2673","author":"Bonnetain","year":"2019","journal-title":"Transp. Res. Rec. J. Transp. Res. Board."},{"key":"ref_29","unstructured":"Bahl, P., and Padmanabhan, V. (2000, January 26\u201327). RADAR: An In-building RF-based User Location and Tracking System. Proceedings of the IEEE Infocom, Tel-Aviv, Israel."},{"key":"ref_30","unstructured":"Kaemarungsi, K., and Krishnamurthy, P. (2004, January 7\u201311). Modeling of indoor positioning systems based on location fingerprinting. Proceedings of the IEEE INFOCOM 2004\u2013The Conference on Computer Communications\u2013Twenty Third Annual Joint Conference of the IEEE Computer and Communications Societies, Hong Kong, China."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shin, H., and Cha, H. (2010, January 23\u201325). Wi-Fi Fingerprint-Based Topological Map Building for Indoor User Tracking. Proceedings of the 16th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA\u201910, Macau, China.","DOI":"10.1109\/RTCSA.2010.23"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"12168","DOI":"10.1109\/TVT.2021.3107936","article-title":"Indoor Localization Based on CSI Fingerprint by Siamese Convolution Neural Network","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TVT.2013.2274646","article-title":"RSSI-fingerprinting-based mobile phone localization with route constraints","volume":"63","author":"Ergen","year":"2014","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_34","unstructured":"Thiagarajan, A., Ravindranath, L.S., Balakrishnan, H., Madden, S., and Girod, L. (April, January 30). Accurate, Low-Energy Trajectory Mapping for Mobile Devices. Proceedings of the 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2011), Boston, MA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1080\/10095020.2019.1616933","article-title":"A map-matching algorithm dealing with sparse cellular fingerprint observations","volume":"22","author":"Torre","year":"2019","journal-title":"Geo-Spatial Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1049\/iet-its.2019.0542","article-title":"Integrated tracking and route classification for travel time estimation based on cellular network signalling data","volume":"14","author":"Karlsson","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_37","first-page":"1","article-title":"CLSTERS: A General System for Reducing Errors of Trajectories Under Challenging Localization Situations","volume":"1","author":"Wu","year":"2017","journal-title":"ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Karimi, H.A. (2016). Advanced Location-Based Technologies and Services, CRC Press.","DOI":"10.1201\/b14940"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, M.Y., Sohn, T., Chmelev, D., and H\u00e4hnel, D. (2006, January 17\u201321). Practical metropolitan-scale positioning for gsm phones. Proceedings of the International Conference Ubiquitous Computing, Orange County, CA, USA.","DOI":"10.1007\/11853565_14"},{"key":"ref_40","unstructured":"Greenfeld, J.S. (2002, January 13\u201317). Matching GPS observations to locations on a digital map. Proceedings of the Transportation Research Board 81st Annual Meeting, Washington, DC, USA."},{"key":"ref_41","unstructured":"Brakatsoulas, S., Pfoser, D., Salas, R., and Wenk, C. (2005, January 4\u20136). On Map-Matching Vehicle Tracking Data. Proceedings of the 31st Inter-national Conference on Very Large Data Bases (VLDB), Trento, Italy."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1049\/iet-its.2011.0226","article-title":"On-line map-matching framework for floating car data with low sampling rate in urban road networks","volume":"7","author":"He","year":"2013","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., and Huang, Y. (2009, January 4\u20136). Map-matching for low-sampling-rate GPS trajectories. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems GIS 09, Seattle, DC, USA.","DOI":"10.1145\/1653771.1653820"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Dub\u00e9, R., Sommer, H., Gawel, A., Bosse, M., and Siegwart, R. (2016, January 16\u201321). Non-uniform sampling strategies for continuous correction based trajectory estimation. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487683"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1109\/TCSS.2020.3003538","article-title":"Discovering Density-Based Clustering Structures Using Neighborhood Distance Entropy Consistency","volume":"7","author":"Kamali","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_46","unstructured":"Ghahramani, M., O\u2019Hagan, A., Zhou, M., and Sweeney, J. (2021). Intelligent Geodemographic Clustering Based on Neural Network and Particle Swarm Optimization. IEEE Trans. Syst. Man Cybern., 1\u201311."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1109\/TCSS.2020.3011471","article-title":"Fuzzy clustering based on automated feature pattern-driven similarity matrix reduction","volume":"8","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/JAS.2021.1004350","article-title":"Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS\/GPS Integration","volume":"9","author":"Dang","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Wei, G., and Wei, Q. (2010, January 10\u201312). Map matching of mobile probes based on handover location technology. Proceedings of the IEEE International Conference on Networking, Sensing and Control, Chicago, IL, USA.","DOI":"10.1109\/ICNSC.2010.5461593"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1080\/15472450902858400","article-title":"Impacts of Sensor Spacing on Accurate Freeway Travel Time Estimation for Traveler Information","volume":"13","author":"Bertini","year":"2009","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2780","DOI":"10.1109\/TITS.2017.2713983","article-title":"Systematic Relation of Estimated Travel Speed and Actual Travel Speed","volume":"18","author":"Kim","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_52","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference Knowledge Discovery and Data Mining (KDD\u201996), Portland, OR, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/18.61115","article-title":"Divergence measures based on the Shannon entropy","volume":"37","author":"Lin","year":"1991","journal-title":"IEEE Trans. Inform. Theory."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Paul, U., Subramanian, A.P., Buddhikot, M.M., and Das, S.R. (2011, January 10\u201315). Understanding traffic dynamics in cellular data networks. Proceedings of the 2011 Proceedings IEEE INFOCOM, Shanghai, China.","DOI":"10.1109\/INFCOM.2011.5935313"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Luomala, J., and Hakala, I. (2015, January 13\u201316). Effects of Temperature and Humidity on Radio Signal Strength in Outdoor Wireless Sensor Networks. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Poland.","DOI":"10.15439\/2015F241"},{"key":"ref_56","unstructured":"(2022, January 08). The Historical Weather in Shenzhen, China. Available online: http:\/\/lishi.tianqi.com\/shenzhen\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1605\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:22:22Z","timestamp":1760134942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1605"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,18]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041605"],"URL":"https:\/\/doi.org\/10.3390\/s22041605","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,18]]}}}