Short-Term Prediction of Traffic Volume in Urban Arterials

Hamed, Mohammad M.; Al‐Masaeid, Hashem R.; Said, Zahi M. Bani

doi:10.1061/(asce)0733-947x(1995)121:3(249)

Cited by 450 publications

(166 citation statements)

References 6 publications

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“…Many approaches based on nonparametric models to tackle this problem have been proposed, such as multilayer perceptron with a learning rule based on a Kalman filter [49], wavelet-based neural network [18], fuzzy-neural model [52], ARIMA models [23], graphical-lasso neural network [20], multi-task neural network [19], multi-task ensemble neural network [45], k-nearest neighbour nonparametric regression [53].…”

Section: Previous Work In the Fields Of Data Streammentioning

confidence: 99%

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

2017

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Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for shortterm traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to nonstationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.

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Section: Previous Work In the Fields Of Data Streammentioning

confidence: 99%

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

2017

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“…Thereinto, the autoregressive integrated moving average (ARIMA) (Ahmed & Cook, 1979) family of models such as simple ARIMA (Levin & Tsao, 1980;Nihan & Holmesland, 1980;Hamed et al, 1995;Smith, 1995;Williams, 1999), ATHENA , subset ARIMA (Lee & Fambro, 1999), SARIMA family (Smith et al, 2002;Williams et al, 1998Williams et al, , 2003Ghosh et al, 2005), are classical milestones in forecasting area. Such time series methods belong to time domain approaches, and frequency domain approaches like spectral analysis, "which are regressions on periodic sines and cosines, show their important insights into traffic data which may not apparent in an analysis in the time domain only" (Stathopoulos & Karlaftis, 2001a, b).…”

Section: Parametric Traffic Forecasting Approachesmentioning

confidence: 99%

How to Provide Accurate and Robust Traffic Forecasts Practically?

Zhang¹

2012

Intelligent Transportation Systems

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“…Davis et al (1991) applied a single ARIMA model to forecast the bottleneck formation on a freeway. Later, Hamed et al (1995) applied an ARIMA model to forecast urban traffic volumes.…”

Section: Travel Time Prediction Techniques and Travel Time Estimationmentioning

confidence: 99%

The Use of Telematics in the Daily Distribution of Perishable Goods: The Case of NIKAS SA

Zeimpekis

2011

Intelligent Agrifood Chains and Networks

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Short-Term Prediction of Traffic Volume in Urban Arterials

Cited by 450 publications

References 6 publications

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

How to Provide Accurate and Robust Traffic Forecasts Practically?

The Use of Telematics in the Daily Distribution of Perishable Goods: The Case of NIKAS SA

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