Short-Term Traffic Flow Intensity Prediction Based on CHS-LSTM

Zhao, Lei; Wang, Quanmin; Jin, Biao; Ye, Congmin

doi:10.1007/s13369-020-04862-3

Cited by 23 publications

(11 citation statements)

References 28 publications

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“…In order to make the comparison among the constructed models more convenient, the average absolute percentage error (MAPE), the average absolute error (MAE), and the root mean square error (RMSE) were introduced to evaluate the prediction error [47,48]. Besides, the accuracy ratio (A) and correlation coefficient (R) were also introduced to measure the correctness and reliability of the model.…”

Section: Comparison Of Multiple Modelsmentioning

confidence: 99%

Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model

et al. 2022

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Traffic flow is used as an essential indicator to measure the performance of the road network and a pivotal basis for road classification. However, the combined prediction model of traffic flow based on seasonal characteristics has been given little attention at present. Because the seasonal autoregressive integrated moving average model (SARIMA) has superior linear fitting characteristics, it is often used to process seasonal time series. In contrast, the non-autoregressive dynamic neural network (NAR) has a vital memory function and nonlinear interpretation capabilities. They are suitable for constructing combined forecasting models. The traffic flow time series of a highway in southwest China is taken as the research object in this paper. Combining the SARIMA (0,1,2) (0,1,2)12 model and the NAR model with 15 hidden layer neurons and fourth-order delay, two combined models are constructed: the linear and nonlinear component combination method is realized by the SARIMA-NAR combination model 1, and the MSE weight combination method is used by the SARIMA-NAR combination model 2. We calculated that the prediction accuracy of SARIMA-NAR combined model 1 is as high as 0.92, and the prediction accuracy of SARIMA-NAR combined model 2 is 0.90. In addition, the traffic flow forecast under the influence of the epidemic is also discussed. Through a comprehensive comparison of multiple indicators, the results show that the SARIMA-NAR combined model 1 has better road traffic flow fitting and prediction effects and is suitable for the greater volatility of traffic flow during the epidemic. This model improves the effectiveness and reliability of traffic flow forecasting, and the forecasting process is more convenient and efficient.

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Section: Comparison Of Multiple Modelsmentioning

confidence: 99%

Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model

et al. 2022

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“…Since the research on ETC transaction data mining is in its initial stage, there are not many researches. Currently, ETC transaction data mining mainly includes statistics and prediction of travel time [3,4], traffic flow [5], driving speed [6,7] etc. Zou et al had done a lot of researches based on ETC data, which contains ETC abnormal data detection [8], travel time prediction [3], expressway maximum speed limit recognition [9], expressway speed prediction [7].…”

Section: Etc Transaction Data Miningmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Positioning method of expressway ETC gantry by multi‐source traffic data

Guo

Zou

Luo

et al. 2022

IET Intelligent Trans Sys

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With the rapid development of expressway Electronic Toll Collection (ETC) technology in China, the expressway management system is becoming digital and intelligent, which provides a solid foundation for expressway vehicle infrastructure cooperation and autonomous driving. The gantry position is the key part of the ETC system. However, there are still some problems (e.g. gantry position missing or false), which can seriously affect the intelligent development of expressways. To address these two issues, an ETC gantry positioning method is proposed. First, the ETC transaction data and the GPS data on expressways are preprocessed to remove abnormal data and retrieve missed data. Then, combined with Dead Reckoning (DR) and Median Center, the potential position of the gantry is calculated from ETC transaction data and GPS data. Finally, the switching strategy based on Kalman Filter (KF) is used to capture the final gantry position. By comparing the results of the proposal with the collected gantry position, it is found that the positioning error of the gantry position calculated by this proposal is about 37 m. The positioning accuracy is 98.78% with the threshold of 100 m. The experimental results show that the proposal can effectively locate the expressway gantry.

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“…These methods not only enhance the understanding of environmental changes but also improve the adaptability and flexibility of ventilation systems. Chen Zhiya et al [18] proposed a traffic flow prediction model based on Long Short-Term Memory (LSTM) neural networks and delved into the impact of multidimensional factors such as time occupancy rate on traffic flow prediction, achieving more precise short-term traffic flow predictions.…”

Section: Introductionmentioning

confidence: 99%

Research on Feedforward Ventilation in High-altitude Tunnels Based on ARIMA Model

SUN,

ZHENG,

CAO

2024

Preprint

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This research aims to address the challenges of tunnel ventilation in high-altitude areas by proposing a novel feedforward ventilation strategy based on the Autoregressive Integrated Moving Average (ARIMA) model. By predicting ventilation needs and integrating real-time air quality monitoring, it optimizes air flow and quality within the tunnel, making it particularly suitable for the low oxygen, extreme climates, and complex geological conditions prevalent in high-altitude areas. The research methods include the construction and adjustment of the ARIMA model, optimizing the operation of the ventilation system through preprocessing of environmental parameter data and time series forecasting. Integrating a smart sensor network, the study achieves high accuracy in data input, thereby enhancing prediction accuracy. The results demonstrate that compared to traditional ventilation methods, this strategy significantly improves ventilation efficiency and reduces energy consumption. Overall, this research offers a new perspective on optimizing ventilation strategies for high-altitude internal combustion traction tunnels and showcases the immense potential of the ARIMA model in practical engineering applications. Through precise prediction of ventilation needs combined with real-time monitoring, the study significantly improves air quality within the tunnel, reduces energy consumption, and enhances the overall performance of the ventilation system.

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Short-Term Traffic Flow Intensity Prediction Based on CHS-LSTM

Cited by 23 publications

References 28 publications

Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model

Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model

Positioning method of expressway ETC gantry by multi‐source traffic data

Research on Feedforward Ventilation in High-altitude Tunnels Based on ARIMA Model

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