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Appl. Sci. 2017, 7(9), 923; doi:10.3390/app7090923

Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai

1
Department of Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi L2017E, UAE
2
Department of Nuclear Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi L2017E, UAE
3
Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi L2017E, UAE
*
Author to whom correspondence should be addressed.
Received: 30 July 2017 / Revised: 31 August 2017 / Accepted: 6 September 2017 / Published: 8 September 2017
(This article belongs to the Special Issue Application of Artificial Neural Networks in Geoinformatics)
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Abstract

Traditionally, departments of transportation (DOTs) have dispatched probe vehicles with dedicated vehicles and drivers for monitoring traffic conditions. Emerging assisted GPS (AGPS) and accelerometer-equipped smartphones offer new sources of raw data that arise from voluntarily-traveling smartphone users provided that their modes of transportation can correctly be identified. By introducing additional raster map layers that indicate the availability of each mode, it is possible to enhance the accuracy of mode detection results. Even in its simplest form, an artificial neural network (ANN) excels at pattern recognition with a relatively short processing timeframe once it is properly trained, which is suitable for real-time mode identification purposes. Dubai is one of the major cities in the Middle East and offers unique environments, such as a high density of extremely high-rise buildings that may introduce multi-path errors with GPS signals. This paper develops real-time mode identification ANNs enhanced with proposed mode availability geographic information system (GIS) layers, firstly for a universal mode detection and, secondly for an auto mode detection for the particular intelligent transportation system (ITS) application of traffic monitoring, and compares the results with existing approaches. It is found that ANN-based real-time mode identification, enhanced by mode availability GIS layers, significantly outperforms the existing methods. View Full-Text
Keywords: artificial neural network; traffic monitoring; GPS; GIS; mode detection artificial neural network; traffic monitoring; GPS; GIS; mode detection
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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. (CC BY 4.0).

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Byon, Y.-J.; Ha, J.S.; Cho, C.-S.; Kim, T.-Y.; Yeun, C.Y. Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai. Appl. Sci. 2017, 7, 923.

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