Data compression techniques for urban traffic data

Asif, Muhammad; Srinivasan, K.; Dauwels, Justin; Jaillet, Patrick

doi:10.1109/civts.2013.6612288

Cited by 30 publications

(31 citation statements)

References 30 publications

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“…However, no analysis in terms of reconstruction efficiency of NMF was provided. In a related study, we compared the reconstruction efficiency of different low-dimensional models which were obtained from matrix and tensor based subspace methods for a network comprising of around 6000 road segments [23].…”

Section: Introductionmentioning

confidence: 99%

Near-Lossless Compression for Large Traffic Networks

Asif

Srinivasan

Mitrović

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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Abstract-With advancements in sensor technologies, intelligent transportation systems (ITS) can collect traffic data with high spatial and temporal resolution. However, the size of the networks combined with the huge volume of the data puts serious constraints on the system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this study, we analyze the reconstruction efficiency of several low-dimensional models for large and diverse networks. The compression performed by low-dimensional models is lossy in nature. To address this issue, we propose a near-lossless compression method for traffic data by applying the principle of lossy plus residual coding. To this end, we first develop low-dimensional model of the network. We then apply Huffman coding in the residual layer. The resultant algorithm guarantees that the maximum reconstruction error will remain below a desired tolerance limit. For analysis, we consider a large and heterogeneous test network comprising of more than 18000 road segments. The results show that the proposed method can efficiently compress data obtained from a large and diverse road network, while maintaining the upper bound on the reconstruction error.

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Section: Introductionmentioning

confidence: 99%

Near-Lossless Compression for Large Traffic Networks

Asif

Srinivasan

Mitrović

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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“…In another study, Asif et al applied different subspace methods for compression of traffic speed [11]. These studies showed that subspace methods such as PCA and discrete cosine transform (DCT) can efficiently compress traffic data [11]. However, they fail to provide insight about traffic behavior at specific roads and time periods.…”

Section: Introductionmentioning

confidence: 99%

“…Djukic et al applied PCA on small network with OD pair data [9,10]. In another study, Asif et al applied different subspace methods for compression of traffic speed [11]. These studies showed that subspace methods such as PCA and discrete cosine transform (DCT) can efficiently compress traffic data [11].…”

Section: Introductionmentioning

confidence: 99%

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CUR decomposition for compression and compressed sensing of large-scale traffic data

Mitrović

Asif

Rasheed

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Intelligent Transportation Systems (ITS) often operate on large road networks, and typically collect traffic data with high temporal resolution. Consequently, ITS need to handle massive volumes of data, and methods to represent that data in more compact representations are sorely needed. Subspace methods such as Principal Component Analysis (PCA) can create accurate low-dimensional models. However, such models are not readily interpretable, as the principal components usually involve a large number of links in the traffic network. In contrast, the CUR matrix decomposition leads to low-dimensional models where the components correspond to individual links in the network; the resulting models can be easily interpreted, and can also be used for compressed sensing of the traffic network. In this paper, the CUR matrix decomposition is applied for two purposes: (1) compression of traffic data; (2) compressed sensing of traffic data. In the former, only data from a "random" subset of links and time instances is stored. In the latter, data for the entire traffic network is inferred from measurements at a "random" subset of links. Numerical results for a large traffic network in Singapore demonstrate the feasibility of the proposed approach.

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“…One method of representing a road network in a compressed form would be to use principal component analysis (PCA) [25]- [27]. This compressed form is given as basis vectors and latent variables.…”

Section: Introductionmentioning

confidence: 99%

Traffic state estimation via a particle filter over a reduced measurement space

Hawes

Amer

Mihaylova

2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-Traffic control and vehicle route planning require accurate estimates of the traffic state in order to be successfully implemented. This estimation problem can be solved by using particle filters in conjunction with macroscopic traffic models such as the stochastic compositional model. The accuracy of the estimates can be decreased for road segments where there are no measurements available. However, the inclusion of measurements for all segment boundaries carries a computational cost associated with the evaluation of the likelihood function required by the particle filter. To solve this problem, this paper proposes using the column based matrix decomposition method to select the most significant locations in the road network. This results in the particle filter being applied over a reduced measurement space, allowing a trade-off between computational efficiency and estimation accuracy to be achieved. A performance evaluation based on a simulated stretch of road is provided to validate the proposed method. It shows that by selecting half the original number of measurements, the computational time is reduced by approximately 9% without significantly decreasing the estimation accuracy. A more significant improvement in terms of savings in computational complexity can be expected when considering larger urban road networks.

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Data compression techniques for urban traffic data

Cited by 30 publications

References 30 publications

Near-Lossless Compression for Large Traffic Networks

Near-Lossless Compression for Large Traffic Networks

CUR decomposition for compression and compressed sensing of large-scale traffic data

Traffic state estimation via a particle filter over a reduced measurement space

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