Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions

Ermagun, Alireza; Levinson, David

doi:10.1177/2399808318763368

Cited by 27 publications

(13 citation statements)

References 45 publications

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“…Williams [30] suggested the ARIMAX model with spatial explanatory variables, defined on the basis of road connectivity (upstream segments) and cross-correlation of traffic flows. Later, several other statistical approaches for the identification of spatial explanatory variables were suggested [31,32]. Another direction of methodological advances, the multivariate time series models, allows for the simultaneous modeling of many road segments and the data-driven identification of spatiotemporal relationships.…”

Section: Spatiotemporal Urban Traffic Forecasting Methodologymentioning

confidence: 99%

Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting

Pavlyuk

2020

Algorithms

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Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research.

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Section: Spatiotemporal Urban Traffic Forecasting Methodologymentioning

confidence: 99%

Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting

Pavlyuk

2020

Algorithms

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“…An alternative exogenous feature filtering approach, which is not directly based on connections between spatial locations, has been suggested by Ermagun and Levinson [13][14][15]. The introduced network weight matrix utilises graph characteristics of the road network such as betweenness centrality and vulnerability to discover complementary and competitive spatial links.…”

Section: Class 1: Exogenous Feature Filtering Methodsmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

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A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.

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“…Studies of spatial networks have to leverage the embedded direction of edges in order to characterize patterns (Barthelemy, ; Gastner & Newman, ). To incorporate the topology and structure of spatial networks, Ermagun and Levinson () introduced a network weight matrix in which the edge direction is explicitly considered. Road networks are a classic application of spatial networks; Boeing () explored the distribution of road directions in cities and discovered that directional patterns of roads varied among different cities, which can be used as one factor to characterize urban configurations.…”

Section: Where Direction Has Been Consideredmentioning

confidence: 99%

Making direction a first‐class citizen of Tobler's first law of geography

Zhu

Janowicz

Mai

2019

Transactions in GIS

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Waldo Tobler frequently reminded us that the law named after him was nothing more than calling for exceptions. This article discusses one of these exceptions. Spatial relations between points are frequently modeled as vectors in which both distance and direction are of equal prominence. However, in Tobler's first law of geography, such a relation is described only from the perspective of distance by relating the decreasing similarity of observations in some attribute space to their increasing distance in geographic space. Although anisotropic versions of many geographic analysis techniques, such as directional semivariograms, anisotropy clustering, and anisotropic point pattern analysis, have been developed over the years, direction remains on the level of an afterthought. We argue that, compared to distance, directional information is still under‐explored and anisotropic techniques are substantially less frequently applied in everyday GIS analysis. Commonly, when classical spatial autocorrelation indicators, such as Moran's I, are used to understand a spatial pattern, the weight matrix is only built from distance, without direction being considered. Similarly, GIS operations, such as buffering, do not take direction into account either, with distance in all directions being treated equally. In reality, meanwhile, particularly in urban structures and when processes are driven by the underlying physical geography, direction plays an essential role. In this article we ask whether the development of early GIS, data (sample) sparsity, and Tobler's law lead to a theory‐induced blindness for the role of direction. If so, is it possible to envision direction becoming a first‐class citizen of equal importance to distance instead of being an afterthought only considered when the deviation from a perfect circle becomes too obvious to be ignored?

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Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions

Cited by 27 publications

References 45 publications

Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting

Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Making direction a first‐class citizen of Tobler's first law of geography

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