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Amirian, Pouria; Basiri, Anahid; Morley, Jeremy (2017)
Languages: English
Types: Unknown
Subjects: Computer Science - Artificial Intelligence
The explosive growth of the location-enabled devices coupled with the increasing use of Internet services has led to an increasing awareness of the importance and usage of geospatial information in many applications. The navigation apps (often called Maps), use a variety of available data sources to calculate and predict the travel time as well as several options for routing in public transportation, car or pedestrian modes. This paper evaluates the pedestrian mode of Maps apps in three major smartphone operating systems (Android, iOS and Windows Phone). In the paper, we will show that the Maps apps on iOS, Android and Windows Phone in pedestrian mode, predict travel time without learning from the individual's movement profile. In addition, we will exemplify that those apps suffer from a specific data quality issue which relates to the absence of information about location and type of pedestrian crossings. Finally, we will illustrate learning from movement profile of individuals using various predictive analytics models to improve the accuracy of travel time estimation.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Amirian, P. and Basiri, A. 2016. Landmark-Based Pedestrian Navigation Using Augmented Reality and Machine Learning. Progress in Cartography. Springer International Publishing. 451-465.
    • [2] Aylett, M.P. and Lawson, S. 2016. The Smartphone: A Lacanian Stain, A Tech Killer, and an Embodiment of Radical Individualism. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '16 (New York, New York, USA, 2016), 501-511.
    • Basiri, A., Amirian, P., Winstanley, A., Marsh, S., Moore, T. and Gales, G. 2016. Seamless Pedestrian Positioning and Navigation Using Landmarks. Journal of Navigation. (2016), 1-17.
    • 2011. Discovering personalized routes from trajectories.
    • SIGSPATIAL '11:Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks. 1, (2011), 33-40.
    • Chung, J. and Schmandt, C. 2009. Going my way: a useraware route planner. Proceedings of the 27th international conference on Human factors in computing systems - CHI 09 (New York, New York, USA, 2009), 1899.
    • Delling, D., Goldberg, A. V., Goldszmidt, M., Krumm, J., Talwar, K. and Werneck, R.F. 2015. Navigation made personal. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '15. (2015), 1-9.
    • Finnis, K.K. and Walton, D. 2008. Field observations to determine the influence of population size, location and individual factors on pedestrian walking speeds.
    • Ergonomics. 51, 6 (Jun. 2008), 827-842.
    • Furukawa, H. 2015. Empirical evaluation of the pedestrian navigation method for easy wayfinding. 2015 International Conference and Workshop on Computing and Communication (IEMCON) (Oct. 2015), 1-7.
    • Hastie, T., Tibshirani, R. and Friedman, J. 2009. The Elements of Statistical Learning. Springer Series in Statistics.
    • 2011. A comparison of three methods for assessing the walkability of the pedestrian environment. Journal of Transport Geography. 19, 6 (2011), 1500-1508.
    • Letchner, J., Krumm, J. and Horvitz, E. 2006. Trip router with individualized preferences (trip): Incorporating personalization into route planning. Proceedings of the National Conference on Artificial Intelligence. 21, 2 (2006), 1795.
    • Malmi, E. and Weber, I. 2016. You Are What Apps You Use: Demographic Prediction Based on User's Apps.
    • arXiv preprint arXiv:1603.00059. (2016).
    • de Montjoye, Y.-A., Hidalgo, C.A., Verleysen, M. and Blondel, V.D. 2013. Unique in the Crowd: The privacy bounds of human mobility. Scientific reports. 3, (2013), 1376.
    • 2015. Privacy-preserving inference of social relationships from location data. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '15 (New York, New York, USA, 2015), 1-4.
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