Using Temporal Detrending to Observe the Spatial Correlation of Traffic
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Abstract
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DOI: 10.1371/journal.pone.0176853
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Other versions of this item:
- Alireza Ermagun & Snigdhansu Chatterjee & David Levinson, 2017. "Using temporal detrending to observe the spatial correlation of traffic," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-21, May.
References listed on IDEAS
- Qiang Shang & Ciyun Lin & Zhaosheng Yang & Qichun Bing & Xiyang Zhou, 2016. "A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-25, August.
- Anastasios Noulas & Salvatore Scellato & Renaud Lambiotte & Massimiliano Pontil & Cecilia Mascolo, 2012. "A Tale of Many Cities: Universal Patterns in Human Urban Mobility," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-10, May.
- Chengbin Peng & Xiaogang Jin & Ka-Chun Wong & Meixia Shi & Pietro Liò, 2012. "Collective Human Mobility Pattern from Taxi Trips in Urban Area," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-8, April.
Citations
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Cited by:
- Alireza Ermagun & David Levinson, 2017. "Spatiotemporal Short-term Traffic Forecasting using the Network Weight Matrix and Systematic Detrending," Working Papers 000166, University of Minnesota: Nexus Research Group.
- Mengying Cui & David Levinson, 2018.
"Accessibility analysis of risk severity,"
Transportation, Springer, vol. 45(4), pages 1029-1050, July.
- Mengying Cui & David Levinson, 2015. "Accessibility Analysis of Risk Severity," Working Papers 000134, University of Minnesota: Nexus Research Group.
- Alireza Ermagun & David Levinson, 2017. "Traffic Flow Variation and Network Structure," Working Papers 000167, University of Minnesota: Nexus Research Group.
- Lei, Da & Cheng, Long & Wang, Pengfei & Chen, Xuewu & Zhang, Lin, 2024. "Identifying service bottlenecks in public bikesharing flow networks," Journal of Transport Geography, Elsevier, vol. 116(C).
- Alireza Ermagun & David M Levinson, 2019.
"Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions,"
Environment and Planning B, , vol. 46(9), pages 1684-1705, November.
- Alireza Ermagun & David Levinson, 2019. "Development and Application of the Network Weight Matrix to Predict Traffic Flow for Congested and Uncongested Conditions," Working Papers 2019-04, University of Minnesota: Nexus Research Group.
- Dongqing Zhang & Zhaoxia Guo, 2019. "On the Necessity and Effects of Considering Correlated Stochastic Speeds in Shortest Path Problems Under Sustainable Environments," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
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More about this item
Keywords
statistics; spatial weight matrix; traffic forecasting; network weight matrix Publication status: Published in PLoS One. 12(5): e0176853.;All these keywords.
JEL classification:
- R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
- R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
NEP fields
This paper has been announced in the following NEP Reports:- NEP-FOR-2017-09-17 (Forecasting)
- NEP-TRE-2017-09-17 (Transport Economics)
- NEP-URE-2017-09-17 (Urban and Real Estate Economics)
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