Three-stage approach for dynamic traffic temporal-spatial model

• Published in: Journal of Central South University Vol. 23; no. 10; pp. 2728 - 2734
• Main Authors: Lu, Hua-pu, Sun, Zhi-yuan, Qu, Wen-cong
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.10.2016; Springer Nature B.V
• Subjects: Calibration; Civil; Correlation; Energy and Traffic Engineering; Engineering; Geological; Machine learning; Metallic Materials; Regression analysis; Regression models; Roads; Simplification; Traffic flow; dynamic traffic flow; temporal-spatial model; big-data driven
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-016-3334-3

In order to describe the characteristics of dynamic traffic flow and improve the robustness of its multiple applications, a dynamic traffic temporal-spatial model (DTTS) is established. With consideration of the temporal correlation, spatial correlation and historical correlation, a basic DTTS model is built. And a three-stage approach is put forward for the simplification and calibration of the basic DTTS model. Through critical sections pre-selection and critical time pre-selection, the first stage reduces the variable number of the basic DTTS model. In the second stage, variable coefficient calibration is implemented based on basic model simplification and stepwise regression analysis. Aimed at dynamic noise estimation, the characteristics of noise are summarized and an extreme learning machine is presented in the third stage. A case study based on a real-world road network in Beijing, China, is carried out to test the efficiency and applicability of proposed DTTS model and the three-stage approach.