Using temporal detrending to observe the spatial correlation of traffic

Ermagun, Alireza; Chatterjee, Snigdhansu; Levinson, David

doi:10.1371/journal.pone.0176853

Cited by 34 publications

(22 citation statements)

References 31 publications

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“…They found that the spatial context can spread out very far. Similarly, Ermagun et al [ 48 ] developed a data de-trending algorithm to evaluate the spatial correlation between both competitive and complementary links in a grid-like traffic network in Minneapolis, USA. They found that a strong negative correlation happens in rush hours, while a positive correlation occurs between upstream and downstream links.…”

Section: Discussionmentioning

confidence: 99%

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

Luan

et al. 2017

Sensors

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Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks.

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Section: Discussionmentioning

confidence: 99%

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

Luan

et al. 2017

Sensors

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“…Proposed methods postulate two hypotheses: (1) traffic links are positively correlated and (2) near links are more related than distant links. The current study shows these are restrictive assumptions that misrepresent the flow of traffic links, given the competitive and complementary nature of links (Ermagun et al ).…”

Section: Introductionmentioning

confidence: 88%

An Introduction to the Network Weight Matrix

Ermagun

Levinson

2017

Geographical Analysis

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This study introduces the network weight matrix as a replacement for the spatial weight matrix to measure the spatial dependence between links of a network. This matrix stems from the concepts of betweenness centrality and vulnerability in network science. The elements of the matrix are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrix has distinctive characteristics, which are capable of reflecting spatial dependence between traffic links: (1) elements are allowed to have negative and positive values capturing the competitive and complementary nature of links, (2) diagonal elements are not fixed to zero, which takes the self-dependence of a link upon itself into consideration, and (3) elements not only reflect the spatial dependence based on the network structure, but they acknowledge the demand configuration as well. We verify the network weight matrix by modeling traffic flows in a 3 3 3 grid test network with 9 nodes and 24 directed links connecting 72 origin-destination (OD) pairs. Models encompassing the network weight matrix outperform both models without spatial components and models with the spatial weight matrix. The network weight matrix represents a more accurate and defensible spatial dependency between traffic links, and offers the potential to augment traffic flow prediction.

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“…Various randomnesses exist in travel speeds on real-world urban links. It has been reported that travel speeds could follow different probability distributions 10–12 , and there exist spatial and temporal correlations between travel speed on different links and in different time periods 13–15 . It is thus critical to share and publish the link travel speed dataset with real-world distributions and correlations.…”

Section: Background and Summarymentioning

confidence: 99%

Urban link travel speed dataset from a megacity road network

et al. 2019

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Link travel speeds in road networks are fundamental data in many research areas of traffic, transportation, and logistics. To support the research in these areas, we develop a dataset, containing the travel speeds on each road link and in different time periods together with the real road network map. The dataset is collected from a representative megacity in Western China, Chengdu. The road network of this city involves different urban road network structures. The dataset shows the realistic variations and randomness of urban link travel speeds. This enables the research of real data-driven decision-making problems in traffic, transportation and logistics areas.

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Using temporal detrending to observe the spatial correlation of traffic

Cited by 34 publications

References 31 publications

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

An Introduction to the Network Weight Matrix

Urban link travel speed dataset from a megacity road network

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