Mixed Traffic Flow Modeling Solution on Digital Map

This paper presents an advanced mixed traffic flow modeling solution utilizing deep learning and computer vision techniques integrated with digital mapping technologies. The proposed system employs YOLOv8 to detect and classify various traffic participants, including cars, trucks,… DeepSORT for multi-object tracking. OpenCV, along with our specialized algorithms, is utilized to estimate the speed of each detected object, recognize vehicle license plates, and determine the GPS location of each participant based on the camera's geolocation. These data points are then used to simulate real-time traffic flow on a digital map. The system's effectiveness is validated through a series of experiments, demonstrating its potential for digitizing traffic flow on a digital map, thereby enhancing traffic management and urban planning.

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Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/icce-asia63397.2024.10773793
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Mixed Traffic Flow Modeling Solution on Digital Map

Ning Dong, Hoang Vu Tran, Phan Cao Tho, Gia Nhu Nguyen
Traffic Prediction and Management Techniques
article

Mixed Traffic Flow Modeling Solution on Digital Map

Ning Dong, Hoang Vu Tran, Phan Cao Tho, Gia Nhu Nguyen
article en

Abstract

This paper presents an advanced mixed traffic flow modeling solution utilizing deep learning and computer vision techniques integrated with digital mapping technologies. The proposed system employs YOLOv8 to detect and classify various traffic participants, including cars, trucks,… DeepSORT for multi-object tracking. OpenCV, along with our specialized algorithms, is utilized to estimate the speed of each detected object, recognize vehicle license plates, and determine the GPS location of each participant based on the camera's geolocation. These data points are then used to simulate real-time traffic flow on a digital map. The system's effectiveness is validated through a series of experiments, demonstrating its potential for digitizing traffic flow on a digital map, thereby enhancing traffic management and urban planning.

Duy Tan University (VN), University of Da Nang (VN)
Openalex Percentile: Top 73%
Traffic Prediction and Management Techniques
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