Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems
Foggy weather seriously deteriorates the performance of freeway systems, particularly regarding traffic safety and efficiency. General macroscopic traffic models have difficulty reflecting the characteristics of a freeway under foggy weather conditions. In the present study, a macroscopic traffic mo...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/5125724 |
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| author | Changle Sun Hongyan Gao |
| author_facet | Changle Sun Hongyan Gao |
| author_sort | Changle Sun |
| collection | DOAJ |
| description | Foggy weather seriously deteriorates the performance of freeway systems, particularly regarding traffic safety and efficiency. General macroscopic traffic models have difficulty reflecting the characteristics of a freeway under foggy weather conditions. In the present study, a macroscopic traffic model using a correction factor under foggy weather conditions is therefore proposed, which is regulated according to the different levels of visibility and curve radius of the freeway using the Takagi–Sugeno (T-S) model. Based on the proposed traffic model, a local ramp metering strategy with density correction under foggy weather conditions is proposed to improve traffic safety. The proposed local ramp metering strategy regulates the on-ramp flow using the T-S model according to the mainstream density, speed, and visibility. The correction factors are determined based on the parameters of the consequent part in the T-S model, which are optimized using the particle swarm optimization algorithm. The sum of the mean absolute percentage error of the mainstream traffic density and speed is used to evaluate the proposed traffic model. The real-time crash-risk prediction model, which reflects the degree of traffic safety, is used to evaluate the proposed local ramp metering strategy. Simulations using VISSIM and MATLAB show that the proposed traffic model is suitable under foggy weather conditions and that the proposed local ramp metering strategy achieves a better performance in reducing fog-related crashes. |
| format | Article |
| id | doaj-art-97b3768fbaef4bc78b69c8517fd1bf6a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-97b3768fbaef4bc78b69c8517fd1bf6a2025-02-03T05:46:51ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/51257245125724Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy SystemsChangle Sun0Hongyan Gao1College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaFoggy weather seriously deteriorates the performance of freeway systems, particularly regarding traffic safety and efficiency. General macroscopic traffic models have difficulty reflecting the characteristics of a freeway under foggy weather conditions. In the present study, a macroscopic traffic model using a correction factor under foggy weather conditions is therefore proposed, which is regulated according to the different levels of visibility and curve radius of the freeway using the Takagi–Sugeno (T-S) model. Based on the proposed traffic model, a local ramp metering strategy with density correction under foggy weather conditions is proposed to improve traffic safety. The proposed local ramp metering strategy regulates the on-ramp flow using the T-S model according to the mainstream density, speed, and visibility. The correction factors are determined based on the parameters of the consequent part in the T-S model, which are optimized using the particle swarm optimization algorithm. The sum of the mean absolute percentage error of the mainstream traffic density and speed is used to evaluate the proposed traffic model. The real-time crash-risk prediction model, which reflects the degree of traffic safety, is used to evaluate the proposed local ramp metering strategy. Simulations using VISSIM and MATLAB show that the proposed traffic model is suitable under foggy weather conditions and that the proposed local ramp metering strategy achieves a better performance in reducing fog-related crashes.http://dx.doi.org/10.1155/2019/5125724 |
| spellingShingle | Changle Sun Hongyan Gao Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems Complexity |
| title | Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems |
| title_full | Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems |
| title_fullStr | Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems |
| title_full_unstemmed | Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems |
| title_short | Traffic Model and On-Ramp Metering Strategy under Foggy Weather Conditions Using T-S Fuzzy Systems |
| title_sort | traffic model and on ramp metering strategy under foggy weather conditions using t s fuzzy systems |
| url | http://dx.doi.org/10.1155/2019/5125724 |
| work_keys_str_mv | AT changlesun trafficmodelandonrampmeteringstrategyunderfoggyweatherconditionsusingtsfuzzysystems AT hongyangao trafficmodelandonrampmeteringstrategyunderfoggyweatherconditionsusingtsfuzzysystems |