Performance of design options of automated ARIMA model construction for dynamic vehicle GPS location prediction

Alzyout, Mohammad S.; Alsmirat, Mohammad A.

doi:10.1016/j.simpat.2020.102148

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…The flowchart for our proposed algorithm is shown in figure 3. A previous work [14] shows that the ARIMA model can predict the vehicle's location for several future steps with acceptable accuracy in GNSS standalone positioning. In this work, we seek to further improve the accuracy of vehicle position prediction by introducing the hybrid model ARIMA-MLP.…”

Section: Algorithmmentioning

confidence: 99%

“…And the new incoming GNSS is detected as an outlier if the distance from the predicted values exceeds a threshold value. Alzyout and Alsmirat [14] proposed an anomaly detection method that is based on the autoregressive integrated moving average (ARIMA). ARIMA can well model linear patterns in position information, however, it is not applicable to nonlinear patterns.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A new GNSS outlier mitigation method for GNSS/INS integrated system

Guo

Zhou

Zhao

et al. 2023

Meas. Sci. Technol.

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High-precision positioning with Global Navigation Satellite Systems (GNSS) remains a significant challenge in urban environments, due to the outliers caused by the insufficient number of accessible satellites and environmental interference. A GNSS outlier mitigation algorithm with effective Fault Detection and Exclusion (FDE) is required for high-precision positioning. The traditional methods are designed to deal with zero-mean noise in GNSS, which leads to instabilities under biased measurements. Considering that GNSS data are typical time series data, a dynamic FDE scheme is constructed by combining a prediction-model-based method and a dissimilarity-based method. First, a hybrid prediction model which combines Autoregressive Integrated Moving Average (ARIMA) model and Multilayer Perceptron (MLP) model is proposed to provide pseudo-GNSS series by predicting the vehicle’s location for several future steps. Then, a dissimilarity-based method of Dynamic Time Warping (DTW) measure is utilized to analyze the pairwise dis-similarity between the pseudo-GNSS series and the received GNSS series. The performance of the different models in forecasting is evaluated, and the results show that the positioning accuracy is significantly improved by applying the ARIMA-MLP. The effectiveness of the proposed FDE method is verified through simulation experiments and real experiments based on a typical urban canyon public dataset collected in Tokyo.

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Section: Algorithmmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A new GNSS outlier mitigation method for GNSS/INS integrated system

Guo

Zhou

Zhao

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This approach allowed them to calculate the precise coordinates of the target vehicle through local fingerprint localization. Mohammad S. Alzyout et al [35] introduced a short-term vehicle location prediction framework that enhances prediction accuracy and framework execution time by dynamically adjusting parameters and employing both multi-selective and single-selective ARIMA models. Baby Anitha et al [36] addressed the limitations of current methods in vehicle location prediction, which often lack analysis of both current and future vehicle positions and are affected by errors in GPS location data.…”

Section: Related Researchmentioning

confidence: 99%

A Highway In-Transit Vehicle Position Estimation Method Considering Road Characteristics and Short-Term Driving Style

Luo,

Zou,

Guo

et al. 2024

IEEE Access

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Existing vehicle position estimation methods are mostly based on Global Positioning System (GPS) or a fusion of GPS and machine learning methods to realize vehicle position estimation. While highway tunnels are many, GPS signals are easy to be interfered, and the vehicle loading rate of GPS devices is limited, this kind of method can not be realized in a wide range of applications. In this context, taking into account the ETC equipment that has been deployed and applied in large scale in China, the vehicle equipment loading rate is over 90%, but the ETC gantry interval is large, and it is not possible to effectively perceive the vehicle driving status inside the segment. Therefore, this paper is based on the ETC transaction data to build the basic driving characteristics and short-term driving style of the vehicle history segment, using GPS positioning data to build the internal characteristics of the segment, including the characteristics of the road structure within the segment, the pattern of change of the vehicle position, so as to put forward the highway in-transit vehicle position estimation method that considers the road characteristics and short-term driving style. Firstly, the SC-Kmeans-Bilstm vehicle segment speed prediction model based on PCA optimization is constructed by fusing vehicle short-term driving styles; secondly, the road model within the segment is constructed by using moving average and wavelet smoothing methods; lastly, the vehicle position data is temporally stabilized using linear interpolation and first-order inverse difference, and vehicle position estimation within the highway segment is realized by using DLCNN-LSTM-ATTENTION fusion model based on L1 regularization by combining vehicle segment speeds, road characteristics, and vehicle base driving characteristics. Among them, the short-term driving style helps us to obtain the vehicle segment speed more accurately, and the addition of the road model makes this method better explain the variability of the data. The experimental results show that the present method can achieve on-travel vehicle position estimation within 2km with an error of less than 50m in a full-sample highway environment, and can provide over-the-horizon sensing for intelligent vehicles.

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“…ARIMA model can effectively analyze the correlation of periodic non-stationary data sequences. ARIMA model has good forecasting ability for linear sequences (Alzyout & Alsmirat 2020). Therefore, ARIMA is suitable for the forecasting of the IMFs with larger Hurst exponents.…”

Section: Arima Modelmentioning

confidence: 99%

Runoff forecasting model based on CEEMD and combination model: a case study in the Manasi River, China

Lian

2022

Water Supply

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Accurate forecasting of runoff is necessary for water resources management. However, the runoff time series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved forecasting accuracy, a novel combination model based on complementary ensemble empirical mode decomposition (CEEMD) for runoff forecasting is proposed and applied in this paper. Firstly, the original runoff series is decomposed into a limited number of intrinsic mode functions (IMFs) and one residual based on CEEMD, which makes the runoff time series stationary. Then, approximate entropy is introduced to judge the complexity of each IMF and residual. According to the calculation results of approximate entropy, the high complexity components are predicted by Gaussian process regression (GPR), the medium complexity components are predicted by support vector machine (SVM), and the low complexity components are predicted by autoregressive integrated moving average model (ARIMA). The advantages of each forecasting model are used to forecasting the appropriate components. In order to solve the problem that the forecasting performance of GPR and SVM is affected by their parameters, an improved fireworks algorithm (IFWA) is proposed to optimize the parameters of two models. Finally, the final forecasting result is obtained by adding the forecasted values of each component. The runoff data collected from the Manasi River, China is chosen as the research object. Compared with some state-of-the-art forecasting models, the comparison result curve between the forecasted value and actual value of runoff, the forecasting error, the histogram of the forecasting error distribution, the performance indicators and related statistical indicators show that the developed forecasting model has higher prediction accuracy and is able to reflect the change laws of runoff correctly.

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Performance of design options of automated ARIMA model construction for dynamic vehicle GPS location prediction

Cited by 9 publications

References 18 publications

A new GNSS outlier mitigation method for GNSS/INS integrated system

A new GNSS outlier mitigation method for GNSS/INS integrated system

A Highway In-Transit Vehicle Position Estimation Method Considering Road Characteristics and Short-Term Driving Style

Runoff forecasting model based on CEEMD and combination model: a case study in the Manasi River, China

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