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A deep learning approach to real-time short-term traffic flow prediction with spatial-temporal features

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  • Tue, 10/09/2018 - 1:00pm - 3:00pm




A deep learning approach to real-time short-term traffic flow prediction with spatial-temporal features

MSc Thesis Proposal by:

Sindhuja Gutha

Date:  Tuesday, October 9th, 2018
Time:  1: 00 pm – 3:00 pm
Location: 3105, Lambton Tower

Abstract: In the realm of Intelligent Transportation Systems (ITS), accurate traffic flow prediction plays an important role in the traffic control and management. The study on the prediction of traffic flow has attracted considerable attention from many researchers in this field in the past three decades. The challenges in traffic flow prediction largely come from the complex spatial dependency on road networks and non-linear temporal dynamics with changing road conditions. Given that in recent years, deep learning based methods have demonstrated its competitiveness to the time series analysis which is an essential part of traffic prediction, we propose a deep learning based approach to handle the complicated nonlinear spatial and temporal correlations by combining the recurrent neural networks and the convolutional neural networks. Our hybrid prediction model will take into account some major traffic impact factors like weather data and seasonal factors. The performance of our proposed model will be evaluated on the dataset obtained from an array of Remote Transportation Microwave Sensors (RTMS) operated by Canada-U.S. Cross Border Institute (CBI) in the border corridor and also on the dataset of the Caltrans Performance Measurement System (PeMS).

Thesis Committee:
Internal Reader: Dr. Xiaobu Yuan   
External Reader: Dr. Hanna Maoh
Advisor: Dr. Jessica Chen
Co- Advisor: Dr. Mehdi Kargar

 



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