Using Temporal Detrending to Observe the Spatial Correlation of Traffic

Alireza Ermagun, Snigdhansu Chatterjee and David Levinson

No 161, Working Papers from University of Minnesota: Nexus Research Group

Abstract: This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis—St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models.

Keywords: statistics; spatial weight matrix; traffic forecasting; network weight matrix Publication status: Published in PLoS One. 12(5): e0176853. (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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Citations: View citations in EconPapers (6)

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https://doi.org/10.1371/journal.pone.0176853 First version, 2017 (application/pdf)

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Journal Article: Using temporal detrending to observe the spatial correlation of traffic (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:nex:wpaper:detrending

DOI: 10.1371/journal.pone.0176853

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