Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending

Alireza Ermagun and David Levinson ()
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David Levinson: School of Civil Engineering, University of Sydney

No 166, Working Papers from University of Minnesota: Nexus Research Group

Abstract: This study examines the dependency between traffic links using a three-dimensional data detrending algorithm to build a network weight matrix in a real-world example. The network weight matrix reveals how links are spatially dependent in a complex network and detects the competi- tive and complementary nature of traffic links. We model the traffic flow of 140 traffic links in a sub-network of the Minneapolis - St. Paul highway system for both rush hour and non-rush hour time intervals, and validate the extracted network weight matrix. The results of the modeling indi- cate: (1) the spatial weight matrix is unstable over time-of-day, while the network weight matrix is robust in all cases and (2) the performance of the network weight matrix in non-rush hour traffic regimes is significantly better than rush hour traffic regimes. The results of the validation show the network weight matrix outperforms the traditional way of capturing spatial dependency between traffic links. Averaging over all traffic links and time, this superiority is about 13.2% in rush hour and 15.3% in non-rush hour, when only the 1st -order neighboring links are embedded in modeling. Aside from the superiority in forecasting, a remarkable capability of the network weight matrix is its stability and robustness over time, which is not observed in spatial weight matrix. In addition, this study proposes a naïve two-step algorithm to search and identify the best look-back time win- dow for upstream links. We indicate the best look-back time window depends on the travel time between two study detectors, and it varies by time-of-day and traffic link.

Keywords: Traffic Forecasting; Spatial correlation; Competitive links; Traffic Flow; Weight matrix (search for similar items in EconPapers)
JEL-codes: C21 C31 C33 R41 R48 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-for, nep-tre and nep-ure
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Published in Transportation Research part C. 104, 38-52.

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https://hdl.handle.net/11299/189878 First version, 2017 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:nex:wpaper:shorttermtrafficforecasting

DOI: 10.1016/j.trc.2019.04.014

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