Abstract
With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.
Highlights
Real-time traffic flow state identification and prediction is one of the critical components of intelligent transportation system
To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory
As the first problem that needs to be solved, traffic flow state can be measured by level of service (LOS), which is first introduced in the 1965 Highway Capacity Manual (HCM 1965) [1]
Summary
Real-time traffic flow state identification and prediction is one of the critical components of intelligent transportation system. Canaud et al [18] presented a probability hypothesis density filtering based model for real-time traffic flow state prediction. The traffic big data has a higher accuracy to describe the relationships between traffic flow state of section s at time t and the others. Traffic big data can improve the efficiency of real-time traffic flow state identification and prediction. The method of real-time traffic flow state identification and prediction, which perceives ability to handle big data, is put forward in detail.
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