Master Thesis Defense - March 18, 2020: Utilizing Computer Vision and Data Mining for Predicting Road Traffic Congestion
Wednesday, March 18, 2020 at 10:15 a.m.
You are invited to attend the following M.A.Sc. (Quality Systems Engineering) thesis examination.
Dr. C. Wang, Chair
Dr. A. Awasthi, Supervisor
Dr. F. Naderkhani, CIISE Examiner
Dr. C. Alecsandru, External Examiner (BCEE)
Traffic Congestion wastes time and energy, which are the two most valuable commodities of the current century. It happens when too many vehicles try to use a transportation infrastructure without having enough capacity. However, researches indicate that adding extra lane without studying the future consequences does not improve the situation. Our goal is to add another layer of information to the traffic data, find which type of vehicles are contributing to road traffic congestion, and predict future road traffic congestions and demands based on the historical data.
We collected more than 400,000 images from traffic cameras installed in Autoroute 40, in the city of Montreal. The images were collected for five consecutive weeks from different locations from April 14, 2019, up until May 18, 2019. To process these images and extract useful information out of them, we created an object detection and classification model using the Faster RCNN algorithm. Our goal was to be able to detect different types of vehicles and see if we have traffic congestion in an image. In order to improve the accuracy and reduce the error rate, we provided multiple examples with different conditions to the model. By introducing blurry, rainy, and low light images to the model, we managed to build a robust model that could do the detection and classification task with excellent accuracy.
Furthermore, by extracting the information from the collected images, we created a dataset of the number of vehicles in each location. After analyzing and visualizing the data, we find out the most congested areas, the behavior of the traffic flow during the day, peak hours, the contribution of each type of vehicle to the traffic, seasonality of the data, and where we can see each type of vehicle the most.
Finally, we managed to predict the total number of congestion incidents for seven days based on historical data. Besides, we were able to predict the total number of different types of vehicles on the road as well. In order to do this task, we developed multiple Regression, Deep Learning, and Time Series Forecasting models and trained them with our vehicle count dataset. Based on the experimental results, we were able to get the best predictions with the Deep Learning models and succeeded in predicting future road traffic congestions with excellent accuracy.