Implementing Convolution Neural Network (CNN) Based Approach for Traffic Queue Length and Delay Estimation at Highway Ramps
Publication: Computing in Civil Engineering 2023
ABSTRACT
The queue length is a critical performance indicator for traffic networks, traditionally measured using sensors such as induction loops, piezoelectric sensors, and pneumatic tubes. However, these sensors often fail to provide accurate information about traffic flow during periods of congestion, leading to potential inaccuracies in measuring queue length. To overcome these challenges, this paper proposes an image processing technique combining object detection with You Only Look Once (YOLO) followed by simple online and realtime tracking with deep association metric (DeepSORT) to estimate the queue length and delay. To test the performance of the given framework, 50 samples of 1, 5, 10, and 15 min long videos were randomly extracted from Utah highways. The proposed method estimated the queue length and queue delay with 72.56% and 69.08% accuracy, respectively. This study has significant implications for estimating ramp parameters and improving ramp performance.
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Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 257 - 264
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computer vision and image processing
- Computing in civil engineering
- Engineering fundamentals
- Equipment and machinery
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Methodology (by type)
- Neural networks
- Probe instruments
- Ramps (road)
- Traffic congestion
- Traffic delay
- Traffic engineering
- Traffic flow
- Traffic management
- Transportation engineering
- Transportation management
- Transportation networks
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