LAPSE:2023.30849
Published Article

LAPSE:2023.30849
The Double Lanes Cell Transmission Model of Mixed Traffic Flow in Urban Intelligent Network
April 17, 2023
Abstract
The connected and autonomous vehicle (CAV) is promised to ease congestion in the future with the rapid development of related technologies in recent years. To explore the characteristics of mixed-traffic flow and the dynamic transmission mechanism, this paper firstly detailed the car-following model of different vehicle types, establishing the fundamental diagram of the mixed-traffic flow through considering the different penetration rates and fleet size of CAV. Secondly, this paper constructed the lane-changing judgment mechanism based on the random utility theory. Finally, the paper proposed a lane-level dynamic cell transmission process, combined with a lane-changing strategy and cell transmission model. The effectiveness and feasibility of the model are verified using simulation analysis. This model makes a systematic, theoretical analysis from the perspective of the internal operation mechanism of traffic flow, and the lane-level traffic strategy provides a theoretical basis for balancing urban lane distribution and intelligent traffic management and control.
The connected and autonomous vehicle (CAV) is promised to ease congestion in the future with the rapid development of related technologies in recent years. To explore the characteristics of mixed-traffic flow and the dynamic transmission mechanism, this paper firstly detailed the car-following model of different vehicle types, establishing the fundamental diagram of the mixed-traffic flow through considering the different penetration rates and fleet size of CAV. Secondly, this paper constructed the lane-changing judgment mechanism based on the random utility theory. Finally, the paper proposed a lane-level dynamic cell transmission process, combined with a lane-changing strategy and cell transmission model. The effectiveness and feasibility of the model are verified using simulation analysis. This model makes a systematic, theoretical analysis from the perspective of the internal operation mechanism of traffic flow, and the lane-level traffic strategy provides a theoretical basis for balancing urban lane distribution and intelligent traffic management and control.
Record ID
Keywords
cell transmission model, lane-changing judgment mechanism, mixed-traffic flow fundamental diagram, the random utility theory, vehicle driving characteristics
Subject
Suggested Citation
Tian W, Ma J, Qiu L, Wang X, Lin Z, Luo C, Li Y, Fang Y. The Double Lanes Cell Transmission Model of Mixed Traffic Flow in Urban Intelligent Network. (2023). LAPSE:2023.30849
Author Affiliations
Tian W: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Ma J: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China [ORCID]
Qiu L: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Wang X: College of Rail Transportation, Soochow University, Suzhou 215131, China [ORCID]
Lin Z: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Luo C: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Li Y: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China [ORCID]
Fang Y: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Ma J: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China [ORCID]
Qiu L: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Wang X: College of Rail Transportation, Soochow University, Suzhou 215131, China [ORCID]
Lin Z: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Luo C: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Li Y: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China [ORCID]
Fang Y: College of Electrical Engineering, Zhejiang University, Hangzhou 310011, China
Journal Name
Energies
Volume
16
Issue
7
First Page
3108
Year
2023
Publication Date
2023-03-29
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en16073108, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.30849
This Record
External Link

https://doi.org/10.3390/en16073108
Publisher Version
Download
Meta
Record Statistics
Record Views
364
Version History
[v1] (Original Submission)
Apr 17, 2023
Verified by curator on
Apr 17, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
http://psecommunity.org/LAPSE:2023.30849
Record Owner
Auto Uploader for LAPSE
Links to Related Works
(0.09 seconds)
[0.09 s]
