An approach for computing routes without complicated decision points in landmark-based pedestrian navigation

References

• Ahn, C.W. and Ramakrishna, R.S., 2002. A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Transactions on Evolutionary Computation, 6 (6), 566–579. doi:10.1109/TEVC.2002.804323
   Web of Science ®Google Scholar
• Amirian, P. and Basiri, A., 2016. Landmark-based pedestrian navigation using augmented reality and machine learning. Lecture notes in geoinformation and cartography. In: G. Gartner, M. Jobst, and H. Huang, eds. Progress in cartography: euroCarto 2015. Cham: Springer International Publishing, 451–465.
   Google Scholar
• Basiri, A., et al., 2016. Seamless pedestrian positioning and navigation using landmarks. Journal of Navigation, 69, 24–40. doi:10.1017/S0373463315000442
   Web of Science ®Google Scholar
• Beeharee, A.K. and Steed, A., 2006. A natural wayfinding exploiting photos in pedestrian navigation systems. In: Proceedings of the 8th conference on human-computer interaction with mobile devices and services. Helsinki, Finland, 81–88.
   Google Scholar
• Caduff, D. and Timpf, S., 2005. The landmark spider: representing landmark knowledge for wayfinding tasks. In: T. Barkowsky, et al., eds. Reasoning with mental and external diagrams: computational modeling and spatial assistance - papers from the 2005 AAAI spring symposium. California, USA: AAAI Press, 30–35.
   Google Scholar
• Daniel, M.P. and Denis, M., 1998. Spatial descriptions as navigational aids: a cognitive analysis of route directions. Kognitionswissenschaft, 7 (1), 45–52. doi:10.1007/BF03354963
   Google Scholar
• Delikostidis, I., van Elzakker, C.P., and Kraak, M.J., 2016. Overcoming challenges in developing more usable pedestrian navigation systems. Cartography and Geographic Information Science, 43 (3), 189–207. doi:10.1080/15230406.2015.1031180
   Web of Science ®Google Scholar
• Denis, M., et al., 1999. Spatial discourse and navigation: an analysis of route directions in the city of Venice. Applied Cognitive Psychology, 13 (2), 145–174. doi:10.1002/(SICI)1099-0720(199904)13:2<145::AID-ACP550>3.0.CO;2-4
   Web of Science ®Google Scholar
• Dijkstra, E.W., 1959. A note on two problems in connection with graphs. Numerische Mathematics, 1 (1), 269–271. doi:10.1007/BF01386390
   Google Scholar
• Duckham, M. and Kulik, L., 2003. “Simplest” paths: automated route selection for navigation. In: W. Kuhn, M.F. Worboys, and S. Timpf, eds. Spatial Information Theory. Foundations of geographic information science, lecture notes in computer science. Berlin: Springer, 169–185.
   Google Scholar
• Elias, B. and Sester, M., 2006. Incorporating landmarks with quality measures in routing procedures. In: M. Raubal, et al., eds. Geographic information science: 4th international conference, GIScience 2006, münster, germany, September 20–23. Berlin: Springer, 65–80.
   Google Scholar
• Fang, Z., et al., 2017. An artificial bee colony-based multi-objective route planning algorithm for use in pedestrian navigation at night. International Journal of Geographical Information Science, 31 (10), 2020–2044. doi:10.1080/13658816.2017.1346795
   Web of Science ®Google Scholar
• Fang, Z., Li, Q., and Zhang, X., 2011. A multiobjective model for generating optimal landmark sequences in pedestrian navigation applications. International Journal of Geographical Information Science, 25 (5), 785–805. doi:10.1080/13658816.2010.500290
   Web of Science ®Google Scholar
• Fang, Z., et al., 2012. A GIS data model for landmark-based pedestrian navigation. International Journal of Geographical Information Science, 26 (5), 817–838. doi:10.1080/13658816.2011.615749
   Web of Science ®Google Scholar
• Frank, A.U., 2003. Pragmatic information content—how to measure the information in a route description. In: M. Duckham, M.F. Goodchild, and M.F. Worboys, eds.. Foundations in geographic information science. London: Taylor & Francis, 47–68.
   Google Scholar
• Gen, M. and Lin, L., 2005. Multiobjective hybrid genetic algorithm for bicriteria network design problem. Complexity International, 11, 73–83.
   Google Scholar
• Goldberg, D.E., 1989. Genetic algorithms in search, optimization, and machine learning. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
   Google Scholar
• Golledge, R.G. and Gärling, T., 2004. Cognitive maps and urban travel. In: D.A. Hensher, et al., eds. Handbook of transport geography and spatial systems. Amsterdam: Elsevier, 501–512.
   Google Scholar
• Hile, H., et al., 2008. Landmark-based pedestrian navigation from collections of geotagged photos. Proceedings of the 7th international conference on mobile and ubiquitous multimedia. Umeå, Sweden: ACM, 145–152.
   Google Scholar
• Holland, J.H., 1975. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press.
   Google Scholar
• Huang, B., Cheu, R.L., and Liew, Y.S., 2004. GIS and genetic algorithms for HAZMAT route planning with security considerations. International Journal of Geographical Information Science, 18 (8), 769–787. doi:10.1080/13658810410001705307
   Web of Science ®Google Scholar
• Hue, X., 1997. Genetic algorithms for optimization: background and applications. Scotland: The University of Edinburgh, Edinburgh Parallel Computing Centre.
   Google Scholar
• Jiang, B. and Liu, X., 2011. Computing the fewest-turn map directions based on the connectivity of natural roads. International Journal of Geographical Information Science, 25 (7), 1069–1082. doi:10.1080/13658816.2010.510799
   Web of Science ®Google Scholar
• Klippel, A., 2003. Wayfinding Choremes. In: W. Kuhn, M.F. Worboys, and S. Timpf, eds. Spatial information theory. Berlin: Springer, 301–315.
   Google Scholar
• Krisp, J.M. and Keler, A., 2015. Car navigation–computing routes that avoid complicated crossings. International Journal of Geographical Information Science, 29 (11), 1988–2000. doi:10.1080/13658816.2015.1053485
   Web of Science ®Google Scholar
• Le Yaouanc, J.M., Saux, É., and Claramunt, C., 2010. A semantic and language-based representation of an environmental scene. GeoInformatica, 14 (3), 333–352. doi:10.1007/s10707-010-0103-6
   Web of Science ®Google Scholar
• Levi, D.M., 2008. Crowding–an essential bottleneck for object recognition: A mini-review. Vision Research, 48 (5), 635–654. doi:10.1016/j.visres.2007.12.009
   PubMed Web of Science ®Google Scholar
• Li, R., et al., 2013. A genetic algorithm for multiobjective dangerous goods route planning. International Journal of Geographical Information Science, 27 (6), 1073–1089. doi:10.1080/13658816.2012.737921
   Web of Science ®Google Scholar
• Mark, D.M., 1986. Automated route selection for navigation. IEEE Aerospace and Electronic Systems Magazine, 1 (9), 2–5. doi:10.1109/MAES.1986.5005198
   Google Scholar
• May, A.J., et al., 2003. Pedestrian navigation aids: information requirements and design implications. Personal and Ubiquitous Computing, 7 (6), 331–338. doi:10.1007/s00779-003-0248-5
   Web of Science ®Google Scholar
• Michon, P.E. and Denis, M., 2001. When and why are visual landmarks used in giving directions? In: D.R. Montello, ed.. Spatial information theory. Berlin: Springer, 292–305.
   Google Scholar
• Millonig, A. and Schechtner, K., 2007a. Decision loads and route qualities for pedestrians — key requirements for the design of pedestrian navigation services. In: N. Waldau, et al., eds. Pedestrian and evacuation dynamics 2005. Berlin, Heidelberg: Springer, 109–118.
   Google Scholar
• Millonig, A. and Schechtner, K., 2007b. Developing landmark-based pedestrian-navigation systems. IEEE Transactions on Intelligent Transportation Systems, 8 (1), 43–49. doi:10.1109/TITS.2006.889439
   Web of Science ®Google Scholar
• Miyazaki, Y. and Kamiya, T., 2006. Pedestrian navigation system for mobile phones using panoramic landscape images. In: Proceedings of the 2005 symposium on applications and the internet (SAINT06). Phoenix, USA, 102–108.
   Google Scholar
• O’Neill, M.J., 1991. Evaluation of a conceptual model of architectural legibility. Environment and Behavior, 23 (3), 259–284. doi:10.1177/0013916591233001
   Web of Science ®Google Scholar
• Raubal, M. and Winter, S., 2002. Enriching wayfinding instructions with local landmarks. In: M.J. Egenhofer and D.M. Mark, eds. Geographic information science. Berlin: Springer, 243–259.
   Google Scholar
• Richter, K.F., 2009. Adaptable path planning in regionalized environments. In: K.S. Hornsby, et al., eds. Spatial information theory: 9th international conference, COSIT 2009 Aber Wrac’h, France, September 21–25 Proceedings. Berlin, Heidelberg: Springer, 453–470.
   Google Scholar
• Ross, T., May, A., and Thompson, S., 2004. The use of landmarks in pedestrian navigation instructions and the effects of context. In: S. Brewster and M. Dunlop, eds.. Mobile human-computer interaction - mobileHCI 2004. Berlin: Springer, 300–304.
   Google Scholar
• Schöning, J., et al., 2009. PhotoMap: using spontaneously taken images of public maps for pedestrian navigation tasks on mobile devices. In: Proceedings of the 11th international conference on human-computer interaction with mobile devices and services. Bonn, Germany.
   Google Scholar
• Takeda, K., et al., 2015. A visible corner-landmark based route finding algorithm for pedestrian navigation. In: 2015 IEEE 4th global conference on consumer electronics (GCCE), October. Osaka, Japan, 601–602.
   Google Scholar
• Xia, J.C., et al., 2008. The wayfinding process relationships between decision-making and landmark utility. Tourism Management, 29 (3), 445–457. doi:10.1016/j.tourman.2007.05.010
   Web of Science ®Google Scholar
• Zhang, X., et al., 2014. An assessment method for landmark recognition time in real scenes. Journal of Environmental Psychology, 40, 206–217. doi:10.1016/j.jenvp.2014.06.008
   Web of Science ®Google Scholar
• Zhu, S. and Levinson, D., 2015. Do people use the shortest path? An empirical test of Wardrop’s first principle. PloS one, 10 (8), 1–18.
   Web of Science ®Google Scholar