Pedestrian network generation based on crowdsourced tracking data

References

• Ahmed, M., et al., 2015. A comparison and evaluation of map construction algorithms using vehicle tracking data. Geoinformatica, 19 (3), 601–632. doi:10.1007/s10707-014-0222-6
   Web of Science ®Google Scholar
• Bakovic, Z., 2012. Vulnerable road users: characteristics of pedestrians. Journal of the Australasian College of Road Safety, 23 (1), 45–48.
   Google Scholar
• Balata, J., et al., 2016. Automatically generated landmark-enhanced navigation instructions for blind pedestrians. In: Proceedings of the federated conference on computer science and information systems, 11-14 September. Gdansk, Poland: IEEE, 1605–1612.
   Google Scholar
• Ballester, M., Pérez, M., and Stuiver, H., 2011. Automatic pedestrian network generation. In: Proceedings of the 14th AGILE international conference on geographic information science, 18-22 April. Utrecht, Netherlands: 1–13.
   Google Scholar
• Biagioni, J. and Eriksson, J., 2012. Map inference in the face of noise and disparity. In: Proceedings of the 20th international conference on advances in geographic systems, 06-09 November. Redondo Beach, California, USA: ACM, 79–88. doi:10.1177/1753193411423875
   Google Scholar
• Cao, L. and Krumm, J., 2009. From GPS traces to a routable road map. In: O. Wolfson, D. Agrawal, and C. Lu, eds. Proceedings of the 17th international conference on advances in geographic information systems, 04-06 November. Seattle, Washington, USA: ACM, 3–12.
   Google Scholar
• Chen, Y. and Krumm, J., 2010. Probabilistic modeling of traffic lanes from GPS traces. In: D. Agrawal, et al., eds. Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, 02-05 November. San Jose, California, USA: ACM, 81–88.
   Google Scholar
• Edelkamp, S. and Schrödl, S., 2003. Route planning and map inference with global positioning traces. In: K. Rolf, S. Hans-Werner, and W. Lutz, eds. Computer science in perspective. Berlin, Germany: Springer Berlin Heidelberg, 2598, 128–151.
   Google Scholar
• Feng, S., Li, Z., and Zhang, W., 2008. Width of pavement in the city. Journal of Harbin Institute of Technology, 40 (3), 586–588.
   Google Scholar
• Gaglio, S., et al., 2017. Human activity recognition process using 3-D posture data. IEEE Transactions on Human-Machine Systems, 45 (5), 586–597. doi:10.1109/THMS.2014.2377111
   Web of Science ®Google Scholar
• Gharebaghi, A., et al., 2017. A confidence-based approach for the assessment of accessibility of pedestrian network for manual wheelchair users. In: M. Peterson, ed. Advances in cartography and giscience. Cham: Springer, 463–477.
   Google Scholar
• Goodchild, M., 2007. Citizens as sensors: the world of volunteered geography. Geojournal, 69 (4), 211–221. doi:10.1007/s10708-007-9111-y
   Google Scholar
• Graham, R., 1972. An efficient algorithm for determining the convex hull of a finite planar set. Information Processing Letters, 1 (4), 132–133. doi:10.1016/0020-0190(72)90045-2
   Google Scholar
• Karimi, H. and Kasemsuppakorn, P., 2013. Pedestrian network map generation approaches and recommendation. International Journal of Geographical Information Science, 27 (5), 947–962. doi:10.1080/13658816.2012.730148
   Web of Science ®Google Scholar
• Kasemsuppakorn, P. and Karimi, H., 2009. Pedestrian network data collection through location-based social networks. In: International conference on collaborative computing: networking, applications and worksharing, 11-14 November. Washington, DC, USA: IEEE, 1–9.
   Google Scholar
• Kasemsuppakorn, P. and Karimi, H., 2013. A pedestrian network construction algorithm based on multiple GPS traces. Transportation Research Part C: Emerging Technologies, 26 (1), 285–300. doi:10.1016/j.trc.2012.09.007
   Google Scholar
• Kim, J., Bang, Y., and Yu, K., 2009. Automatic derivation of a pedestrian network based on existing spatial data sets. In: ASPRS/MAPPS 2009 Fall Conference, 16-19 November, San Antonio, Texas, USA:1–7.
   Google Scholar
• Kim, T., et al., 2017. Characterizing dynamic walking patterns and detecting falls with wearable sensors using gaussian process methods. Sensors, 17 (5), 1172. doi:10.3390/s17051172
   Web of Science ®Google Scholar
• Klinkenberg, B., 1994. A review of methods used to determine the fractal dimension of linear features. Mathematical Geology, 26 (1), 23–46. doi:10.1007/BF02065874
   Google Scholar
• Li, X., 2014. Using complexity measures of movement for automatically detecting movement types of unknown GPS trajectories. American Journal of Geographic Information System, 3 (2), 63–74.
   Google Scholar
• Liu, Y., et al., 2017. An hidden Markov model based complex walking pattern recognition algorithm. In: Proceedings of international conference on ubiquitous positioning, 2-4 November. Shanghai, China: IEEE, 223–229.
   Google Scholar
• Lu, W., et al., 2017. Trajectory-based motion pattern analysis of crowds. Neurocomputing, 247, 213–223. doi:10.1016/j.neucom.2017.03.074
   Web of Science ®Google Scholar
• Mannini, A. and Sabatini, A., 2010. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 10 (2), 1154–1175. doi:10.3390/s100201154
   PubMed Web of Science ®Google Scholar
• Nams, V. and Bourgeois, M., 2004. Fractal analysis measures habitat use at different spatial scales: an example with American marten. Revue Canadienne De Zoologie, 82 (11), 1738–1747. doi:10.1139/z04-167
   Web of Science ®Google Scholar
• National Geographic Institute (IGN), Available from: http://www.ign.es [Accessed December 2010]
   Google Scholar
• Ouyang, X., et al., 2018. Pedestrian-Synthesis-GAN: generating pedestrian data in real scene and beyond. arXiv Preprint arXiv:1804.02047, 1–22.
   Google Scholar
• Scholliers, J., Sambeek, M.V., and Moerman, K., 2017. Integration of vulnerable road users in cooperative ITS systems. European Transport Research Review, 9 (2), 15. doi:10.1007/s12544-017-0230-3
   Web of Science ®Google Scholar
• Tang, L., et al., 2012. Road network change detection based on floating car data. Journal of Networks, 7 (7), 1063–1070. doi:10.4304/jnw.7.7.1063-1070
   Google Scholar
• Tang, L., et al., 2016. CLRIC: collecting lane-based road information via crowdsourcing. IEEE Transactions on Intelligent Transportation Systems, 17 (9), 2552–2562. doi:10.1109/TITS.2016.2521482
   Web of Science ®Google Scholar
• Timothy, B.L., 2016. Why Google and car companies are about to spend billions mapping American roads [online]. Available from: http://www.vox.com/2016/4/22/11476636/selfdriving-car-maps [Accessed 22 April 2016].
   Google Scholar
• Tu, W., et al., 2014. A bi-level Voronoi diagram-based metaheuristic for a large-scale multi-depot vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 61, 84–97. doi:10.1016/j.tre.2013.11.003
   Web of Science ®Google Scholar
• Tulloch, D., 2014. Crowdsourcing geographic knowledge: volunteered geographic information (VGI) in theory and practice. International Journal of Geographical Information Systems, 28 (4), 847–849. doi:10.1080/13658816.2013.873440
   Web of Science ®Google Scholar
• Uduwaragoda, E.R.I.A.C., Perera, A.S., and Dias, S.A.D., 2013. Generating lane level road data from vehicle trajectories using kernel density estimation. In: Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, 06-09 October. Hague, Netherlands: IEEE, 384–391.
   Google Scholar
• Wang, J., et al., 2012. Walking pattern classification and walking distance estimation algorithms using gait phase information. IEEE Transactions on Biomedical Engineering, 59 (10), 2884–2892. doi:10.1109/TBME.2012.2212245
   PubMed Web of Science ®Google Scholar
• Wang, J., et al., 2015. A novel approach for generating routable road maps from vehicle GPS traces. International Journal of Geographical Information Science, 29 (1), 69–91. doi:10.1080/13658816.2014.944527
   Web of Science ®Google Scholar
• Watzenig, D. and Horn, M., 2016. Automated driving: safer and more efficient future driving. In: D. Watzenig, and M. Horn, eds. Automated Driving. Springer International Publishing AG, 203.
   Google Scholar
• Wiedemann, C., 2003. External evaluation of road networks. Laprs, 34, 93–98.
   Google Scholar
• Xie, X. and Ou, G., 2018. Pedestrian network information extraction based on VGI. Geomatica, 72 (3), 85–99. doi:10.1139/geomat-2018-0006
   Google Scholar
• Yang, X., et al., 2018a. Generating lane-based intersection maps from crowdsourcing big trace data. Transportation Research Part C: Emerging Technologies, 89, 168–187. doi:10.1016/j.trc.2018.02.007
   Web of Science ®Google Scholar
• Yang, X., et al. 2018b. Automatic change detection in lane-level road networks using GPS trajectories. International Journal of Geographical Information Science, 32 (3), 601–621.
   Web of Science ®Google Scholar
• Yang, X., et al., 2018c. A data cleaning method for big trace data using movement consistency. Sensors, 18 (3), 824. doi:10.3390/s18030824
   Web of Science ®Google Scholar
• Zhang, H. and Ye, C., 2017. RGB-D camera based walking pattern recognition by support vector machines for a smart rollator. International Journal of Intelligent Robotics & Applications, 1 (1), 32. doi:10.1007/s41315-016-0002-6
   PubMed Web of Science ®Google Scholar
• Zheng, Y., Xie, X., and Ma, W., 2010. GeoLife: a collaborative social networking service among user, location and trajectory. In IEEE Data Engineering Bulletin, 33 (2), 32–40.
   Google Scholar
• Zheng, Y., et al., 2009. Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on World Wide Web, 20-24 April. Madrid, Spain: ACM, 791–800. doi:10.4315/0362-028x-72.4.791
   Google Scholar