01697519

Component

Urban traffic prediction is a challenging task due to the complexity of urban network. Many studies have been conducted to improve the accuracy of the urban traffic prediction, but the limitation still remains that their accuracy varies with location and time. To overcome this limitation, it is necessary to investigate in depth the various phenomena that change the traffic flow patterns. Among the phenomena, this study aims to analyze the effect of inherent variation in a link and spatiotemporal dependency between links in predicting travel speed in urban networks and to identify the factors that influence the two phenomena. The authors present three measures to quantify them, i.e., coefficient of variation, forecastable component analysis, and cross-correlation function. The results show that the variation and dependency have significant differences according to locations. The results also indicate that the effects of the dependency of the upstream and the downstream are different each other and the effects of the two phenomena vary depending on the prediction horizon of the prediction model. In the longer prediction horizon, in particular, the effect of the variation is increased, but the effect of the dependency between adjacent links is decreased gradually. The authors also identify the factors that affect the two phenomena and recommend guidelines for urban traffic prediction.

This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.

19-02528

English

5p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

References

Mathematical prediction; Traffic flow rate; Traffic speed; Travel time; Urban transportation; Variational inequalities

Data and Information Technology; Highways; Operations and Traffic Management

Transportation Research Board Annual Meeting 2019 Paper #19-02528

TRIS, TRB, ATRI

Dec 7 2018 9:30AM