Improved grey system models for predicting traffic parameters

Comert, Gurcan; Begashaw, Negash; Huynh, Nathan

doi:10.1016/j.eswa.2021.114972

Cited by 53 publications

(19 citation statements)

References 18 publications

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“…The original rainfall data in Zhengzhou are X (0) . The parameters a = 0.0033 and b = 675.4202 could be achieved based on grey system theory [ 38 ]. Then, the prediction formula of rainfall could be calculated as follows.…”

Section: Resultsmentioning

confidence: 99%

Causal Analysis and Prevention Measures for Extreme Heavy Rainstorms in Zhengzhou to Protect Human Health

Liu

2022

Behavioral Sciences

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This study focused on the extreme heavy rainstorm that occurred in Zhengzhou in July 2021; approximately 380 people were killed or missing as a result of this storm. To investigate the evolution behaviors of this rainstorm and take corresponding prevention measures, several methods and models were adopted, including cloud modeling, preliminary hazard analysis (PHA), fault tree analysis (FTA), bow-tie modeling, and chaos theory. The main reasons for this rainstorm can be divided into the following three aspects: force majeure, such as terrain and extreme weather conditions, issues with city construction, and insufficient emergency rescue. The secondary disasters caused by this rainstorm mainly include urban water logging, river flooding, and mountain torrents and landslides. The main causes of the subway line-5 accident that occurred can be described as follows: the location of the stabling yard was low, the relevant rules and regulations of the subway were not ideal, insufficient attention was given to the early warning information, and the emergency response mechanism was not ideal. Rainstorms result from the cross-coupling of faults in humans, objects, the environment, and management subsystems, and the evolution process shows an obvious butterfly effect. To prevent disasters caused by rainstorms, the following suggestions should be adopted: vigorously improve the risk awareness and emergency response capabilities of leading cadres, improve the overall level of urban disaster prevention and mitigation, reinforce the existing reservoirs in the city, strengthen the construction of sponge cities, and improve the capacity of urban disaster emergency rescue.

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Section: Resultsmentioning

confidence: 99%

Causal Analysis and Prevention Measures for Extreme Heavy Rainstorms in Zhengzhou to Protect Human Health

Liu

2022

Behavioral Sciences

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“…In our work in this paper, WinBUGS examples in [15], MCMC in [14], and examples from [16] are used. Solutions and GM codes are our original codes from [3,5] which are revised for Bayesian regression-based parameter estimations for every new data observed.…”

Section: Computational Resultsmentioning

confidence: 99%

“…In GM, we first estimate parameters using the method of least squares then we plug in the estimated parameters in the solution of the first order linear differential equation used as the whitenization of the grey model to make predictions. For real time usage, a window size of 4 previous data points is found to be effective for updating the parameters [2][3][4][5][6]. WinBUGS models use R 2 but we do not use R 2 in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…In a previous work, we used GMs for short-term speed and travel time predictions in transportation [3]. In [5], improved grey system models were used to predict short-term traffic parameters (e.g., speed, travel time, flow, and occupancy). [6] showed the effectiveness of GMs in short-term traffic signal queue lengths which are highly nonlinear (cyclic).…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Parameter Estimations for Grey System Models in Online Traffic Speed Predictions

Comert¹,

Begashaw²,

Medhin³

2021

Preprint

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This paper presents Bayesian parameter estimation for first order Grey system models' parameters (or sometimes referred to as hyperparameters). There are different forms of firstorder Grey System Models. These include GM(1, 1), GM(1, 1| cos(ωt), GM(1, 1| sin(ωt), and GM(1, 1| cos(ωt), sin(ωt). The whitenization equation of these models is a first-order linear differential equation of the formwhere a is a parameter and= bx 2 in Grey Verhulst model (GVM), and where b, b 1 , b 2 , b 3 are parameters. The resultsfrom Bayesian estimations are compared to the least square estimated models with fixed ω. We found that using rolling Bayesian estimations for GM parameters can allow us to estimate the parameters in all possible forms. Based on the data used for the comparison, the numerical results showed that models with Bayesian parameter estimations are up to 45% more accurate in mean squared errors.

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“…Here, several novel online Grey systems are proposed [12]. To evaluate the performance of the proposed models, they are compared against a set of benchmark models: GM(1,1) model, Grey Verhulst models with and without Fourier error corrections, linear time series model, and nonlinear time series model.…”

Section: Introductionmentioning

confidence: 99%

Forcasting Natural Gas Consumption in Nigeria using the Modified Grey Model (MGM(1,1,⊗b))

Samuel

Akeyede

2022

Afr. Sci. Rep.

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Accurate prediction of the natural gas consumption in Nigeria is crucial to Gas management. This study utilizes the improved Grey model (MGM(1,1,⊗b)), which is an improvement of the modified Grey model (MGM(1,1)), to forecast the natural gas consumption of Nigeria for the year 2021 to 2025. A secondary data retrieved from the NNPC 2019 annual statistics bulletin was used to build a model for this prediction. Noting that MGM(1,1) model uses the Grey action quantity as a unique real number which do not reflect the uncertainty nature of Grey systems. A model (MGM(1,1,⊗b)) was developed such that it extends the MGM(1,1) model to retain the uncertainty nature of Grey systems. The new modified Grey model (MGM(1,1,⊗b)) was used to make prediction of the natural gas consumption of Nigeria and the results shows that the (MGM(1,1,⊗b)) model gives a prediction interval which the actual value is bracketed. This implies that natural gas consumption of Nigeria for 2021 to 2025 lies within the (MGM(1,1,⊗b)) model prediction values for the same year.

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Improved grey system models for predicting traffic parameters

Cited by 53 publications

References 18 publications

Causal Analysis and Prevention Measures for Extreme Heavy Rainstorms in Zhengzhou to Protect Human Health

Causal Analysis and Prevention Measures for Extreme Heavy Rainstorms in Zhengzhou to Protect Human Health

Bayesian Parameter Estimations for Grey System Models in Online Traffic Speed Predictions

Forcasting Natural Gas Consumption in Nigeria using the Modified Grey Model (MGM(1,1,⊗b))

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