Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate
Connected and autonomous vehicles (CAVs) are on the way to the field application. In the beginning stage, there will be a mixed traffic flow, containing the regular human-driven vehicles and CAVs with a low penetration rate. Recently, the discussion about the impact of a small proportion of CAVs in...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/1361583 |
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| _version_ | 1832566474513842176 |
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| author | Shanglu He Xiaoyu Guo Fan Ding Yong Qi Tao Chen |
| author_facet | Shanglu He Xiaoyu Guo Fan Ding Yong Qi Tao Chen |
| author_sort | Shanglu He |
| collection | DOAJ |
| description | Connected and autonomous vehicles (CAVs) are on the way to the field application. In the beginning stage, there will be a mixed traffic flow, containing the regular human-driven vehicles and CAVs with a low penetration rate. Recently, the discussion about the impact of a small proportion of CAVs in the mixed traffic is controversial. This paper investigated the possibility of applying the limited data from these lowly penetrated CAVs to estimate the average freeway link speeds based on the Kalman filtering (KF) method. First, this paper established a VISSIM-based microsimulation model to mimic the mixed traffic with different CAV penetration rates. The characteristics of this mixed traffic were then discussed based on the simulation data, including the sample size distribution, data-missing rate, speed difference, and fundamental diagram. Accordingly, the traditional KF-based method was introduced and modified to adapt data from CAVs. Finally, the evaluations of the estimation accuracy and the sensitive analysis of the proposed method were conducted. The results revealed the possibility and applicability of link speed estimation using data from a small proportion of CAVs. |
| format | Article |
| id | doaj-art-4ff5b54dd5094742be5980947e75cf68 |
| 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-4ff5b54dd5094742be5980947e75cf682025-02-03T01:03:58ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/13615831361583Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration RateShanglu He0Xiaoyu Guo1Fan Ding2Yong Qi3Tao Chen4School of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaZachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-3135, USAJoint Research Institute on Internet of Mobility, Southeast University and University of Wisconsin-Madison, Madison, WI, USASchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaKey Laboratory for Automotive Transportation Safety Enhancement Technology of the Ministry of Communication, Chang’an University, Xi’an 710084, ChinaConnected and autonomous vehicles (CAVs) are on the way to the field application. In the beginning stage, there will be a mixed traffic flow, containing the regular human-driven vehicles and CAVs with a low penetration rate. Recently, the discussion about the impact of a small proportion of CAVs in the mixed traffic is controversial. This paper investigated the possibility of applying the limited data from these lowly penetrated CAVs to estimate the average freeway link speeds based on the Kalman filtering (KF) method. First, this paper established a VISSIM-based microsimulation model to mimic the mixed traffic with different CAV penetration rates. The characteristics of this mixed traffic were then discussed based on the simulation data, including the sample size distribution, data-missing rate, speed difference, and fundamental diagram. Accordingly, the traditional KF-based method was introduced and modified to adapt data from CAVs. Finally, the evaluations of the estimation accuracy and the sensitive analysis of the proposed method were conducted. The results revealed the possibility and applicability of link speed estimation using data from a small proportion of CAVs.http://dx.doi.org/10.1155/2020/1361583 |
| spellingShingle | Shanglu He Xiaoyu Guo Fan Ding Yong Qi Tao Chen Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate Journal of Advanced Transportation |
| title | Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate |
| title_full | Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate |
| title_fullStr | Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate |
| title_full_unstemmed | Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate |
| title_short | Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate |
| title_sort | freeway traffic speed estimation of mixed traffic using data from connected and autonomous vehicles with a low penetration rate |
| url | http://dx.doi.org/10.1155/2020/1361583 |
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