Login | Register

Utilizing Computer Vision and Data Mining for Predicting Road Traffic Congestion

Title:

Utilizing Computer Vision and Data Mining for Predicting Road Traffic Congestion

Amoei, Mohsen (2020) Utilizing Computer Vision and Data Mining for Predicting Road Traffic Congestion. Masters thesis, Concordia University.

[thumbnail of Amoei_MASc_S2020.pdf]
Preview
Text (application/pdf)
Amoei_MASc_S2020.pdf - Accepted Version
Available under License Spectrum Terms of Access.
14MB

Abstract

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 congestion 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 congestion with excellent accuracy.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Amoei, Mohsen
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:2 March 2020
Thesis Supervisor(s):Awasthi, Anjali
Keywords:Computer Vision, Data Mining, Time Series Forecasting, Road Traffic Congestion, Deep Learning
ID Code:986671
Deposited By: Mohsen Amoei
Deposited On:25 Jun 2020 19:51
Last Modified:25 Jun 2020 19:51

References:

S Agatonovic-Kustrin and R Beresford. Basic concepts of artificial neural network (ann)
modeling and its application in pharmaceutical research. Journal of pharmaceutical and
biomedical analysis, 22(5):717–727, 2000.
Dave Anderson and George McNeill. Artificial neural networks technology. Kaman Sciences
Corporation, 258(6):1–83, 1992.
ARIMA. Auto ARIMA for python. http://alkaline-ml.com/pmdarima/, 2019.
Accessed: 2019-11-13.
Shashank Bharadwaj, Sudheer Ballare, Chandel MK Rohit, and MK Chandel. Impact
of congestion on greenhouse gas emissions for road transport in mumbai metropolitan
region. Transportation Research Procedia, 25:3538–3551, 2017.
Christopher M Bishop et al. Neural networks for pattern recognition. Oxford university
press, 1995.
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. Time series
analysis: forecasting and control. John Wiley & Sons, 2015.
Leo Breiman. Random forests. Machine learning, 45(1):5–32, 2001.
Yeong-Hyeon Byeon and Keun-Chang Kwak. A performance comparison of pedestrian
detection using faster rcnn and acf. In 2017 6th IIAI International Congress on Advanced
Applied Informatics (IIAI-AAI), pages 858–863. IEEE, 2017.
Chengtao Cai, Boyu Wang, and Xin Liang. A new family monitoring alarm system based
on improved yolo network. In 2018 Chinese Control And Decision Conference (CCDC),
pages 4269–4274. IEEE, 2018.
Peng Chen, Hongyong Yuan, and Xueming Shu. Forecasting crime using the arima model.
In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery,
volume 5, pages 627–630. IEEE, 2008.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A largescale
hierarchical image database. In 2009 IEEE conference on computer vision and
pattern recognition, pages 248–255. Ieee, 2009.
Zhen Dong, Yuwei Wu, Mingtao Pei, and Yunde Jia. Vehicle type classification using a
semisupervised convolutional neural network. IEEE transactions on intelligent transportation
systems, 16(4):2247–2256, 2015.
Timothy Dozat. Incorporating nesterov momentum into adam. 2016.
Gilles Duranton and Matthew A Turner. The fundamental law of road congestion: Evidence
from us cities. American Economic Review, 101(6):2616–52, 2011.
Quanfu Fan, Lisa Brown, and John Smith. A closer look at faster r-cnn for vehicle detection.
In 2016 IEEE intelligent vehicles symposium (IV), pages 124–129. IEEE, 2016.
Carolina Garcia-Ascanio and Carlos Maté. Electric power demand forecasting using interval
time series: A comparison between var and imlp. Energy Policy, 38(2):715–725,
2010.
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics:
The kitti dataset. The International Journal of Robotics Research, 32(11):1231–1237,
2013.
Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer
vision, pages 1440–1448, 2015.
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies
for accurate object detection and semantic segmentation. In Proceedings of the IEEE
conference on computer vision and pattern recognition, pages 580–587, 2014.
Hua Gong, Yong Zhang, Ke Xu, and Fang Liu. A multitask cascaded convolutional neural
network based on full frame histogram equalization for vehicle detection. In 2018
Chinese Automation Congress (CAC), pages 2848–2853. IEEE.
Google-Maps. Google maps. https://www.google.ca/maps/, 2019. Accessed:
2019-11-13.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image
recognition. In Proceedings of the IEEE conference on computer vision and pattern
recognition, pages 770–778, 2016.
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation,
9(8):1735–1780, 1997.
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko,WeijunWang, Tobias
Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional
neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
Shih-Chung Hsu, Chung-Lin Huang, and Cheng-Hung Chuang. Vehicle detection using
simplified fast r-cnn. In 2018 International Workshop on Advanced Image Technology
(IWAIT), pages 1–3. IEEE, 2018.
Jonathan Huang, Vivek Rathod, Derek Chow, Chen Sun, Menglong Zhu, Matthew
Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song,
Sergio Guadarrama, Kovalevskyi Viacheslav Uijlings, Jasper and, and Kevin Murphy.
Github repository of tensorflow object detection api. https://github.com/
tensorflow/models/tree/master/research/object_detection,
2019. Accessed: 2019-11-13.
David H Hubel and Torsten NWiesel. Receptive fields, binocular interaction and functional
architecture in the cat’s visual cortex. The Journal of physiology, 160(1):106–154, 1962.
Huaizu Jiang and Erik Learned-Miller. Face detection with the faster r-cnn. In 2017 12th
IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017),
pages 650–657. IEEE, 2017.
Kazuya Kawakami. Supervised sequence labelling with recurrent neural networks. Ph. D.
thesis, 2008.
Keras. Keras: The python deep learning library. https://keras.io/, 2019. Accessed:
2019-11-13.
Janaki Koirala et al. Food object recognition: An application of deep learning. 2018.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep
convolutional neural networks. In Advances in neural information processing systems,
pages 1097–1105, 2012.
Yann LeCun, Yoshua Bengio, et al. Convolutional networks for images, speech, and time
series. The handbook of brain theory and neural networks, 3361(10):1995, 1995.
PIERRE LEGENDRE. Model ii regression user’s guide, r edition. R Vignette, 14, 1998.
Jonathan I Levy, Jonathan J Buonocore, and Katherine Von Stackelberg. Evaluation of
the public health impacts of traffic congestion: a health risk assessment. Environmental
health, 9(1):65, 2010.
Andy Liaw, MatthewWiener, et al. Classification and regression by randomforest. R news,
2(3):18–22, 2002.
Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos. Anomaly detection
in crowded scenes. In 2010 IEEE Computer Society Conference on Computer Vision and
Pattern Recognition, pages 1975–1981. IEEE, 2010.
Matplotlib. Matplotlib website. https://matplotlib.org/, 2019. Accessed: 2019-
11-13.
Wanli Min and LauraWynter. Real-time road traffic prediction with spatio-temporal correlations.
Transportation Research Part C: Emerging Technologies, 19(4):606–616, 2011.
Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann
machines. In Proceedings of the 27th international conference on machine learning
(ICML-10), pages 807–814, 2010.
NOAA. National centers for environmental information. https://www.ncdc.noaa.
gov/, 2019. Accessed: 2019-11-13.
Numpy. Numpy website. https://numpy.org/, 2019. Accessed: 2019-11-13.
Ping-Feng Pai and Chih-Sheng Lin. A hybrid arima and support vector machines model in
stock price forecasting. Omega, 33(6):497–505, 2005.
JC Palomares-Salas, JJG De La Rosa, JG Ramiro, J Melgar, A Aguera, and A Moreno.
Arima vs. neural networks for wind speed forecasting. In 2009 IEEE International
Conference on Computational Intelligence for Measurement Systems and Applications,
pages 129–133. IEEE, 2009.
Bei Pan, Ugur Demiryurek, and Cyrus Shahabi. Utilizing real-world transportation data for
accurate traffic prediction. In 2012 IEEE 12th International Conference on Data Mining,
pages 595–604. IEEE, 2012.
Pandas. Pandas website. https://pandas.pydata.org/, 2019. Accessed: 2019-
11-13.
Markos Papageorgiou, Christina Diakaki, Vaya Dinopoulou, Apostolos Kotsialos, and Yibing
Wang. Review of road traffic control strategies. Proceedings of the IEEE, 91(12):
2043–2067, 2003.
Polynomial-Regression. Polynomial regression, daniel borcard, département de sciences
biologiques, université de montréal. http://biol09.biol.umontreal.ca/
PLcourses/Polynomial_regression.pdf, 2019. Accessed: 2019-11-26.
Quebec511. Québec 511 website. https://www.quebec511.info/, 2019. Accessed:
2019-11-13.
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards realtime
object detection with region proposal networks. In Advances in neural information
processing systems, pages 91–99, 2015.
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning internal representations
by error propagation. Technical report, California Univ San Diego La Jolla
Inst for Cognitive Science, 1985.
Astrid Schneider, Gerhard Hommel, and Maria Blettner. Linear regression analysis: part
14 of a series on evaluation of scientific publications. Deutsches Ärzteblatt International,
107(44):776, 2010.
Scikit-learn. scikit-learn: Machine learning in python. https://scikit-learn.
org/, 2019. Accessed: 2019-11-13.
Seaborn. Seaborn: statistical data visualization. https://seaborn.pydata.org/,
2019. Accessed: 2019-11-13.
Yantai Shu, Zhigang Jin, Lianfang Zhang, LeiWang, and OliverWWYang. Traffic prediction
using farima models. In 1999 IEEE International Conference on Communications
(Cat. No. 99CH36311), volume 2, pages 891–895. IEEE, 1999.
Sima Siami-Namini, Neda Tavakoli, and Akbar Siami Namin. A comparison of arima and
lstm in forecasting time series. In 2018 17th IEEE International Conference on Machine
Learning and Applications (ICMLA), pages 1394–1401. IEEE, 2018.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale
image recognition. arXiv preprint arXiv:1409.1556, 2014.
Daniel Soutner and Ludˇek Müller. Application of lstm neural networks in language modelling.
In International Conference on Text, Speech and Dialogue, pages 105–112.
Springer, 2013.
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna.
Rethinking the inception architecture for computer vision. In Proceedings of the IEEE
conference on computer vision and pattern recognition, pages 2818–2826, 2016.
Lei Tai and Ming Liu. Deep-learning in mobile robotics-from perception to control systems:
A survey on why and why not. arXiv preprint arXiv:1612.07139, 2016.
Sean J Taylor and Benjamin Letham. Forecasting at scale. The American Statistician, 72
(1):37–45, 2018.
Tensorflow. Tensorflow website. https://www.tensorflow.org/, 2019. Accessed:
2019-11-13.
TomTom. Tomtom traffic index. https://www.tomtom.com/en_gb/trafficindex/
ranking/?country=CA,MX,US, 2019. Accessed: 2020-02-26.
Fang-Mei Tseng and Gwo-Hshiung Tzeng. A fuzzy seasonal arima model for forecasting.
Fuzzy Sets and Systems, 126(3):367–376, 2002.
Tzutalin. Labelimg. https://github.com/tzutalin/labelImg, 2015. Accessed:
2019-11-13.
Dong-wei Xu, Yong-dong Wang, Li-min Jia, Yong Qin, and Hong-hui Dong. Real-time
road traffic state prediction based on arima and kalman filter. Frontiers of Information
Technology & Electronic Engineering, 18(2):287–302, 2017.
Ramin Yasdi. Prediction of road traffic using a neural network approach. Neural computing
& applications, 8(2):135–142, 1999.
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features
in deep neural networks? In Advances in neural information processing systems, pages
3320–3328, 2014.
G Peter Zhang. Time series forecasting using a hybrid arima and neural network model.
Neurocomputing, 50:159–175, 2003.
Xiaotong Zhao, Wei Li, Yifang Zhang, T Aaron Gulliver, Shuo Chang, and Zhiyong Feng.
A faster rcnn-based pedestrian detection system. In 2016 IEEE 84th Vehicular Technology
Conference (VTC-Fall), pages 1–5. IEEE, 2016.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top