01664313

Component

https://doi.org/10.1177/0361198118774735

Real-time traffic control systems are widely implemented on roadways around the world as a measure to improve freeway mobility. However, the systems, which rely on data from road-side and on-road sensors and other electronic equipment, continue to suffer from issues related to missing and erroneous data. While many data imputation methods are documented in the related literature, traffic control systems still lack an imputation method that is applicable in practice, accurate in imputation, and simple in computation. In response, this paper puts forth a linear imputation model that considers both temporal traffic trend and spatial detector correlations. To adapt the model to dynamic traffic variations, the imputation method was equipped with an online calibration module. The proposed imputation method was evaluated with field data from two stations on the Whitemud Drive, a busy urban freeway in Edmonton, Alberta, Canada. The proposed model benefited from its time-of-day temporal trend and outperforms the previous model that considers only spatial correlations. Moreover, the online calibration module was effective in improving imputation accuracy. Finally, the sensitivity of imputation performance was analyzed. The results show that the imputation with online calibration is more sensitive to missing data ratios than that with offline calibration. The sensitivity analysis revealed that imputation with online calibration is more suitable for online imputation in traffic control implementations.

18-03997

English

pp 44-54

2018-12

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

Figures (6) ; References (24) ; Tables (3)

Calibration; Data fusion; Freeway management systems; Methodology; Sensitivity analysis; Traffic control

Edmonton (Canada)

Data and Information Technology; Highways; Operations and Traffic Management

TRIS, TRB, ATRI

Jan 8 2018 10:59AM

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