Short-term Forecast Model of Vehicles Volume Based on ARIMA Seasonal Model and Holt-Winters

Wang, Zhi‐Hui; Lu, Chenyang; Pu, Bin; Li, Gui-Wen; Guo, Zhen-Jiang

doi:10.1051/itmconf/20171204028

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…In addition, three other methods are compared with this new method, to further evaluate the performance of this method. These methods are ARIMA, the Holt-Winters model (HW) and the long short-term memory model (LSTM), which are models widely used to predict traffic flow in many existing studies ( 10 , 17 , 32–34 ). The ARIMA model is used to predict the temporally changing features in this proposed method, but it could also be used to predict the original time series of entry-traffic flows.…”

Section: Methodsmentioning

confidence: 99%

Synchronized Entry-Traffic Flow Prediction for Regional Expressway System Based on Multidimensional Tensor

Gao

Wang

Yan

et al. 2021

Transportation Research Record

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Predicting entry-traffic flows synchronously could enable inferences about the changing trends and spatial structure of dynamic traffic flows in an expressway network. This research develops a synchronized entry-traffic flow prediction method for regional expressway systems. The new method first organizes numerous entry-traffic flows as a three-dimensional (time slots, spatial locations, and vehicle types) tensor, then applies tensor decomposition to extract their temporally changing features. After forecasting the temporally changing features, predicted values of entry-traffic flows can be calculated synchronously by tensor reconstruction. Data from hourly entry-traffic flows involving nine vehicle types and 201 spatial locations in a regional expressway system of China are used to discuss the performance of this new method. The results show that the new method could obtain prediction results with high overall accuracy. Comparative experiments indicate that the new method and existing methods (autoregressive integrated moving average, or ARIMA, and Holt-Winters) could generate prediction results with similar accuracy. However, the proposed method has the advantage of reducing the number of time series that need to be handled in the prediction of numerous entry-traffic flows for regional expressway systems. This method might be helpful for administrators to guide and manage vehicles so that they enter the expressway system effectively.

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Section: Methodsmentioning

confidence: 99%

Synchronized Entry-Traffic Flow Prediction for Regional Expressway System Based on Multidimensional Tensor

Gao

Wang

Yan

et al. 2021

Transportation Research Record

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“…Traditional traffic flow prediction models include parametric models such as Autoregressive Integrated Moving Average model (ARIMA) [7], Kalman filtering model [8] and their extensions. For example, EMD-ARIMA model [9], Seasonal ARIMA [10], an integration of Kalman filter and ARIMA [11] have been reported for traffic flow prediction in literature. These time series prediction methods have been developed to assist Transportation Cyber-Physical Systems (TCPS) to analyze traffic flow and predict the traffic trend.…”

Section: A Traditional Machine Learningmentioning

confidence: 99%

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Zhou

Dai

Wang

2021

IEEE Trans. Ind. Inf.

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Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively-available traffic data collected from various sensors in Transportation Cyber-Physical Systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, we propose a wide-attention and deep-composite (WADC) model consisting of a wide-attention module and a deep-composite module in this paper. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with selfattention mechanism. The deep-composite module can generalize local key features via Convolutional Neural Network component and Long Short-Term Memory Network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches.

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“…In the literature, we can find numerous examples [28] that the Autoregressive Integrated Moving Average often outperforms the Holt-Winters algorithm. Therefore, forecasting using the ARIMA model has also been attempted in this work.…”

Section: Forecasting Metrics With Arimamentioning

confidence: 99%

Software Development Metrics Prediction Using Time Series Methods

Choraś

Kozik

Pawlicki

et al. 2019

Lecture Notes in Computer Science

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The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it's declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software's release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.

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Short-term Forecast Model of Vehicles Volume Based on ARIMA Seasonal Model and Holt-Winters

Cited by 10 publications

References 10 publications

Synchronized Entry-Traffic Flow Prediction for Regional Expressway System Based on Multidimensional Tensor

Synchronized Entry-Traffic Flow Prediction for Regional Expressway System Based on Multidimensional Tensor

Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems

Software Development Metrics Prediction Using Time Series Methods

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