Time scales, fluctuations and constant flow periods in uni-directional traffic

Breiman, Leo; Lawrence, Robert L.

doi:10.1016/0041-1647(73)90007-5

Cited by 26 publications

(17 citation statements)

References 3 publications

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“…A common finding among the researches on the applications of spectral analysis in traffic engineering (such as Lam and Rothery (1970), Breiman and Lawrence (1973), Nicholson and Swann (1974), and Lu (1992)) was that traffic flow parameters, with the exception of periods of light traffic, formed nonlinear and nonstationary time series. This is clearly supported by many traffic flow theories (FHWA, 2002).…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

Near‐Term Travel Speed Prediction Utilizing Hilbert–Huang Transform

Hamad

Shourijeh

Lee

et al. 2009

Computer aided Civil Eng

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Accurate short-term prediction of travel speed as a proxy for time is central to many Intelligent Transportation Systems, especially for Advanced Traveler Information Systems and Advanced Traffic Management Systems. In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert-Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real-life loop detector data obtained from I-66 in Fairfax, Virginia. ous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters.

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Section: Hilbert-huang Transformmentioning

confidence: 99%

Near‐Term Travel Speed Prediction Utilizing Hilbert–Huang Transform

Hamad

Shourijeh

Lee

et al. 2009

Computer aided Civil Eng

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“…A number of techniques have been proposed to overcome the problem of non-stationarity in traffic data, including trend correction procedures (Breiman and Lawrence, 1973), the construction of time scales (Polhemns, 1980), and the application of specialized high-pass filters that remove selective frequencies from the data (Polhemns, 1976). Another technique which can sometimes be used is the removal of trend by means of appropriate transformations.…”

Section: Arima Modelsmentioning

confidence: 99%

Operational filtering of traffic data

Ahmed

1989

Journal of Forecasting

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An operational filter of traffic state variables is presented for use in designing computer-aided traffic surveillance and control systems. A total of 166 data sets from three traffic surveillance systems were used in the filter development. All the data sets were best represented by an ARlMA (0, 1,3) filter. This filter has the following advantages: (1) it yields minimum mean-square-error forecasts if stationarity of the observations can be obtained; (2) it provides much better results than the existing ad hoc filters; (3) it is computationally tractable; and (4) it requires modest computer storage of data. Suggestions and implications for the use of this filter are given.

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“…Using results of this type [36][37][38], it is shown in [23] that under spatially homogeneous conditions density is proportional to occupancy.…”

Section: The Estimation Of Aggregate Variables From Presence Detmentioning

confidence: 99%

Dynamic model-based techniques for the detection of incidents on freeways

Willsky

Chow

Gershwin³

et al. 1980

IEEE Trans. Automat. Contr.

160

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In this paper we discuss an approach to the detection of incidents on freeways. Our techniques are based on the use of a macroscopic dynamic model describing the evolution of spatialaverage traffic variables (velocities, flows, and densities) over sections of the freeway. With such a model as a starting point we develop two incident detection algorithms based on the multiple model and generalized likelihood ratio techniques. We also describe a new and very simple system for processing raw data from presence-type vehicle detectors to produce estimates of the aggregate variables, which are then in turn used as the input variables to the incident detection algorithms. Simulation results using a microscopic simulation of a two-lane freeway indicate that:(1) our algorithms are robust to the differences between the dynamics of actual traffic and the aggregated dynamics used to design the detection systems; and (2) our methods appear to work as well as existing algorithms in heavy traffic conditions and work better in moderate to light traffic. Areas for future work are outlined at the end of the paper.

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Time scales, fluctuations and constant flow periods in uni-directional traffic

Cited by 26 publications

References 3 publications

Near‐Term Travel Speed Prediction Utilizing Hilbert–Huang Transform

Near‐Term Travel Speed Prediction Utilizing Hilbert–Huang Transform

Operational filtering of traffic data

Dynamic model-based techniques for the detection of incidents on freeways

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