Think Like A Graph: Real-Time Traffic Estimation at City-Scale

Abstract

This paper presents a graph processing based traffic estimation system, GPTE, which is able to achieve high accuracy and high scalability to support city scale traffic estimation. GPTE benefits from its non-linear traffic correlation modeling and the graph-parallel processing framework built on clustered machines. By representing the road network as a property graph, GPTE decomposes the numerous computations involved in non-linear models to vertices and performs traffic estimation via neural network modeling and iterative information propagation. This paper presents our experiences in designing and implementing GPTE on top of the Spark, an emerging cluster computing framework. Extensive experiments are performed with real-world data input from Singapore's transport authority. Experimental results show that GPTE achieves as high as 88 percent accuracy in traffic estimation and up to $8\times$8× performance gain in computation efficiency with the optimization techniques applied. Comparison study demonstrates that GPTE outperforms the baseline solutions by 34 percent on accuracy and 46 percent on processing time.