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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1711-1728页

作者： Jing-Doo Wang Chayadi Oktomy Noto Susanto Departement of Computer Science and Information Engineering Asia UniversityTaichungTaiwanChina Departement of Information Technology Universitas Muhammadiyah YogyakartaYogyakartaIndonesia

A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic *** advancements in deep neural networks have enabled the development of models to represent traffic flow ***,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal *** paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention *** studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast *** addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast *** experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

关键词： traffic flow prediction sptiotemporal data heterogeneous data Conv-BiLSTM data-centric intra-data

Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic Science and Technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China College of Computer Science&Software Engineering Shenzhen UniversityShenzhen518060China School of Computer Science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network Multi-head attention mechanism Spatio-temporal dependency traffic flow prediction

traffic flow prediction based on BILSTM model and data denoising scheme

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Chinese Physics B 2022年第4期31卷 191-200页

作者： Zhong-Yu Li Hong-Xia Ge Rong-Jun Cheng Faculty of Maritime and Transportation Ningbo UniversityNingbo 315211China Jiangsu Provincial Collaborative Innovation Center for Modern Urban traffic Technologies Nanjing 210096China National traffic Management Engineering and Technology Research Center Ningbo University Subcenter Ningbo 315211China

Accurate prediction of road traffic flow is a significant part in the intelligent transportation *** prediction can alleviate traffic congestion,and reduce environmental *** the management department,it can make effective use of road *** individuals,it can help people plan their own travel paths,avoid congestion,and save *** to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain *** the influence of noise on traffic flow prediction is a piece of very important ***,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow *** the same time,three denoising algorithms are compared to find the best combination *** this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow *** addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic *** data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic *** results for mainline show that data denoising can improve prediction ***,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for *** is indicated that this model is suitable for different road sections and long-term prediction.

关键词： traffic flow prediction bidirectional long short-term memory network data denoising

traffic flow prediction with Heterogenous Data Using a Hybrid CNN-LSTM Model

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Computers, Materials & Continua 2023年第9期76卷 3097-3112页

作者： Jing-Doo Wang Chayadi Oktomy Noto Susanto Departement of Computer Science and Information Engineering Asia UniversityTaichungTaiwan Departement of Information Technology Universitas Muhammadiyah YogyakartaYogyakartaIndonesia

Predicting traffic flow is a crucial component of an intelligent transportation *** monitoring and predicting traffic flow remains a challenging ***,existingmethods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes,resulting in the loss of essential information and lower forecast *** the other hand,the availability of spatiotemporal data is *** research offers alternative spatiotemporal data with three specific features as input,vehicle type(5 types),holidays(3 types),and weather(10 conditions).In this study,the proposed model combines the advantages of the capability of convolutional(CNN)layers to extract valuable information and learn the internal representation of time-series data that can be interpreted as an image,as well as the efficiency of long short-term memory(LSTM)layers for identifying short-term and long-term *** approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning *** research findings show that adding spatiotemporal feature data increases the forecast’s performance;weather by 25.85%,vehicle type by 23.70%,and holiday by 14.02%.

关键词： Heterogeneous data traffic flow prediction deep learning CNN LSTM

Optimal Logistics Activities Based Deep Learning Enabled traffic flow prediction Model

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Computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic *** order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

Kalman Filter-Based CNN-BiLSTM-ATT Model for traffic flow prediction

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Computers, Materials & Continua 2023年第7期76卷 1047-1063页

作者： Hong Zhang Gang Yang Hailiang Yu Zan Zheng Institute of Transportation Inner Mongolia UniversityHohhot010020China Inner Mongolia Engineering Research Center for Urban Transportation Data Science and Applications Hohhot010020China

To accurately predict traffic flow on the highways,this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism(CNN-BiLSTM-Attention)traffic flow prediction model based on Kalman-filtered data ***,the original fluctuating data is processed by Kalman filtering,which can reduce the instability of short-term traffic flow prediction due to unexpected *** the local spatial features of the traffic data during different periods are extracted,dimensionality is reduced through a one-dimensional CNN,and the BiLSTM network is used to analyze the time series ***,the Attention Mechanism assigns feature weights and performs Soft-max *** experimental results show that the data processed by Kalman filter is more accurate in predicting the results on the CNN-BiLSTM-Attention *** with the CNN-BiLSTM model,the Root Mean Square Error(RMSE)of the Kal-CNN-BiLSTM-Attention model is reduced by 17.58 and Mean Absolute Error(MAE)by 12.38,and the accuracy of the improved model is almost free from non-working *** further verify the model’s applicability,the experiments were re-run using two other sets of fluctuating data,and the experimental results again demonstrated the stability of the ***,the Kal-CNN-BiLSTM-Attention traffic flow prediction model proposed in this paper is more applicable to a broader range of data and has higher accuracy.

关键词： Highway traffic flow prediction Kalman filter CNN-BiLSTM-Attention

MEEMD-DBA-based short term traffic flow prediction

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High Technology Letters 2023年第1期29卷 41-49页

作者：张玺君 HAO Jun NIE Shengyuan CUI Yong College of Computer and Communication Lanzhou University of TechnologyLanzhou 730050P.R.China

Aiming at the problem that ensemble empirical mode decomposition(EEMD)method can not completely neutralize the added noise in the decomposition process,which leads to poor reconstruction of decomposition results and low accuracy of traffic flow prediction,a traffic flow prediction model based on modified ensemble empirical mode decomposition(MEEMD),double-layer bidirectional long-short term memory(DBiLSTM)and attention mechanism is ***,the intrinsic mode functions(IMFs)and residual components(Res)are obtained by using MEEMD algorithm to decompose the original traffic data and separate the noise in the ***,the IMFs and Res are put into the DBiLSTM network for ***,the attention mechanism is used to enhance the extraction of data features,then the obtained results are reconstructed and *** experimental results show that in different scenarios,the MEEMD-DBiLSTM-attention(MEEMD-DBA)model can reduce the data reconstruction error effectively and improve the accuracy of the short-term traffic flow prediction.

关键词： modified ensemble empirical mode decomposition(MEEMD) double bidirectional-directional gated recurrent unit(DBiGRU) attention mechanism traffic flow prediction

traffic flow prediction of urban road network based on LSTM-RF model

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Journal of Measurement Science and Instrumentation 2020年第2期11卷 135-142页

作者： ZHAO Shu-xu ZHANG Bao-hua School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of *** traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction *** this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination *** the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road ***,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction *** traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and *** results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.

关键词： traffic flow prediction long short time memory and random forest(LSTM-RF)model random forest combination model spatial-temporal correlation

Terminal traffic flow prediction Method Under Convective Weather Using Deep Learning Approaches

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Transactions of Nanjing University of Aeronautics and Astronautics 2021年第4期38卷 634-645页

作者： PENG Ying WANG Hong MAO Limin WANG Peng College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China Nanjing LES Information Technology Co. LtdNanjing 210014P.R.China

In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather,a multi-input deep learning(MICL)model is *** the basis of previous studies,this paper expands the set of weather characteristics affecting the traffic flow in the terminal area,including weather forecast data and Meteorological Report of Aerodrome Conditions(METAR)*** terminal airspace is divided into smaller areas based on function and the weather severity index(WSI)characteristics extracted from weather forecast data are established to better quantify the impact of *** model preserves the advantages of the convolution neural network(CNN)and the long short-term memory(LSTM)model,and adopts two channels to input WSI and METAR information,respectively,which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal ***-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective *** results show that the MICL model has excellent performance in mean squared error(MSE),root MSE(RMSE),mean absolute error(MAE)and other performance indicators compared with the existing machine learning models or deep learning models,such as Knearest neighbor(KNN),support vector regression(SVR),CNN and *** the forecast period ranging from 30 min to 6 h,the MICL model has the best prediction accuracy and stability.

关键词： air traffic management traffic flow prediction convective weather deep learning

Short-Term traffic flow prediction Based on LSTM-XGBoost Combination Model

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 95-109页

作者： Xijun Zhang Qirui Zhang College of Computer and Communication Lanzhou University of TechnologyLanzhou730050China

According to the time series characteristics of the trajectory history data,we predicted and analyzed the traffic *** paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity,stationary and abnormality of time *** can improve the traffic flow prediction effect,achieve efficient traffic guidance and traffic *** model combined the characteristics of LSTM(Long Short-Term Memory)network and XGBoost(Extreme Gradient Boosting)***,we used the LSTM model that increases dropout layer to train the data set after ***,we replaced the full connection layer with the XGBoost ***,we depended on the model training to strengthen the data association,avoided the overfitting phenomenon of the fully connected layer,and enhanced the generalization ability of the prediction *** used the Kears based on Tensorflow to build the LSTM-XGBoost *** speed data samples of multiple road sections in Shenzhen to complete the model verification,we achieved the comparison of the prediction effects of the *** results show that the combined prediction model used in this paper can not only improve the accuracy of prediction,but also improve the practicability,real-time and scalability of the model.

关键词： traffic flow prediction time series LSTM XGBoost deep learning