Urban traffic flow prediction using a fuzzy-neural approach

Yin, Hujun; Wong, S. C.; Xu, Jie; Wong, C.K.

doi:10.1016/s0968-090x(01)00004-3

Cited by 374 publications

(162 citation statements)

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“…How to properly exploit the information of weights to improve the prediction accuracy is still an unsolved problem. A harder problem is to predict the weights of links, which is relevant to the traffic prediction for urban transportation and air transportation systems [152]. We are expecting some variants of link prediction algorithms can also contribute to this domain.…”

Section: Discussionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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Section: Discussionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

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“…To this end, we exploit the predictability of large-scale urban vehicular traffic flows, which are known to follow common mobility patterns over a road topology [3], [4], [5]. By studying such traffic dynamics, we determine the way vehicular flows spread over the streets layout and employ this information to guide the APs placement.…”

Section: Access Points Deploymentmentioning

confidence: 99%

Cooperative download in urban vehicular networks

Fiore

Barceló-Ordinas

2009

2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems

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Abstract-We target urban scenarios where vehicular users can download large files from road-side Access Points (APs), and define a framework to exploit opportunistic encounters between mobile nodes to increase their transfer rate. We first devise a technique for APs deployment, based on vehicular traffic flows analysis, which fosters cooperative download. Then, we propose and evaluate different algorithms for carriers selection and chunk scheduling in carry&forward data transfers. Results obtained under realistic road topology and vehicular mobility conditions show that coupling our APs deployment scheme with probabilistic carriers selection and redundant chunk scheduling yields a worstcase 2x gain in the average download rate with respect to direct download, as well as a lOx reduction in the rate of undelivered chunks with respect to a blind carry&forward.

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“…In this branch, Kalman filter [14] and ARMA (Autoregressive Moving Average) [15], originated from state space theory, are popularly used to predict linear variation tendency of traffic flows [14][15] [19]. In [20][21] [22], neural networks [20] [21] and hybrid non-linear dynamic systems [22] are used to approximate short-term non-linear fluctuations of traffic flow states. Due to the intrinsic multiple-input and multiple-output (MIMO) structures, neural networks intrinsically integrate spatio-temporal correlations between local link segments.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization

Han

Moutarde

2014

Int. J. ITS Res.

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In this paper, we present our work on clustering and prediction of temporal evolution of global congestion configurations in a large-scale urban transportation network. Instead of looking into temporal variations of traffic flow states of individual links, we focus on temporal evolution of the complete spatial configuration of congestions over the network. In our work, we pursue to describe the typical temporal patterns of the global traffic states and achieve long-term prediction of the large-scale traffic evolution in a unified data-mining framework. To this end, we formulate this joint task using regularized Non-negative Tensor Factorization, which has been shown to be a useful analysis tool for spatio-temporal data sequences. Clustering and prediction are performed based on the compact tensor factorization results. The validity of the proposed spatio-temporal traffic data analysis method is shown on experiments using simulated realistic traffic data.

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Urban traffic flow prediction using a fuzzy-neural approach

Cited by 374 publications

References 1 publication

Link prediction in complex networks: A survey

Link prediction in complex networks: A survey

Cooperative download in urban vehicular networks

Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization

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