Monitoring People’s Mobility in the Cities: A Review of Advanced Technologies

  • Chapter
  • First Online:
Information and Communications Technologies for Smart Cities and Societies

Part of the book series: The City Project ((TCP,volume 5))

  • 133 Accesses

Abstract

The knowledge of how people move in urban areas is helpful for the effective deployment of many city services, such as planning and management of transport mobility services, management of security procedures during crowded public events, and design of new public spaces. In the last decade, several technologies have been exploited to collect relevant data to get key insights on the number of people that gather in different points of interest, the amount of time the people spend there, and how frequently people return. This chapter reviews the latest technological solutions that have been developed in this field which exploit the following data sources: radars, lidars, cameras, Wi-Fi sniffers, CDRs, and crowdsourcing applications. It also provides an analysis of the pros and cons of each alternative, the achievable accuracy, and the types of areas that can be monitored.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. B.-W. Chen, W. Ji, Intelligent marketing in smart cities: crowdsourced data for geo-conquesting. IT Prof. 18(4), 18–24 (2016). https://doi.org/10.1109/MITP.2016.64

    Article  Google Scholar 

  2. R.M. Savithramma, B.P. Ashwini, R. Sumathi. Smart mobility implementation in smart cities: a comprehensive review on state-of-art technologies, in 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (2022) pp. 10–17. https://doi.org/10.1109/ICSSIT532642022.9716288

  3. C.-W. Lu et al. An energy-efficient smart city for sustainable green tourism industry. Sustain. Energy Technol. Assessments 47(2021), 101494. ISSN: 2213-1388. https://doi.org/10.1016/j.seta.2021.101494. https://www.sciencedirect.com/science/article/pii/S2213138821005051

  4. S. Khan, et al. Criminal investigation using call data records (CDR) through big data technology, in 2017 International Conference onNascent Technologies in Engineering (ICNTE). pp. 1–5. (2017) https://doi.org/10.1109/ICNTE.2017.7947942

  5. J. Cinnamon, S. Jones, W. Adger, Evidence and future potential of mobile phone data for disease disaster management. Geoforum 75, 253–264 (2016). https://doi.org/10.1016/j.geoforum.2016.07.019

    Article  Google Scholar 

  6. A.M. Al-Shaery et al., In-depth survey to detect, monitor and manage crowd. IEEE Access 8, 209008–209019 (2020). https://doi.org/10.1109/ACCESS.2020.3038334

    Article  Google Scholar 

  7. K. Vidović, S. Mandžuka, D. Brčić, Estimation of urban mobility using public mobile network, in 2017 International Symposium ELMAR, (2017), pp. 21–24. https://doi.org/10.23919/ELMAR.2017.8124426

  8. General Data Protection Regulation (GDPR). (European Union, 2018). https://gdpr.eu/article-4-definitions/

  9. B. Liu, et al. Indoor monitoring human movements using dual-receiver radar, in 2017 IEEE Radar Conference (RadarConf) (2017), pp. 0520–0523. https://doi.org/10.1109/RADAR.2017.7944258

  10. M. Skolnik. Radar Handbook (Mc Graw Hill, 2008)

    Google Scholar 

  11. C. Jamie, et al. Lidar 101: An Introduction to Lidar Technology, Data, and Applications (National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center, 2012)

    Google Scholar 

  12. M. Stephan, et al. People counting solution using an FMCW radar with knowledge distillation from camera data, in 2021 IEEE Sensors (2021), pp. 1–4. https://doi.org/10.1109/SENSORS47087.2021.9639798

  13. A. Günter, et al. Privacy-preserving people detection enabled by solid state LiDAR, in 2020 16th International Conference on Intelligent Environments (IE) (2020), pp. 1–4. https://doi.org/10.1109/IE49459.2020.9154970

  14. A. Jalalvand, et al. Radar signal processing for human identification by means of reservoir computing networks, in 2019 IEEE Radar Conference (RadarConf) (2019), pp. 1–6. https://doi.org/10.1109/RADAR.2019.8835753

  15. P. Zhao et al. mID: tracking and identifying people with millimeter wave radar, in 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) (2019), pp. 33–40. https://doi.org/10.1109/DCOSS.2019.00028

  16. J. Shackleton, B. VanVoorst, J. Hesch. Tracking people with a 360-degree lidar, in 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance (2010) pp. 420–426. https://doi.org/10.1109/AVSS.2010.52

  17. X. Li et al., Data fusion for intelligent crowd monitoring and management systems: a survey. IEEE Access 9, 47069–47083 (2021). https://doi.org/10.1109/ACCESS.2021.3060631

    Article  Google Scholar 

  18. K. Han, S. Hong, Detection and localization of multiple humans based on curve length of I/Q signal trajectory using MIMO FMCW radar. IEEE Microw Wireless Components Lett. 31(4), 413–416 (2021). https://doi.org/10.1109/LMWC.2021.3057867

    Article  Google Scholar 

  19. C. Álvarez-Aparicio, et al. LIDAR-based people detection and tracking for @home Competitions, in 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) (2019), pp. 1–6. https://doi.org/10.1109/ICARSC.2019.8733624

  20. B.M. Bharadhwaj, B.B. Nair, Deep learning-based 3D object detection using LiDAR and image data fusion, in 2022 IEEE 19th India Council International Conference (INDICON) (2022), pp. 1–6. https://doi.org/10.1109/INDICON56171.2022.10040030

  21. N. Ilyas, A. Shahzad, K. Kim, Convolutional-neural network-based image crowd counting: review, categorization, analysis, and performance evaluation. Sensors 20(1), 43 (2019)

    Article  Google Scholar 

  22. L. Boominathan, S.S.S. Kruthiventi, R. Venkatesh Babu. Crowdnet: a deep convolutional network for dense crowd counting, in Proceedings of the 24th ACMinternational conference on Multimedia (2016), pp. 640–644

    Google Scholar 

  23. X. Deng et al., iCaps: iterative category-level object pose and shape estimation. IEEE Robot. Autom. Lett. 7(2), 1784–1791 (2022)

    Article  Google Scholar 

  24. J. Redmon, et al. You only look once: unified, real-time object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp 779–788

    Google Scholar 

  25. M. Vajgl, P. Hurtik, T. Nejezchleba, Dist-YOLO: fast object detection with distance estimation. Appl. Sci. 12(3), 1354 (2022)

    Article  Google Scholar 

  26. I. Goodfellow et al., Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

    Article  MathSciNet  Google Scholar 

  27. V.A. Sindagi, V.M. Patel, A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recognit. Lett. 107, 3–16 (2018)

    Article  Google Scholar 

  28. W. Wang et al., AttentiveWaveBlock: complementarity-enhanced mutual networks for unsupervised domain adaptation in person re-identification and beyond. IEEE Trans. Image Proc. 31, 1532–1544 (2022)

    Article  Google Scholar 

  29. J. Yun et al., GAN-based sensor data augmentation: application for counting moving people and detecting directions using PIR sensors. Eng. Appl. Artif. Intell. 117, 105508 (2023)

    Article  Google Scholar 

  30. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  31. M. Vanhoef et al. Why MAC address randomization is not enough: an analysis of Wi-Fi network discovery mechanisms, in Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security (2016) pp. 413–424

    Google Scholar 

  32. C. Matte. Wi-Fi tracking: Fingerprinting Attacks and Counter-Measures. PhD thesis. (Universitè de Lyon, 2017)

    Google Scholar 

  33. A.D. Luzio, A. Mei, J. Stefa. Mind your probes: de-anonymization of large crowds through smartphone WiFi probe requests, inIEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications (2016), pp. 1–9. https://doi.org/10.1109/INFOCOM.2016.7524459

  34. IEEE Standard for Information Technology-Telecommunications and Information Exchange between Systems - Local and Metropolitan Area Networks-Specific Requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, in IEEE Std 802.11-2020 (Revision of IEEE Std 802.11-2016) (2021), pp. 1–4379. https://doi.org/10.1109/IEEESTD.2021.9363693

  35. L. Pintor, L. Atzori, A dataset of labelled device Wi-Fi proberequests for MAC address de-randomization. Comput. Netw. 205, 108783 (2022)

    Article  Google Scholar 

  36. Defense Advanced Research Projects Agency (DARPA). The Radio Frequency Spectrum + Machine Learning = A New Wave in Radio Technology (2017). https://www.darpa.mil/news-events/2017-08-11a

  37. A. Al-Shawabka et al., Massive-scale I/Q datasets for WiFi radio fingerprinting. Comput. Netw. 182, 107566 (2020)

    Article  Google Scholar 

  38. A. Simončič et al. Labeled dataset of IEEE 802.11 probe requests. This research was partly funded by the Slovenian Research Agency (ARRS) grant no. P2-0016, J2-3048, J2-2507, and P2-0016 - “COVID extension” (2023). https://doi.org/10.5281/zenodo.7503594

  39. R. Cossu, et al. A blockchain-based data notarization system for smart mobility services in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE (2022), pp. 1231–1238

    Google Scholar 

  40. A. Jagannath, J. Jagannath, P.S. Pattanshetty Vasanth Kumar, A comprehensive survey on radio frequency (rf) fingerprinting: traditional approaches, deep learning, and open challenges. Comput. Netw. 109455 (2022)

    Google Scholar 

  41. C. Tang, T. Yan, Y. An, Radio frequency fingerprint recognition based on deep learning, in 2021 International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS), pp. 708–711. https://doi.org/10.1109/ICITBS53129.2021.00177

  42. M. Valkama, M. Renfors, V. Koivunen. Advanced methods for I/Q imbalance compensation in communication receivers. IEEE Transactions on Signal Processing 49(10), 2335–2344 (2001). ISSN: 1941-0476. https://doi.org/10.1109/78.950789

  43. L. Ding et al., Specific emitter identification via convolutional neural networks. IEEE Commun. Lett 22(12), 2591–2594 (2018)

    Article  MathSciNet  Google Scholar 

  44. K. Sankhe et al., No radio left behind: radio fingerprinting through deep learning of physical-layer hardware impairments. IEEE Trans. Cognit. Commun. Netw. 6(1), 165–178 (2019)

    Article  Google Scholar 

  45. K. Sankhe et al. ORACLE: optimized radio classification through convolutional neural networks, in IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE (2019), pp. 370–378

    Google Scholar 

  46. N. Soltani et al., RF fingerprinting unmanned aerial vehicles with nonstandard transmitter waveforms. IEEE Trans. Vehicular Tech. 69(12), 15518–15531 (2020)

    Article  Google Scholar 

  47. T. Jian et al., Deep learning for RF fingerprinting: Amassive experimental study. IEEE Internet Things Magaz. 3(1), 50–57 (2020)

    Article  Google Scholar 

  48. L. Peng et al., Deep learning based RF fingerprint identification using differential constellation trace figure. IEEE Trans. Vehicular Tech. 69(1), 1091–1095 (2020). https://doi.org/10.1109/TVT.2019.2950670

    Article  MathSciNet  Google Scholar 

  49. L. Pintor, L. Atzori, Analysis of Wi-Fi probe requests towards information element fingerprinting. GLOBECOM 2022-2022 IEEE global communications conference. IEEE (2022), pp. 3857–3862

    Google Scholar 

  50. M. Vega-Barbas et al., AFOROS: a low-costWi-Fi-based monitoring system for estimating occupancy of public spaces. Sensors 21(11), 3863 (2021)

    Article  Google Scholar 

  51. L. Oliveira et al., Mobile device detection through WiFi probe request analysis. IEEE Access 7, 98579–98588 (2019)

    Article  Google Scholar 

  52. M. Nitti et al., iabacus: Awi-fi-based automatic bus passenger counting system. Energ. 13(6), 1446 (2020)

    Google Scholar 

  53. Y. Cai et al. MAC address randomization tolerant crowd monitoring system using Wi-Fi packets. In Proceedings of the 16th Asian Internet Engineering Conference (2021), pp. 27–33

    Google Scholar 

  54. M. Uras et al., MAC address de-randomization for WiFi device counting: combining temporal-and content-based fingerprints. Comp. Netw. 218, 109393 (2022)

    Article  Google Scholar 

  55. A. Simončič et al., Non-intrusive privacy-preserving approach for presence monitoring based on WiFi probe requests. Sensors 23(5), 2588 (2023)

    Article  Google Scholar 

  56. S. Hanna, S. Karunaratne, D. Cabric, WiSig: alargescale WiFi signal dataset for receiver and channel agnostic RF fingerprinting. IEEE Access 10, 22808–22818 (2022)

    Article  Google Scholar 

  57. A. Al-Shawabka, et al. Exposing the fingerprint: dissecting the impact of the wireless channel on radio fingerprinting, in IEEE INFOCOM 2020—IEEE conference on computer communications, IEEE, pp. 646–655 (2020)

    Google Scholar 

  58. S. Riyaz et al., Deep learning convolutional neural networks for radio identification. IEEE Commun. Magaz. 56(9), 146–152 (2018)

    Article  Google Scholar 

  59. Y. Huang et al., Radio frequency fingerprint extraction of radio emitter based on I/Q imbalance. Proced. Comput. Sci. 107, 472–477 (2017)

    Article  Google Scholar 

  60. D. Shaw, W. Kinsner. Multifractal modelling of radio transmitter transients for classification, in IEEE WESCANEX 97 communications, power and computing. Conference Proceedings. IEEE (1997), pp. 306–312

    Google Scholar 

  61. O. Ureten, N. Serinken, Detection of radio transmitter turn-on transients. Electron. Lett. 35(23) (1999)

    Google Scholar 

  62. O. Ureten, N. Serinken, et al. Bayesian detection of radio transmitter turn-on transients, in NSIp (1999), pp. 830–834

    Google Scholar 

  63. L. Zong, IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (IEEE, 2020), pp. 1778–1781

    Google Scholar 

  64. D.S. Terzi, R. Terzi, S. Sagiroglu. A survey on security and privacy issues in big data, in 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST), (2015), pp 202–207. https://doi.org/10.1109/ICITST.2015.7412089

  65. M. Berlingerio et al. AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data (2013). ISBN: 978-3-642-38708-1. https://doi.org/10.1007/978-3-642-40994-3_50

  66. A. Janecek et al., The cellular network as a sensor: from mobile phone data to real-time road traffic monitoring. IEEE Trans. Intell. Transp. Syst. 16(5), 2551–2572 (2015). https://doi.org/10.1109/TITS.2015.2413215

    Article  Google Scholar 

  67. L. Shu et al., When mobile crowd sensing meets traditional industry. IEEE Access 5, 15300–15307 (2017)

    Article  Google Scholar 

  68. G. Musolino, C. Rindone, A. Vitetta. Models for supporting mobility as a service (MaaS) design. Smart Cities 5(1), 206–222 (2022). ISSN: 2624-6511. https://doi.org/10.3390/smartcities5010013. https://www.mdpi.com/2624-6511/5/1/13

  69. A. Nuzzolo, A. Comi. Dynamic optimal travel strategies in intelligent stochastic transit networks. Information 12(7). ISSN: 2078-2489 (2021). https://doi.org/10.3390/info12070281. https://www.mdpi.com/2078-2489/12/7/281

  70. A. Comi et al. Private car O-D flow estimation based on automated vehicle monitoring data: theoretical issues and empirical evidence. Information 12(12) (2021). ISSN: 2078-2489. https://doi.org/10.3390/info12120493. https://www.mdpi.com/2078-2489/12/12/493

  71. O. Altintasi, H. Tuydes-Yaman, K. Tuncay. Detection of urban traffic patterns from floating car data (FCD). Transp. Res. Proced. 22 (2017). 19th EURO Working Group on Transportation Meeting, EWGT2016, 5-7 September 2016, Istanbul, Turkey, pp. 382–391. ISSN: 2352-1465. https://doi.org/10.1016/j.trpro.2017.03.057. https://www.sciencedirect.com/science/article/pii/S235214651730193X

  72. J. Simões, et al. Urban mobility: mobile crowdsensing applications, in Ambient Intelligence-Software and Applications-, 9th International Symposium on Ambient Intelligence (Springer, 2019), pp. 182–189

    Google Scholar 

  73. R.K. Ganti, F. Ye, H. Lei, Mobile crowdsensing: current state and future challenges. IEEE Commun Magaz. 49(11), 32–39 (2011)

    Article  Google Scholar 

  74. B. Guo, et al. From participatory sensing to mobile crowd sensing, in 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS). (IEEE, 2014), pp. 593–598

    Google Scholar 

  75. Y. Chon et al. Automatically characterizing places with opportunistic crowdsensing using smartphones, in Proceedings of the 2012 ACM Conference on Ubiquitous Computing (2012), pp. 481–490

    Google Scholar 

  76. G. Broll et al. Tripzoom: an app to improve your mobility behavior, in Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia (2012), pp. 1–4

    Google Scholar 

  77. J. Froehlich, et al. UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits, in Proceedings of the Sigchi Conference on Human Factors in Computing Systems (2009), pp. 1043–1052

    Google Scholar 

  78. G. Musolino, C. Rindone, A. Vitetta. Mobility as a service (MaaS): framework definition of a survey for passengers’ behaviour, in New Metropolitan Perspectives, ed. by F. Calabró, L. Della Spina, M. Josè Piñeira Mantiñán. (Springer International Publishing, Cham, 2022), pp. 324–333. ISBN: 978-3-031-06825-6

    Google Scholar 

  79. F. Russo, C. Rindone. Smart city for sustainable development: applied processes from SUMP to MaaS at European level. Appl. Sci. 13(3) (2023). ISSN: 2076-3417. https://doi.org/10.3390/app13031773. https://www.mdpi.com/2076-3417/13/3/1773

  80. J. Jariyasunant et al., Quantified traveler: travel feedback meets the cloud to change behavior. J. Intell. Transp. Syst. 19(2), 109–124 (2015)

    Article  Google Scholar 

  81. I. Meloni, B.S. Di, Teulada, I-Pet individual persuasiveEco-travel technology: a tool for VTBC program implementation. Transp. Res. Proced. 11, 422–433 (2015)

    Article  Google Scholar 

  82. J.G.P. Rodrigues, A. Aguiar, C. Queirós. Opportunistic mobilecrowdsensing for gathering mobility information: lessons learned, in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). (IEEE, 2016), pp. 1654–1660

    Google Scholar 

  83. S. Faye et al., Characterizing user mobility using mobile sensing systems. Int. J. Distrib. Sens. Netw. 13(8), 1550147717726310 (2017)

    Article  Google Scholar 

  84. D. Stojanovic, B. Predic, N. Stojanovic, Mobile crowd sensing for smart urban mobility. Eur. Handb. Crowdsourced Geogr. Inf. 371 (2016)

    Google Scholar 

  85. Z. Patterson et al., Itinerum: the open smartphone travel survey platform. SoftwareX 10, 100230 (2019)

    Article  Google Scholar 

  86. J. Wang et al., Energy saving techniques in mobile crowd sensing: current state and future opportunities. IEEE Commun. Magaz. 56(5), 164–169 (2018)

    Article  Google Scholar 

  87. H. **ong et al., EEMC: enabling energy-efficient mobile crowdsensing with anonymous participants. ACM Trans. Intell. Syst. Tech. (TIST) 6(3), 1–26 (2015)

    Article  Google Scholar 

  88. L. Wang et al., effSense: a novel mobile crowd-sensing framework for energy-efficient and cost-effective data uploading. IEEE Trans. Syst. Man Cybernet. Syst. 45(12), 1549–1563 (2015)

    Article  Google Scholar 

  89. C.H. Liu, Z. Chen, Y. Zhan, Energy-efficient distributed mobile crowd sensing: a deep learning approach. IEEE J. Selected Areas Commun. 37(6), 1262–1276 (2019)

    Article  Google Scholar 

  90. H. Wu et al., Enabling data trustworthiness and user privacy in mobile crowdsensing. IEEE/ACM Trans. Netw. 27(6), 2294–2307 (2019)

    Article  Google Scholar 

  91. T. Luo et al., Improving IoT data quality in mobile crowd sensing: a cross validation approach. IEEE Internet Things J. 6(3), 5651–5664 (2019)

    Article  Google Scholar 

  92. E. Zupančič, B. Žalik, Data trustworthiness evaluation in mobile crowdsensing systems with users’ trust dispositions’ consideration. Sensors 19(6), 1326 (2019)

    Article  Google Scholar 

  93. A. Boukerche, B. Kantarci, C. Kaptan, Towards ensuring the reliability and dependability of vehicular crowd-sensing data in GPSless location tracking. Pervas. Mob. Comput. 68, 101248 (2020)

    Article  Google Scholar 

  94. L.G. Jaimes, I.J. Vergara-Laurens, A. Raij, A survey of incentive techniques for mobile crowd sensing. IEEE Internet Things J. 2(5), 370–380 (2015)

    Article  Google Scholar 

  95. R.I. Ogie, Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework. Human-Centric Comput. Inf. Sci. 6(1), 1–31 (2016)

    Article  Google Scholar 

  96. X. Zhang et al., Incentives for mobile crowd sensing: a survey. IEEE Commun. Surveys and Tutor. 18(1), 54–67 (2015)

    Article  Google Scholar 

  97. L. Pournajaf et al., Participant privacy in mobile crowd sensing task management: a survey of methods and challenges. ACMSigmod Rec. 44(4), 23–34 (2016)

    Article  Google Scholar 

  98. J.W. Kim, K. Edemacu, B. Jang. Privacypreserving mechanisms for location privacy in mobile crowdsensing: a survey. J. Netw. Comput. Appl. 103315 (2022)

    Google Scholar 

  99. Z. Wang et al., When mobile crowdsensing meets privacy. IEEE Commun. Magaz. 57(9), 72–78 (2019)

    Article  Google Scholar 

  100. A. Capponi et al., A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun. Surveys Tutor. 21(3), 2419–2465 (2019)

    Article  Google Scholar 

  101. M. Weber, I. Podnar Žarko. A regulatory view on smart city services. Sensors 19(2), 415 (2019)

    Google Scholar 

  102. M. Szocska, et al. Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis. Sci. Rep. 11(1) (2021). https://doi.org/10.1038/s41598-021-81873-6. https://doi.org/10.1038/s41598-021-81873-6

Download references

Acknowledgements

This work has been partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, “Sustainable Mobility Center” (Centro Nazionale per la Mobilitá Sostenibile), CNMS, CN 00000023.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luigi Atzori .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pintor, L., Uras, M., Colistra, G., Atzori, L. (2024). Monitoring People’s Mobility in the Cities: A Review of Advanced Technologies. In: Menozzi, R. (eds) Information and Communications Technologies for Smart Cities and Societies. The City Project, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-031-39446-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-39446-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39445-4

  • Online ISBN: 978-3-031-39446-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Navigation