Traffic Flow Correlation Analysis of K Intersections Based on Deep Learning

2018 ◽  
pp. 196-203
Author(s):  
Hung-Chi Chu ◽  
Chi-Kun Wang ◽  
Yi-Xiang Liao
Keyword(s):  
Deep Learning ◽  
Correlation Analysis ◽  
Traffic Flow

A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis

2021 ◽  
Author(s):  
Unsok Ryu ◽  
Jian Wang ◽  
Unjin Pak ◽  
Sonil Kwak ◽  
Kwangchol Ri ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Traffic Flow ◽  
Prediction Method ◽  
Traffic Flow Prediction ◽  
Spatiotemporal Correlation ◽  
Flow Prediction

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

Vietnamese vehicles speed detection with video-based and deep learning for real-time traffic flow analysis system

2021 ◽  
Author(s):  
Phuoc Ha Quang ◽  
Phong Pham Thanh ◽  
Tuan Nguyen Van Anh ◽  
Son Vo Phi ◽  
Binh Le Nhat ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Flow ◽  
Flow Analysis ◽  
Real Time Traffic ◽  
Analysis System ◽  
Traffic Flow Analysis
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