Survey of neural network‐based models for short‐term traffic state prediction

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

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Traffic State Prediction Using Convolutional Neural Network

2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE) ◽

10.1109/jcsse.2018.8457359 ◽

2018 ◽

Cited By ~ 4

Author(s):

Ratchanon Toncharoen ◽

Mongkut Piantanakulchai

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Traffic State ◽

State Prediction

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Short-Term Highway Traffic State Prediction Using Structural State Space Models

Journal of Intelligent Transportation Systems ◽

10.1080/15472450.2013.836929 ◽

2014 ◽

Vol 18 (3) ◽

pp. 309-322 ◽

Cited By ~ 13

Author(s):

Chung-Cheng Lu ◽

Xuesong Zhou

Keyword(s):

State Space ◽

Structural State ◽

State Space Models ◽

Highway Traffic ◽

Short Term ◽

Traffic State ◽

State Prediction

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A Novel Hybrid Model for Predicting Traffic Flow via Improved Ensemble Learning Combined with Deep Belief Networks

Mathematical Problems in Engineering ◽

10.1155/2021/7328056 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yikang Rui ◽

Wenqi Lu ◽

Ziwei Yi ◽

Renfei Wu ◽

Bin Ran

Keyword(s):

Neural Network ◽

Traffic Flow ◽

Ensemble Learning ◽

Hybrid Model ◽

Urban Traffic ◽

Combination Method ◽

Belief Networks ◽

Deep Belief Networks ◽

Short Term ◽

Traffic State

The intelligent transportation system (ITS) plays an irreplaceable role in alleviating urban traffic congestion and realizing sustainable urban development. Accurate and efficient short-term traffic state forecasting is a significant issue in ITS. This study proposes a novel hybrid model (ELM-IBF) to predict the traffic state on urban expressways by taking advantage of both deep learning models and ensemble learning framework. First, a developed bagging framework is introduced to combine several deep belief networks (DBNs) that are utilized to capture the complicated temporal characteristic of traffic flow. Then, a novel combination method named improved Bayesian fusion (IBF) is proposed to replace the averaging method in the bagging framework since it can better fuse the prediction results of the component DBNs by assigning the reasonable weights to DBNs at each prediction time interval. Finally, the proposed hybrid model is validated with ground-truth traffic flow data captured by the remote traffic microwave sensors installed on the multiple road sections of 2nd Ring Road in Beijing. The experimental results illustrate that the ELM-IBF method can effectively capture sharp fluctuations in the traffic flow. Compared with several benchmark models (e.g., artificial neural network, long short-term memory neural network, and DBN), the ELM-IBF model reveals better performance in forecasting single-step-ahead traffic volume and speed. Additionally, it is proved that the ELM-IBF model is capable of providing stable and high-quality results in multistep-ahead traffic flow prediction.

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A Special Event-Based K-Nearest Neighbor Model for Short-Term Traffic State Prediction

IEEE Access ◽

10.1109/access.2019.2923663 ◽

2019 ◽

Vol 7 ◽

pp. 81717-81729 ◽

Cited By ~ 7

Author(s):

Haiyang Yu ◽

Nan Ji ◽

Yilong Ren ◽

Can Yang

Keyword(s):

Nearest Neighbor ◽

K Nearest Neighbor ◽

Short Term ◽

Special Event ◽

Traffic State ◽

State Prediction ◽

Event Based

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Road traffic state prediction based on a graph embedding recurrent neural network under the SCATS

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.5117180 ◽

2019 ◽

Vol 29 (10) ◽

pp. 103125 ◽

Cited By ~ 2

Author(s):

Dongwei Xu ◽

Hongwei Dai ◽

Yongdong Wang ◽

Peng Peng ◽

Qi Xuan ◽

...

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Road Traffic ◽

Graph Embedding ◽

Traffic State ◽

State Prediction

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Short-Term Traffic State Prediction Based on Temporal–Spatial Correlation

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2013.2258916 ◽

2013 ◽

Vol 14 (3) ◽

pp. 1242-1254 ◽

Cited By ~ 65

Author(s):

T. L. Pan ◽

A. Sumalee ◽

R. X. Zhong ◽

N. Indra-payoong

Keyword(s):

Spatial Correlation ◽

Short Term ◽

Traffic State ◽

State Prediction

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Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks

Computational Intelligence and Neuroscience ◽

10.1155/2021/9966382 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Tian Ma ◽

Guanghong Gong ◽

Yilong Ren

Keyword(s):

Road Network ◽

Large Scale ◽

Short Term Memory ◽

Transportation Networks ◽

Prediction Method ◽

Short Term ◽

Traffic State ◽

State Prediction ◽

Network Experiment ◽

Maximum Proportion

Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections.

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Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers

Sensors ◽

10.3390/s20030685 ◽

2020 ◽

Vol 20 (3) ◽

pp. 685 ◽

Cited By ~ 10

Author(s):

Muhammad Zahid ◽

Yangzhou Chen ◽

Arshad Jamal ◽

Muhammad Qasim Memon

Keyword(s):

Intelligent Transportation Systems ◽

Transportation Systems ◽

Support Vector ◽

Volume Data ◽

Short Term ◽

Hyperparameter Optimization ◽

Traveler Information ◽

Traffic State ◽

Modeling Approaches ◽

State Prediction

Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.

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