Deep Learning-Based Computer Vision Methods for Complex Traffic Environments Perception: A Review

Azfar, Talha; Li, Jinlong; Yu, Hongkai; Cheu, Ruey L.; Lv, Yisheng; Ke, Ruimin

doi:10.1007/s42421-023-00086-7

Data Sci. Transp.

2024

DOI: 10.1007/s42421-023-00086-7

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Deep Learning-Based Computer Vision Methods for Complex Traffic Environments Perception: A Review

Talha Azfar,

Jinlong Li,

Hongkai Yu

et al.

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Cited by 3 publications

References 217 publications

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Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Jin,

Xu,

Wang

et al. 2024

Data Sci. Transp.

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The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

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Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Jin,

Xu,

Wang

et al. 2024

Data Sci. Transp.

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An Imputation-Enhanced Hybrid Deep Learning Approach for Traffic Volume Prediction in Urban Networks: A Case Study in Dresden

Yan,

Li,

Ijaradar

et al. 2024

Data Sci. Transp.

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Advanced traffic management systems rely heavily on accurate traffic state estimation and prediction. Traffic prediction based on conventional road-based sensors faces considerable challenges due to spatiotemporal correlations of traffic flow propagation, and heterogeneous, error-prone, and missing data. This paper proposes a hybrid deep learning approach for online traffic volume prediction in an urban network. The approach ensembles the long short-term memory (LSTM) neural network and the convolutional neural networks (CNN) in a parallel way. In order to deal with missing data, a state-of-the-art Bayesian probabilistic imputation method is employed in the overall prediction pipeline. The hybrid traffic prediction structure can capture the spatiotemporal characteristics of traffic volume. The proposed prediction model is verified by the loop and infrared sensor data in the inner city network of the City of Dresden. Experimental results show that it can achieve superior volume prediction compared with baseline methods.

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Real Time Car Model and Plate Detection System by Using Deep Learning Architectures

Mustafa,

Karabatak

2024

IEEE Access

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Deep Learning-Based Computer Vision Methods for Complex Traffic Environments Perception: A Review

Cited by 3 publications

References 217 publications

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

An Imputation-Enhanced Hybrid Deep Learning Approach for Traffic Volume Prediction in Urban Networks: A Case Study in Dresden

Real Time Car Model and Plate Detection System by Using Deep Learning Architectures

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