Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft-Reference-Cited by-同舟云学术

Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft

Published:2016 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science - IWCTS '16
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Author:

Amirian Pouria¹,Basiri Anahid²,Morley Jeremy¹

Affiliation:

1. Ordnance Survey of Great Britain, Products and Innovation, Southampton, UK

2. The University of Nottingham, Nottingham, UK

Publisher

ACM Press

Reference14 articles.

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.

3. 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.

4. Chang, K.-P., Wei, L.-Y., Yeh, M.-Y. and Peng, W.-C. 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.

5. Chung, J. and Schmandt, C. 2009. Going my way: a user-aware route planner.Proceedings of the 27th international conference on Human factors in computing systems - CHI 09(New York, New York, USA, 2009), 1899.

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