Grenoble Traffic Lab: An Experimental Platform for Advanced Traffic Monitoring and Forecasting [Applications of Control]

doi:10.1109/mcs.2015.2406657

Cited by 32 publications

(4 citation statements)

References 22 publications

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“…Details regarding the datasets are presented below. Grenoble traffic lab (GTL, [60]): This dataset is collected by the Grenoble traffic lab for real‐time traffic modeling, prediction, and control, which involves the traffic speed (km/h) collected from 21 road segments over 56 days (7/1‐8/25,2021) with a 5‐min‐resolution (i.e. the tensor of

21\times 288\times 56

) in Grenoble, France. Guangzhou: This dataset provides the average speed collected from 214 road segments in Guangzhou, China over 2 months (f8/1‐9/30, 2016) and the time window size is 10 min.…”

Section: Methodsmentioning

confidence: 99%

“…Grenoble traffic lab (GTL, [60]): This dataset is collected by the Grenoble traffic lab for real‐time traffic modeling, prediction, and control, which involves the traffic speed (km/h) collected from 21 road segments over 56 days (7/1‐8/25,2021) with a 5‐min‐resolution (i.e. the tensor of

21\times 288\times 56

) in Grenoble, France.…”

Section: Methodsmentioning

confidence: 99%

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Spatiotemporal traffic data imputation by synergizing low tensor ring rank and nonlocal subspace regularization

Ding

Zheng

2023

IET Intelligent Trans Sys

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Spatiotemporal traffic data usually suffers from missing entries in the data acquisition and transmission process. Existing imputation methods only consider the global/local structure of spatiotemporal traffic data, resulting in insufficient estimation performance. Fortunately, it is found that traffic data admits the nonlocal self‐similarity (NSS) prior. This paper incorporates the global and nonlocal low‐rank priors of traffic data and proposes a tensor completion model for spatiotemporal traffic data imputation. To be specific, the proposed method uses tensor ring (TR) decomposition with an enhanced representation capability to characterize the global low‐TR‐rank prior of traffic data, e.g. the correlation of sensor and time modes of the tensor (i.e. traffic data). An implicit plug‐and‐play (PnP)‐based regularization is further utilized to exploit the NSS prior, which depicts the nonlocal similar traffic data patterns. Furthermore, the proximal alternating minimization algorithm under the PnP framework is derived to solve this model. The experiment results on various datasets and missing scenarios show the superiority of the proposed model.

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21\times 288\times 56

Section: Methodsmentioning

confidence: 99%

21\times 288\times 56

) in Grenoble, France.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal traffic data imputation by synergizing low tensor ring rank and nonlocal subspace regularization

Ding

Zheng

2023

IET Intelligent Trans Sys

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“…12. This platform also provides real-time traffic indicators oriented towards the users of the city, traffic operators and researchers [49]. The collected data and computed indicators are available for download at the GTL website.…”

Section: Model Validation With Real Datamentioning

confidence: 99%

Multi-directional continuous traffic model for large-scale urban networks

Tumash

Canudas‐de‐Wit

Monache

2022

Transportation Research Part B: Methodological

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“…Thanks to its simplicity and its established analytical and numerical properties, this model is widely used in modern traffic engineering, see e.g. [6,14] for some recent applications. Nevertheless, like every model describing complex systems, it suffers form some limitations that limit its applicability.…”

mentioning

confidence: 99%

Well-posedness and finite volume approximations of the LWR traffic flow model with non-local velocity

Goatin¹,

Scialanga²

2016

NHM

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We consider an extension of the traffic flow model proposed by Lighthill, Whitham and Richards, in which the mean velocity depends on a weighted mean of the downstream traffic density. We prove well-posedness and a regularity result for entropy weak solutions of the corresponding Cauchy problem, and use a finite volume central scheme to compute approximate solutions. We perform numerical tests to illustrate the theoretical results and to investigate the limit as the convolution kernel tends to a Dirac delta function.

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Grenoble Traffic Lab: An Experimental Platform for Advanced Traffic Monitoring and Forecasting [Applications of Control]

Cited by 32 publications

References 22 publications

Spatiotemporal traffic data imputation by synergizing low tensor ring rank and nonlocal subspace regularization

Spatiotemporal traffic data imputation by synergizing low tensor ring rank and nonlocal subspace regularization

Multi-directional continuous traffic model for large-scale urban networks

Well-posedness and finite volume approximations of the LWR traffic flow model with non-local velocity

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