Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

Ren, Chuanxiang; Chai, Chunxu; Yin, Changchang; Ji, Haowei; Cheng, Xuezhen; Gao, Ge; Zhang, Heng

doi:10.1155/2021/9928073

Cited by 14 publications

(8 citation statements)

References 62 publications

(61 reference statements)

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“…In the process of vehicle operation, there is temporal continuity between adjacent times. Previous studies have shown that the convolution operation of CNN has strong advantages in extracting spatial features of data [23]. Due to its long-term memory function, LSTM can efectively extract temporal features [24].…”

Section: The Estimation Model Of the Road Adhesion Coefficientmentioning

confidence: 99%

Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning

Liu

Xie

et al. 2023

Journal of Advanced Transportation

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Accurate estimation of the road adhesion coefficient can help drivers and vehicles perceive changes in road state effectively, reducing the occurrence of traffic crashes accordingly. Therefore, this paper proposes a road adhesion coefficient estimation method based on vehicle-road coordination and deep learning. Firstly, a vehicle-based data feedback system combined with a vehicle-road network cloud is introduced, and CarSim simulation is used to expand the data set and train the model effectively. Then, the dynamic analysis of the whole vehicle is carried out, and the vehicle operation data related to the adhesion coefficient are obtained as the input of the estimation model. Then a combined model of road adhesion coefficient estimation based on self-attention (SA), convolutional neural network (CNN), and long short-term memory (LSTM) is established, to reduce the instability of the prediction, Q-learning is used to optimize the weight of the model. Finally, the model is verified by the simulation data and the actual vehicle-based data. The results show that the vehicle-based data feedback system combined with the vehicle-road network Ccloud is effective, and compared with other commonly used model, the estimation model proposed in this paper can effectively predict the road adhesion coefficient.

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Section: The Estimation Model Of the Road Adhesion Coefficientmentioning

confidence: 99%

Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning

Liu

Xie

et al. 2023

Journal of Advanced Transportation

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“…Ref. [31] presented a combined deep learning model for short-term traffic flow forecasting based on LSTM, Gater Recurrent Unit (GRU), Convolution Neural Network (CNN), and Dynamic Optimal Weighted Coefficient Algorithm (DOWCA).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach

et al. 2022

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Nowadays, short-term traffic flow forecasting has gained increasing attention from researchers due to traffic congestion in many large and medium-sized cities that pose a serious threat to sustainable urban development. To this end, this research examines the forecasting performance of functional time series modeling to forecast traffic flow in the ultra-short term. An appealing feature of the functional approach is that unlike other methods, it provides information over the whole day, and thus, forecasts can be obtained for any time within a day. Within this approach, a Functional AutoRegressive (FAR) model is used to forecast the next-day traffic flow. For empirical analysis, the traffic flow data of Dublin airport link road, Ireland, collected at a fifteen-minute interval from 1 January 2016 to 30 April 2017, are used. The first twelve months are used for model estimation, while the remaining four months are for the one-day-ahead out-of-sample forecast. For comparison purposes, a widely used model, namely AutoRegressive Integrated Moving Average (ARIMA), is also used to obtain the forecasts. Finally, the models’ performances are compared based on different accuracy statistics. The study results suggested that the functional time series model outperforms the traditional time series models. As the proposed method can produce traffic flow forecasts for the entire next day with satisfactory results, it can be used in decision making by transportation policymakers and city planners.

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“…A number of research works have used convolutional neural networks (CNNs) to extract spatial connections from two-layered geospatial traffic data [49][50][51]. Since it is very difficult to represent the traffic network using 2D grids, few studies in [30,52,53] have attempted to convert the structure of the traffic network into images, which are then used to map spatial similarities between different locations. However, space-time relationships are not reflected in transforming geographic images into different matrices and in learning landmarks using CNN.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features

et al. 2022

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Due to recent advances in the Vehicular Internet of Things (VIoT), a large volume of traffic trajectory data has been generated. The trajectory data is highly unstructured and pre-processing it is a very cumbersome task, due to the complexity of the traffic data. However, the accuracy of traffic flow learning models depends on the quantity and quality of preprocessed data. Hence, there is a significant gap between the size and quality of benchmarked traffic datasets and the respective learning models. Additionally, generating a custom traffic dataset with required feature points in a constrained environment is very difficult. This research aims to harness the power of the deep learning hybrid model with datasets that have fewer feature points. Therefore, a hybrid deep learning model that extracts the optimal feature points from the existing dataset using a stacked autoencoder is presented. Handcrafted feature points are fed into the hybrid deep neural network to predict the travel path and travel time between two geographic points. The chengdu1 and chengdu2 standard reference datasets are used to realize our hypothesis of the evolution of a hybrid deep neural network with minimal feature points. The hybrid model includes the graph neural networks (GNN) and the residual networks (ResNet) preceded by the stacked autoencoder (SAE). This hybrid model simultaneously learns the temporal and spatial characteristics of the traffic data. Temporal feature points are optimally reduced using Stacked Autoencoder to improve the accuracy of the deep neural network. The proposed GNN + Resnet model performance was compared to models in the literature using root mean square error (RMSE) loss, mean absolute error (MAE) and mean absolute percentile error (MAPE). The proposed model was found to perform better by improving the travel time prediction loss on chengdu1 and chengdu2 datasets. An in-depth comprehension of the proposed GNN + Resnet model for predicting travel time during peak and off-peak periods is also presented. The model’s RMSE loss was improved up to 22.59% for peak hours traffic data and up to 11.05% for off-peak hours traffic data in the chengdu1 dataset.

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Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

Cited by 14 publications

References 62 publications

Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning

Road Adhesion Coefficient Estimation Based on Vehicle-Road Coordination and Deep Learning

Forecasting Day-Ahead Traffic Flow Using Functional Time Series Approach

A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features

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