International Conference on Computer and Knowledge Engineering

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T-Rank: Graph Data Analytics for Urban Traffic Modeling

Authors :

Alireza Safarpour¹ Iman Gholampour² Amirhossain Aghazadeh Fard³ Seyed Mohammad Karbasi⁴

1- Department of Electrical Engineering. Sharif University of Technology. Tehran, Iran 2- Sharif University of Technology 3- Department of Electrical Engineering. Sharif University of Technology. Tehran, Iran 4- Department of Electrical Engineering. Sharif University of Technology. Tehran, Iran

Keywords :

Massive Data Analytics, Intelligent Transport Systems, urban Traffic, Spark and Hadoop

Abstract :

Evaluating specific points in urban passages and identifying the factors causing traffic jams are of particular importance. In this paper, we seek for a mathematical model of urban traffic that identifies the factors affecting traffic and evaluating their impact. Such models are capable of predicting the impact of the latent factors on the urban traffic. Our model uses graph evaluation algorithms, similar to ones used in web-page ranking. The model validation is done based on the data gathered by ANPR cameras that detect the plate numbers of the vehicles in the traffic zones. The data analytics in Apache Spark environment shows that our model archives an accuracy of more than 80%, in daily traffic density prediction. The model also predicts the ratio of the vehicles that park in the monitored zone with respect to the ones that pass through the zone accurately.