Table of Contents Author Guidelines Submit a Manuscript
Journal of Advanced Transportation
Volume 2019, Article ID 7602792, 11 pages
https://doi.org/10.1155/2019/7602792
Research Article

A Modified Inverse Distance Weighting Method for Interpolation in Open Public Places Based on Wi-Fi Probe Data

1Faculty of Geographical Science, Beijing Normal University, Beijing, China
2Safety and Emergency Management Lab, Beijing Municipal Institute of Labour Protection, Beijing, China
3Department of Geography, University of Connecticut, Storrs, Connecticut, USA

Correspondence should be addressed to Qiang Li; nc.ude.unb@gnaiqil

Received 31 January 2019; Revised 15 May 2019; Accepted 12 June 2019; Published 4 July 2019

Guest Editor: Nikolai Bode

Copyright © 2019 Da-wei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Lijun and H. Kaiqi, “Video-based crowd density estimation and prediction system for wide-area surveillance,” China Communications, vol. 10, no. 5, pp. 79–88, 2013. View at Publisher · View at Google Scholar
  2. H. Fradi and J. Dugelay, “Towards crowd density-aware video surveillance applications,” Information Fusion, vol. 24, pp. 3–15, 2015. View at Publisher · View at Google Scholar
  3. D.-Y. Chen and P.-C. Huang, “Dynamic human crowd modeling and its application to anomalous events detcetion,” in Proceedings of the IEEE International Conference on Multimedia and Expo, ICME '10, pp. 1582–1587, 2010. View at Scopus
  4. F. Jiang, Y. Wu, and A. K. Katsaggelos, “Detecting contextual anomalies of crowd motion in surveillance video,” in Proceedings of the 16th IEEE International Conference on Image Processing ICIP '09, pp. 1113–1116, 2009. View at Publisher · View at Google Scholar
  5. W. Li, V. Mahadevan, and N. Vasconcelos, “Anomaly detection and localization in crowded scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 1, pp. 18–32, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. V. Eiselein, H. Fradi, I. Keller, T. Sikora, and J.-L. Dugelay, “Enhancing human detection using crowd density measures and an adaptive correction filter,” in Proceedings of the 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS '13, pp. 19–24, 2013. View at Scopus
  7. Y. Asakura and E. Hato, “Tracking survey for individual travel behaviour using mobile communication instruments,” Transportation Research Part C: Emerging Technologies, vol. 12, no. 3-4, pp. 273–291, 2004. View at Publisher · View at Google Scholar
  8. F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, and C. Ratti, “Real-time urban monitoring using cell phones: a case study in Rome,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 1, pp. 141–151, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. F. Calabrese, G. Di Lorenzo, L. Liu, and C. Ratti, “Estimating origin-destination flows using mobile phone location data,” IEEE Pervasive Computing, vol. 10, no. 4, pp. 36–44, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Jiang, G. A. Fiore, Y. Yang, J. Ferreira Jr., E. Frazzoli, and M. C. González, “A review of urban computing for mobile phone traces: Current methods, challenges and opportunities,” in Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13, vol. 1, ACM, New York, NY, USA, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. N. Abedi, A. Bhaskar, and E. Chung, “Tracking spatio-temporal movement of human in terms of space utilization using media-access-control address data,” Applied Geography, vol. 51, pp. 72–81, 2014. View at Publisher · View at Google Scholar
  12. C. E. Kontokosta and N. Johnson, “Urban phenology: toward a real-time census of the city using Wi-Fi data,” Computers, Environment and Urban Systems, vol. 64, pp. 144–153, 2017. View at Publisher · View at Google Scholar
  13. M. Cunche, “I know your MAC address: targeted tracking of individual using Wi-Fi,” Journal of Computer Virology and Hacking Techniques, vol. 10, no. 4, pp. 219–227, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. A. B. M. Musa and J. Eriksson, “Tracking unmodified smartphones using wi-fi monitors,” in Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems (SenSys '12), pp. 281–294, ACM, Ontario, Canada, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. F. Wang, X. Zhu, and J. Miao, “Semantic trajectories-based social relationships discovery using WiFi monitors,” Personal and Ubiquitous Computing, vol. 21, no. 1, pp. 85–96, 2017. View at Publisher · View at Google Scholar
  16. Z. Xu, K. Sandrasegaran, X. Kong et al., “Pedestrain monitoring system using Wi-Fi technology and RSSI based localization,” International Journal of Wireless & Mobile Networks, vol. 5, pp. 17–34, 2013. View at Google Scholar
  17. H. Zou, Y. Yue, Q. Li, and A. G. Yeh, “An improved distance metric for the interpolation of link-based traffic data using kriging: a case study of a large-scale urban road network,” International Journal of Geographical Information Science, vol. 26, no. 4, pp. 667–689, 2012. View at Publisher · View at Google Scholar
  18. T. J. Klatko, T. U. Saeed, M. Volovski, S. Labi, J. D. Fricker, and K. C. Sinha, “Addressing the local road VMT estimation problem using spatial interpolation techniques,” Journal of Transportation Engineering Part A: Systems, vol. 143, 2017. View at Google Scholar · View at Scopus
  19. J. Gutiérrez and J. C. García-Palomares, “Distance-measure impacts on the calculation of transport service areas using GIS,” Environment and Planning B: Planning and Design, vol. 35, no. 3, pp. 480–503, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Miura, “A study of travel time prediction using universal kriging,” TOP, vol. 18, no. 1, pp. 257–270, 2010. View at Publisher · View at Google Scholar
  21. L. Li, T. Losser, C. Yorke, and R. Piltner, “Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter PM2:5 in the contiguous U.S. using parallel programming and k-d tree,” International Journal of Environmental Research and Public Health, vol. 11, no. 9, pp. 9101–9141, 2014. View at Publisher · View at Google Scholar
  22. B. I. Harman, H. Koseoglu, and C. O. Yigit, “Performance evaluation of IDW, Kriging and multiquadric interpolation methods in producing noise mapping: a case study at the city of Isparta, Turkey,” Applied Acoustics, vol. 112, pp. 147–157, 2016. View at Publisher · View at Google Scholar
  23. W. Luo, M. C. Taylor, and S. R. Parker, “A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales,” International Journal of Climatology, vol. 28, no. 7, pp. 947–959, 2008. View at Publisher · View at Google Scholar
  24. X. Chen, M.-P. Kwan, Q. Li, and J. Chen, “A model for evacuation risk assessment with consideration of pre- and post-disaster factors,” Computers, Environment and Urban Systems, vol. 36, no. 3, pp. 207–217, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. E. Vattapparamban, B. S. Çiftler, I. Güvenç, K. Akkaya, and A. Kadri, “Indoor occupancy tracking in smart buildings using passive sniffing of probe requests,” in Proceedings of the IEEE International Conference on Communications Workshops, ICC '16, pp. 38–44, 2016. View at Scopus