Comparison of Four Modeling Techniques for Short-Term AADT Forecasting in Hong Kong

Tang, Yf F.; Lam, William H. K.; Ng, Pan L. Plp

doi:10.1061/(asce)0733-947x(2003)129:3(271)

Cited by 58 publications

(28 citation statements)

References 8 publications

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“…Therefore, the 1-nearest neighbor NPR model was adopted for forecasting in this study. For details of the NPR method refer to Tang et al (2003).…”

Section: Neural Network (Nn)mentioning

confidence: 99%

“…In an earlier study (Tang et al 2003), four (two parametric and two non-parametric) models for short-term prediction of daily traffic flows by day of the week and by month of the whole current year were investigated to estimate the Annual Average Daily Traffic (AADT) in Hong Kong. These four models were developed on the basis of the Auto-Regressive Integrated Moving-Average (ARIMA), Neural Network (NN), NonParametric Regression (NPR) and Gaussian Maximum Likelihood (GML) methods.…”

Section: Introductionmentioning

confidence: 99%

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Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

et al. 2006

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The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region. Copyright Springer 2006Annual Traffic Census, Auto-Regressive Integrated Moving Average, Gaussian Maximum Likelihood, Neural Network, Non-Parametric Regression,

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“…Therefore, the 1-nearest neighbor NPR model was adopted for forecasting in this study. For details of the NPR method refer to Tang et al (2003).…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

et al. 2006

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“…Two measures of model performance, namely, the mean absolute error (MAE) and the mean absolute percentage error (MAPE) have been adopted to evaluate the prediction results. [11][12][13][14] Other evaluation indicators should be considered, including time and effort required for model development, transferability of results, skills and expertise required, adaptability to changing temporal or spatial behavior, to name a few. 10,[15][16][17] In this study, the performance of three representative modeling approaches is compared with real-life data in Beijing.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis for traffic flow forecasting models with real-life data in Beijing

Zhang

Feng

et al. 2015

Advances in Mechanical Engineering

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Rational traffic flow forecasting is essential to the development of advanced intelligent transportation systems. Most existing research focuses on methodologies to improve prediction accuracy. However, applications of different forecast models have not been adequately studied yet. This research compares the performance of three representative prediction models with real-life data in Beijing. They are autoregressive integrated moving average, neutral network, and nonparametric regression. The results suggest that nonparametric regression significantly outperforms the other models. With Wilcoxon signed-rank test, the root mean square errors and the error distribution reveal that the nonparametric regression model experiences superior accuracy. In addition, the nonparametric regression model exhibits the best spatial-transferred application effect.

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“…However, after its publication, the GML (Gaussian Maximum Likekihood) approach, proposed in [5], has been compared with other models, such as non-parametric regression, ARIMA, neural networks, online SVR, and smoothing techniques, concluding that GML-based predictor presented the best forecasting performance, under typical traffic conditions [15], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Kalman filtering for multi-step ahead traffic flow prediction

Ojeda

Kibangou

Wit

2013

2013 American Control Conference

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Abstract-Given the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring.

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Comparison of Four Modeling Techniques for Short-Term AADT Forecasting in Hong Kong

Cited by 58 publications

References 8 publications

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

Comparative analysis for traffic flow forecasting models with real-life data in Beijing

Adaptive Kalman filtering for multi-step ahead traffic flow prediction

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