A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting

Cai, Pinlong; Wang, Yunpeng; Lu, Guangquan; Chen, Peng; Ding, Chuan; Sun, Jian

doi:10.1016/j.trc.2015.11.002

Cited by 350 publications

(185 citation statements)

References 24 publications

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“…Main drawback can be assumption of an underlying DGP distribution, higher computational time in case of high dimensional state transition, and need of retraining to update estimated state distributions. Location depended k-NN method is presented by (Cai et al (2016)) in an attempt to address similarity in historical data and spatial correlations. The method uses large datasets for up to an hour-step speed predictions.…”

Section: Introductionmentioning

confidence: 99%

Short-term freeway traffic parameter prediction: Application of grey system theory models

Bezuglov

Comert

2016

Expert Systems with Applications

170

106

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Section: Introductionmentioning

confidence: 99%

Short-term freeway traffic parameter prediction: Application of grey system theory models

Bezuglov

Comert

2016

Expert Systems with Applications

170

106

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“…Different terms, such as spatial dependency/relationship in traffic [1,2] and spatial correlation [3][4][5][6], are used in the literature to express such relationship between neighboring roads. In this paper, we use the term traffic interaction to describe the traffic influence between neighboring roads, which is fundamentally caused by the dynamic vehicle movements from one road to another.…”

Section: Introductionmentioning

confidence: 99%

Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes

Kang

Gao

Qiu

et al. 2017

IJGI

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Good characterization of traffic interactions among urban roads can facilitate traffic-related applications, such as traffic control and short-term forecasting. Most studies measure the traffic interaction between two roads by their topological distance or the correlation between their traffic variables. However, the distance-based methods neglect the spatial heterogeneity of roads' traffic interactions, while the correlation-based methods cannot capture the non-linear dependency between two roads' traffic variables. In this paper, we propose a novel approach called Road2Vec to quantify the implicit traffic interactions among roads based on large-scale taxi operating route data using a Word2Vec model from the natural language processing (NLP) field. First, the analogy between transportation elements (i.e., road segment, travel route) and NLP terms (i.e., word, document) is established. Second, the real-valued vectors for road segments are trained from massive travel routes using the Word2Vec model. Third, the traffic interaction between any pair of roads is measured by the cosine similarity of their vectors. A case study on short-term traffic forecasting is conducted with artificial neural network (ANN) and support vector machine (SVM) algorithms to validate the advantages of the presented method. The results show that the forecasting achieves a higher accuracy with the support of the Road2Vec method than with the topological distance and traffic correlation based methods. We argue that the Road2Vec method can be effectively utilized for quantifying complex traffic interactions among roads and capturing underlying heterogeneous and non-linear properties.

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“…In recent years, the applications of computational intelligence-based models were the focal point in literature including Neural and Bayesian Networks [18,19], Support Vector Regression (SVR) [20], k-nearest neighbours [21,22], regression trees [23,24], etc. This type of models was considered as inevitable, particularly as most classical approaches have been shown inadequate under unstable traffic conditions and complex road settings when and where it is needed [25].…”

Section: General Description Of Srmentioning

confidence: 99%

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

Li¹,

Fratrović

Zhang³

et al. 2017

PROMET

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show abstract

A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting

Cited by 350 publications

References 24 publications

Short-term freeway traffic parameter prediction: Application of grey system theory models

Short-term freeway traffic parameter prediction: Application of grey system theory models

Road2Vec: Measuring Traffic Interactions in Urban Road System from Massive Travel Routes

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

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