Speed estimation of traffic flow using multiple kernel support vector regression

Jianli Xiao, Chao Wei and Yuncai Liu

Physica A: Statistical Mechanics and its Applications, 2018, vol. 509, issue C, 989-997

Abstract: Industrial loop detectors (ILDs) are the most common traffic detectors. In Shanghai, most of the ILDs are installed in a single loop way, which can detect various parameters, such as flow, saturation, and so on. However, they cannot detect the speed directly, which is one of the key inputs of intelligent transportation systems (ITS) for identifying the traffic state. Thus, this paper is dedicated to estimate speed accurately. It proposes a new algorithm that multiple kernel support vector regression (MKL-SVR) to complete this goal, which improves the accuracy and robustness of the speed estimation. Extensive experiments have been performed to evaluate the performances of MKL-SVR, compared with polynomial fitting, BP neural networks and SVR. All results indicate that the performances of MKL-SVR are the best and most robust.

Keywords: Speed; Estimation; Traffic flow; Multiple kernel learning; Support vector regression (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:509:y:2018:i:c:p:989-997

DOI: 10.1016/j.physa.2018.06.082

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