ARIMA Model and Few-Shot Learning for Vehicle Speed Time Series Analysis and Prediction

Wang, Yingzi; Yu, Ce; Hou, Jue; Chu, Sisi; Zhu, Yue

doi:10.1155/2022/2526821

Cited by 8 publications

(5 citation statements)

References 11 publications

(8 reference statements)

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“…SPFs are widely used by state and local authorities to predict crashes. In [44], a method of speed prediction of the moving vehicle at high speed with autoregressive integrated moving average (ARIMA) models is proposed.…”

Section: Time Seriesmentioning

confidence: 99%

Identification of the Mechanical Failure Factors with Potential Influencing Road Accidents in Ecuador

Montero-Salgado

Muñoz-Sanz

Arenas-Ramírez

et al. 2022

IJERPH

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Road traffic accidents result in injury or even death of passengers. One potential cause of these accidents is mechanical failures due to a lack of vehicle maintenance. In the quest to identify these mechanical failures, this paper aims to set up the procedure to identify the mechanical failures that contribute to traffic accidents in cities located in developing countries, including the city of Cuenca-Ecuador. For present research, a database provided by the entity responsible for the Vehicle Technical Inspection, the Empresa Pública Municipal de Movilidad, Tránsito y Transporte and for the ones responsible of managing traffic accident data, Oficina de Investigación de Accidentes de Tránsito and Sección de Investigación de Accidentes de Tránsito was used. The vehicle subcategories M1 and M3 (bus type) and N1, so named according to Ecuadorian technical standards, were considered the most relevant regarding accident rates. The database was analysed with descriptive statistics, a Pareto chart and time series with the quadratic trend. From this analysis, the most significant failures found in the VTI in all three subcategories were the alignment of the driver headlight, both horizontal and vertical, braking imbalance on the 2nd axle, insufficient tire tread and parking brake effectiveness. All these failures showed a decreasing trend over time and in the forecast at a maximum of two to three years. The most relevant causes of road accidents recorded during the period 2009–2018 related to mechanical failures were the braking system (65.5%) and the steering system (17.2%) for subcategory M1.

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Section: Time Seriesmentioning

confidence: 99%

Identification of the Mechanical Failure Factors with Potential Influencing Road Accidents in Ecuador

Montero-Salgado

Muñoz-Sanz

Arenas-Ramírez

et al. 2022

IJERPH

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“…e current two types of models (time series analysis model [21,22] and multiple cross-sectional regression model [23,24]) have their advantages and disadvantages. e time series analysis model can have a more stable output because it considers the dependencies that pass over time.…”

Section: E Basic Ideamentioning

confidence: 99%

Design of Accounting Earnings Forecasting Model Based on Artificial Intelligence

Liu¹,

Shi²,

Pham

2022

Scientific Programming

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The increasing complexity of the international situation intensifies the changes of the economic environment. People’s demand for information represented by accounting earnings, such as judging the profitability and risk coefficient of the company, is becoming more and more urgent. This study puts forward the theory of predicting accounting earnings through accounting earnings factors in a nonlinear way and designs an accounting earnings forecasting model based on artificial intelligence. Integrating LSTM, seq2seq, and reinforcement learning and combining with self-attention like mechanism, a complex multifactor time series forecasting model is established, and reinforcement learning is used to stabilize the model to prevent overfitting, which puts forward a new solution to the multifactor time series forecasting problem of complex relationship. The experimental results and comparative analysis show the effectiveness of the enhanced recurrent neural network accounting earnings prediction model designed in this study.

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“…This integration owes much to the convergence of various technologies, including pattern recognition, video image processing, and network communication [1,2]. Vehicle speed is a crucial parameter that directly reflects the state of traffic [3,4]. Meanwhile, in highly complex traffic monitoring scenarios and under special weather conditions, intelligent transportation monitoring systems face numerous significant challenges.…”

Section: Introductionmentioning

confidence: 99%

Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Luo,

Bi,

Yang

et al. 2024

Front. Phys.

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Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios.

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ARIMA Model and Few-Shot Learning for Vehicle Speed Time Series Analysis and Prediction

Cited by 8 publications

References 11 publications

Identification of the Mechanical Failure Factors with Potential Influencing Road Accidents in Ecuador

Identification of the Mechanical Failure Factors with Potential Influencing Road Accidents in Ecuador

Design of Accounting Earnings Forecasting Model Based on Artificial Intelligence

Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

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