Proportional-Switch Adjustment Process-Based Day-by-Day Evolution Model for Mixed Traffic Flow in an Autonomous Driving Environment
Given the rapid development of technologies such as new energy vehicles, autonomous driving, and vehicle-to-everything (V2X) communication, a mixed traffic flow comprising connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) is anticipated to emerge. This necessitates the develo...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/16/1/53 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587267133145088 |
|---|---|
| author | Yihao Huang Han Zhang Aiwu Kuang |
| author_facet | Yihao Huang Han Zhang Aiwu Kuang |
| author_sort | Yihao Huang |
| collection | DOAJ |
| description | Given the rapid development of technologies such as new energy vehicles, autonomous driving, and vehicle-to-everything (V2X) communication, a mixed traffic flow comprising connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) is anticipated to emerge. This necessitates the development of a daily dynamic evolution model for mixed traffic flow to address the dynamic traffic management needs of urban environments characterized by mixed traffic. The daily dynamic evolution model can capture the temporal evolution of traffic flow in road networks, with a focus on the daily path choice behavior of travelers and the evolving traffic flow in the network. First, based on the travel characteristics of CAVs and HDVs, the user group in a connected autonomous driving environment is classified into three categories, each adhering to the system optimal (SO) criterion, the user equilibrium (UE) criterion, or the stochastic user equilibrium (SUE) criterion. Next, the pure HDV traffic capacity BPR (Bureau of Public Roads) function is adapted into a heterogeneous traffic flow travel time function to compute the travel time cost for mixed traffic flow. Based on the energy consumption calculation formula for HDVs, the impact of CAVs is fully considered to establish the travel energy consumption cost for both CAVs and HDVs. The total individual travel cost for CAVs and HDVs encompasses both travel time cost and energy consumption cost. Furthermore, a daily dynamic evolution model for mixed traffic flow in a connected autonomous driving environment is developed using the proportional-switch adjustment process (PAP) model. The fundamental properties of the model are validated. Finally, numerical simulations on an N-dimensional (N-D) network confirm the validity and effectiveness of the daily evolution model for mixed traffic flow. A sensitivity analysis of traveler responses in the daily evolution model reveals that, as the sensitivity of CAVs to impedance changes increases, the fluctuations in mixed traffic flow during the early stages of evolution become more pronounced, and the time required to reach a mixed-equilibrium state decreases. Therefore, the PAP-based daily dynamic evolution model for mixed traffic flow effectively captures the evolution process of CAV and HDV mixed traffic flow and supports urban traffic management in a connected autonomous driving environment. |
| format | Article |
| id | doaj-art-1a19d420458142159b1ddb6c59986ba2 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-1a19d420458142159b1ddb6c59986ba22025-01-24T13:52:54ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-01-011615310.3390/wevj16010053Proportional-Switch Adjustment Process-Based Day-by-Day Evolution Model for Mixed Traffic Flow in an Autonomous Driving EnvironmentYihao Huang0Han Zhang1Aiwu Kuang2Department of Automotive Engineering, Xiangtan Technician College, Xiangtan 411100, ChinaHenan Vocational College of Water Conservancy and Environment, Zhengzhou 450008, ChinaSchool of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaGiven the rapid development of technologies such as new energy vehicles, autonomous driving, and vehicle-to-everything (V2X) communication, a mixed traffic flow comprising connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) is anticipated to emerge. This necessitates the development of a daily dynamic evolution model for mixed traffic flow to address the dynamic traffic management needs of urban environments characterized by mixed traffic. The daily dynamic evolution model can capture the temporal evolution of traffic flow in road networks, with a focus on the daily path choice behavior of travelers and the evolving traffic flow in the network. First, based on the travel characteristics of CAVs and HDVs, the user group in a connected autonomous driving environment is classified into three categories, each adhering to the system optimal (SO) criterion, the user equilibrium (UE) criterion, or the stochastic user equilibrium (SUE) criterion. Next, the pure HDV traffic capacity BPR (Bureau of Public Roads) function is adapted into a heterogeneous traffic flow travel time function to compute the travel time cost for mixed traffic flow. Based on the energy consumption calculation formula for HDVs, the impact of CAVs is fully considered to establish the travel energy consumption cost for both CAVs and HDVs. The total individual travel cost for CAVs and HDVs encompasses both travel time cost and energy consumption cost. Furthermore, a daily dynamic evolution model for mixed traffic flow in a connected autonomous driving environment is developed using the proportional-switch adjustment process (PAP) model. The fundamental properties of the model are validated. Finally, numerical simulations on an N-dimensional (N-D) network confirm the validity and effectiveness of the daily evolution model for mixed traffic flow. A sensitivity analysis of traveler responses in the daily evolution model reveals that, as the sensitivity of CAVs to impedance changes increases, the fluctuations in mixed traffic flow during the early stages of evolution become more pronounced, and the time required to reach a mixed-equilibrium state decreases. Therefore, the PAP-based daily dynamic evolution model for mixed traffic flow effectively captures the evolution process of CAV and HDV mixed traffic flow and supports urban traffic management in a connected autonomous driving environment.https://www.mdpi.com/2032-6653/16/1/53day-by-day evolution modelmixed traffic flowN-D network |
| spellingShingle | Yihao Huang Han Zhang Aiwu Kuang Proportional-Switch Adjustment Process-Based Day-by-Day Evolution Model for Mixed Traffic Flow in an Autonomous Driving Environment World Electric Vehicle Journal day-by-day evolution model mixed traffic flow N-D network |
| title | Proportional-Switch Adjustment Process-Based Day-by-Day Evolution Model for Mixed Traffic Flow in an Autonomous Driving Environment |
| title_full | Proportional-Switch Adjustment Process-Based Day-by-Day Evolution Model for Mixed Traffic Flow in an Autonomous Driving Environment |
| title_fullStr | Proportional-Switch Adjustment Process-Based Day-by-Day Evolution Model for Mixed Traffic Flow in an Autonomous Driving Environment |
| title_full_unstemmed | Proportional-Switch Adjustment Process-Based Day-by-Day Evolution Model for Mixed Traffic Flow in an Autonomous Driving Environment |
| title_short | Proportional-Switch Adjustment Process-Based Day-by-Day Evolution Model for Mixed Traffic Flow in an Autonomous Driving Environment |
| title_sort | proportional switch adjustment process based day by day evolution model for mixed traffic flow in an autonomous driving environment |
| topic | day-by-day evolution model mixed traffic flow N-D network |
| url | https://www.mdpi.com/2032-6653/16/1/53 |
| work_keys_str_mv | AT yihaohuang proportionalswitchadjustmentprocessbaseddaybydayevolutionmodelformixedtrafficflowinanautonomousdrivingenvironment AT hanzhang proportionalswitchadjustmentprocessbaseddaybydayevolutionmodelformixedtrafficflowinanautonomousdrivingenvironment AT aiwukuang proportionalswitchadjustmentprocessbaseddaybydayevolutionmodelformixedtrafficflowinanautonomousdrivingenvironment |