CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12050 > Article
Paper
10 November 2021 Characteristic analysis and behavior prediction method of converging behavior in urban expressway weaving section
Yi Su, Cong-ying Li, Jia-wei Zhang, Yue-yang Meng, Wei Li
Author Affiliations +
Proceedings Volume 12050, International Conference on Smart Transportation and City Engineering 2021; 120503X (2021) https://doi.org/10.1117/12.2613899
Event: 2021 International Conference on Smart Transportation and City Engineering, 2021, Chongqing, China
Abstract
The merging behavior in urban expressway weaving section affects the operation efficiency of the weaving area and reduce the efficiency of expressway traffic. This article obtains the video data, selects the merging position and speed of the merging vehicle for analysis. Clustering analysis method is used to evaluate the vehicle's merging position. The contour coefficient is used for evaluate the results. Speed analysis result shows that the optimal cluster number is 3. 89.04% of the vehicle speeds are within the ±2.5mꞏs-1 interval of the main line speed, 42.5% of vehicles merged at a speed lower than the main line speed, and 57.5% of vehicles had a speed higher than the main line traffic speed. According to the above analysis, the merging behavior of expressway interweaving area is classified. And choose the support vector machine algorithm to predict it. The results show that the accuracy of the SVM model is above 78%, indicating that the SVM model can better predict the merging behavior of vehicles in the interweaving area on the ramp.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Yi Su, Cong-ying Li, Jia-wei Zhang, Yue-yang Meng, and Wei Li "Characteristic analysis and behavior prediction method of converging behavior in urban expressway weaving section", Proc. SPIE 12050, International Conference on Smart Transportation and City Engineering 2021, 120503X (10 November 2021); https://doi.org/10.1117/12.2613899
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Machine learning
Data modeling
Analytical research
Detection and tracking algorithms
Feature extraction
Video processing
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top