Short-term freeway traffic parameter prediction: Application of grey system theory models

Bezuglov, Anton; Comert, Gurcan

doi:10.1016/j.eswa.2016.06.032

Cited by 175 publications

(119 citation statements)

References 27 publications

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“…It is mainly used in a complex system with fitting and forecasting characteristics to reveal changes in the dominant factors and the future development trend of change [6]. It has been widely used to carry out quantitative forecasts and reasonable prediction accuracy was obtained [7] [8]. In this paper, a GM(1,1) model was developed to predict the demand for container trucks in a port.…”

Section: Overview Of Gm(11) Modelmentioning

confidence: 99%

Prediction of Container Haulage Demand using GM(1,1) Model

He¹,

Zheng²,

Huang³

2017

Proceedings of the 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017)

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Abstract. The reasonable estimation of container haulage demand is significant for the container transportation in port cities. Based on the number of container trucks and container throughput from year 2009 to 2015 in Ningbo Port, container haulage demand is predicted using GM(1,1) model, the result is compared to prediction using supply-demand balance method, it is shown that the GM(1,1) model has better prediction accuracy and fitting than supply-demand balance method.

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Section: Overview Of Gm(11) Modelmentioning

confidence: 99%

Prediction of Container Haulage Demand using GM(1,1) Model

He¹,

Zheng²,

Huang³

2017

Proceedings of the 2017 7th International Conference on Education, Management, Computer and Society (EMCS 2017)

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“…When the fitness corresponding to the global extreme value satisfies the error requirement, the iteration reaches convergence and the global extreme value is obtained as the optimal solution. Otherwise, the velocity and position are iteratively updated according to the method shown in (10) and (11). The movement of particles can be appropriately restricted by setting the velocity range [V min , V max ] and the position range [ min , max ] to ensure that the particles can efficiently search the global extreme value and avoid the local minimum problem.…”

Section: Two-phase Model Of Multistep Prediction Based On Waveletmentioning

confidence: 99%

“…One-step prediction [11] means predicting the reliability index in the next one step based on current and past reliability index series. Multistep prediction [12] means predicting the reliability index in the next several steps.…”

Section: Multistep Prediction Principlementioning

confidence: 99%

“…Bezuglov and Comert [11] have studied the possible applications and accuracy levels of three gray system theory models for short-term traffic speed and travel time predictions. Gray models consistently demonstrate low prediction errors over all time series, thereby successfully improving the accuracy by approximately 50% on average in root-mean-squared errors and mean absolute percent errors.…”

Section: Introductionmentioning

confidence: 99%

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Two-Phase Model of Multistep Forecasting of Traffic State Reliability

Yang

Liu

Jiang

et al. 2018

Discrete Dynamics in Nature and Society

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Multistep prediction of traffic state is a key technology for advanced transportation information system. The research results based on the principle of multistep prediction can provide more information about the traffic operating quality in advance. Considering that prediction error increases with the increasing numbers of multistep predictions, this research proposes the concept of dynamic predictability that is related to the characteristic of historical traffic flow data used. The traffic flow is characterized by randomness, regularity, and volatility according to the traffic flow theory. Therefore, three key indexes are firstly calculated to measure the characteristics of reliability series. Then a two-phase model is established based on wavelet neural network optimized by particle swarm optimization. The upper phase is a model to estimate the number of predictable steps, and the lower phase is the multistep prediction model of reliability. Compared with that of backpropagation neural network and support vector machine, results show that the convergence time of the wavelet neural network optimized by particle swarm optimization is the lowest, which only costs 256 and 291 seconds in both two-phase models under the same conditions. The average relative error of multistep prediction reached the lowest value, 8.91% and 12.01%, respectively, for weekday and weekend data used. Moreover, the prediction performance based on weekday is better than that of weekend. The research results lay a decision-making basis for managers in determining the key parts of road network to develop future improvement measures.

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“…Yang and Liu [29] used grey numbers and grey sets to represent uncertainties in travel time. Bezuglov and Comert [30] established the GM(1,1) model and grey Verhulst model with Fourier error corrections for short-term traffic speed and travel time predictions. René S. et al [31] developed an improved grey GM(1,4) model for German traffic safety predictions.…”

Section: Introductionmentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

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This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM (1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

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Short-term freeway traffic parameter prediction: Application of grey system theory models

Cited by 175 publications

References 27 publications

Prediction of Container Haulage Demand using GM(1,1) Model

Prediction of Container Haulage Demand using GM(1,1) Model

Two-Phase Model of Multistep Forecasting of Traffic State Reliability

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

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