Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment
Adverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highw...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8842730 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567972721328128 |
|---|---|
| author | Bowen Gong Ruixin Wei Dayong Wu Ciyun Lin |
| author_facet | Bowen Gong Ruixin Wei Dayong Wu Ciyun Lin |
| author_sort | Bowen Gong |
| collection | DOAJ |
| description | Adverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highway, this paper uses a mathematical modeling method to study and control the fleet mixed with human-driven vehicles (HDVs) and connected automatic vehicles (CAVs) in dense fog environment on highway based on distributed model predictive control algorithm (DMPC), along with considering the car-following behavior of HDVs driver based on cellular automatic (CA) model. It aims to provide a feasible solution for controlling the mixed flow of HDVs and CAVs more safely, accurately, and stably and then potentially to improve the mobility and efficiency of highway networks in adverse weather conditions, especially in dense fog environment. This paper explores the modeling framework of the fleet management for HDVs and CAVs, including the state space model of CAVs, the car-following model of HDVs, distributed model predictive control for the fleet, and the fleet stability analysis. The state space model is proposed to identify the status of the feet in the global state. The car-following model is proposed to simulate the driver behavior in the fleet in local. The DMPC-based model is proposed to optimize rolling of the fleet. Finally, this paper used the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment. Finally, numerical experiments were performed in MATLAB to verify the effectiveness of the proposed model. The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability. |
| format | Article |
| id | doaj-art-e07991ade5654d898496bc0953ee1f03 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e07991ade5654d898496bc0953ee1f032025-02-03T01:00:07ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88427308842730Fleet Management for HDVs and CAVs on Highway in Dense Fog EnvironmentBowen Gong0Ruixin Wei1Dayong Wu2Ciyun Lin3Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, ChinaDepartment of Traffic Information and Control Engineering, Jilin University, Changchun 130022, ChinaTexas A&M Transportation Institute, Texas A&M University, College Station, TX 77843, USADepartment of Traffic Information and Control Engineering, Jilin University, Changchun 130022, ChinaAdverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highway, this paper uses a mathematical modeling method to study and control the fleet mixed with human-driven vehicles (HDVs) and connected automatic vehicles (CAVs) in dense fog environment on highway based on distributed model predictive control algorithm (DMPC), along with considering the car-following behavior of HDVs driver based on cellular automatic (CA) model. It aims to provide a feasible solution for controlling the mixed flow of HDVs and CAVs more safely, accurately, and stably and then potentially to improve the mobility and efficiency of highway networks in adverse weather conditions, especially in dense fog environment. This paper explores the modeling framework of the fleet management for HDVs and CAVs, including the state space model of CAVs, the car-following model of HDVs, distributed model predictive control for the fleet, and the fleet stability analysis. The state space model is proposed to identify the status of the feet in the global state. The car-following model is proposed to simulate the driver behavior in the fleet in local. The DMPC-based model is proposed to optimize rolling of the fleet. Finally, this paper used the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment. Finally, numerical experiments were performed in MATLAB to verify the effectiveness of the proposed model. The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability.http://dx.doi.org/10.1155/2020/8842730 |
| spellingShingle | Bowen Gong Ruixin Wei Dayong Wu Ciyun Lin Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment Journal of Advanced Transportation |
| title | Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment |
| title_full | Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment |
| title_fullStr | Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment |
| title_full_unstemmed | Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment |
| title_short | Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment |
| title_sort | fleet management for hdvs and cavs on highway in dense fog environment |
| url | http://dx.doi.org/10.1155/2020/8842730 |
| work_keys_str_mv | AT bowengong fleetmanagementforhdvsandcavsonhighwayindensefogenvironment AT ruixinwei fleetmanagementforhdvsandcavsonhighwayindensefogenvironment AT dayongwu fleetmanagementforhdvsandcavsonhighwayindensefogenvironment AT ciyunlin fleetmanagementforhdvsandcavsonhighwayindensefogenvironment |