Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making
Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper propose...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/7835010 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561073101733888 |
|---|---|
| author | Mudasser Seraj Jiangchen Li Zhijun Qiu |
| author_facet | Mudasser Seraj Jiangchen Li Zhijun Qiu |
| author_sort | Mudasser Seraj |
| collection | DOAJ |
| description | Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations. |
| format | Article |
| id | doaj-art-09fda489a95e466cbba99dd5fa7f7cbc |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-09fda489a95e466cbba99dd5fa7f7cbc2025-02-03T01:25:59ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/78350107835010Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-MakingMudasser Seraj0Jiangchen Li1Zhijun Qiu2Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, CanadaMicroscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations.http://dx.doi.org/10.1155/2018/7835010 |
| spellingShingle | Mudasser Seraj Jiangchen Li Zhijun Qiu Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making Journal of Advanced Transportation |
| title | Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making |
| title_full | Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making |
| title_fullStr | Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making |
| title_full_unstemmed | Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making |
| title_short | Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making |
| title_sort | modeling microscopic car following strategy of mixed traffic to identify optimal platoon configurations for multiobjective decision making |
| url | http://dx.doi.org/10.1155/2018/7835010 |
| work_keys_str_mv | AT mudasserseraj modelingmicroscopiccarfollowingstrategyofmixedtraffictoidentifyoptimalplatoonconfigurationsformultiobjectivedecisionmaking AT jiangchenli modelingmicroscopiccarfollowingstrategyofmixedtraffictoidentifyoptimalplatoonconfigurationsformultiobjectivedecisionmaking AT zhijunqiu modelingmicroscopiccarfollowingstrategyofmixedtraffictoidentifyoptimalplatoonconfigurationsformultiobjectivedecisionmaking |