Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
Intelligent Transportation Systems (ITSs) play a vital role in improving urban and regional mobility by reducing traffic congestion and enhancing trip planning. A key element of ITS is travel-time prediction, which supports informed decisions for both travelers and traffic management. While non-para...
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7729 |
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| author | Miin-Jong Hao Yu-Xuan Zheng |
| author_facet | Miin-Jong Hao Yu-Xuan Zheng |
| author_sort | Miin-Jong Hao |
| collection | DOAJ |
| description | Intelligent Transportation Systems (ITSs) play a vital role in improving urban and regional mobility by reducing traffic congestion and enhancing trip planning. A key element of ITS is travel-time prediction, which supports informed decisions for both travelers and traffic management. While non-parametric models offer flexibility, they often require large datasets and significant computation. Parametric models, though easier to fit and interpret, are less adaptable. Fuzzy logic models, by contrast, provide robustness and scalability, adjusting to new data and changing conditions. This paper proposes a cascaded fuzzy logic system for highway travel-time prediction, using the Greenshields model as its reasoning foundation. The system consists of multiple fuzzy subsystems, each representing a highway segment. These subsystems transform traffic flow and density inputs into speed predictions through fuzzification, Greenshields-based rules, and defuzzification. The approach enables localized and segment-specific predictions, enhancing route planning and congestion avoidance. The system’s accuracy is evaluated by comparing its predictions with those of a regression model using real traffic data from the Sun Yat-Sen Highway in Taiwan. Simulation results confirm that the proposed model achieves reliable, adaptable travel-time forecasts, including for long-distance trips. |
| format | Article |
| id | doaj-art-f3ebc454310d40018bc2f18eb6fa3dfe |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-f3ebc454310d40018bc2f18eb6fa3dfe2025-08-20T03:58:26ZengMDPI AGApplied Sciences2076-34172025-07-011514772910.3390/app15147729Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic SystemsMiin-Jong Hao0Yu-Xuan Zheng1Department of Computer and Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 824005, TaiwanDepartment of Computer and Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 824005, TaiwanIntelligent Transportation Systems (ITSs) play a vital role in improving urban and regional mobility by reducing traffic congestion and enhancing trip planning. A key element of ITS is travel-time prediction, which supports informed decisions for both travelers and traffic management. While non-parametric models offer flexibility, they often require large datasets and significant computation. Parametric models, though easier to fit and interpret, are less adaptable. Fuzzy logic models, by contrast, provide robustness and scalability, adjusting to new data and changing conditions. This paper proposes a cascaded fuzzy logic system for highway travel-time prediction, using the Greenshields model as its reasoning foundation. The system consists of multiple fuzzy subsystems, each representing a highway segment. These subsystems transform traffic flow and density inputs into speed predictions through fuzzification, Greenshields-based rules, and defuzzification. The approach enables localized and segment-specific predictions, enhancing route planning and congestion avoidance. The system’s accuracy is evaluated by comparing its predictions with those of a regression model using real traffic data from the Sun Yat-Sen Highway in Taiwan. Simulation results confirm that the proposed model achieves reliable, adaptable travel-time forecasts, including for long-distance trips.https://www.mdpi.com/2076-3417/15/14/7729travel timevehicle speed predictionITSfuzzy logic systemGreenshields models |
| spellingShingle | Miin-Jong Hao Yu-Xuan Zheng Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems Applied Sciences travel time vehicle speed prediction ITS fuzzy logic system Greenshields models |
| title | Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems |
| title_full | Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems |
| title_fullStr | Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems |
| title_full_unstemmed | Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems |
| title_short | Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems |
| title_sort | highway travel time forecasting with greenshields model based cascaded fuzzy logic systems |
| topic | travel time vehicle speed prediction ITS fuzzy logic system Greenshields models |
| url | https://www.mdpi.com/2076-3417/15/14/7729 |
| work_keys_str_mv | AT miinjonghao highwaytraveltimeforecastingwithgreenshieldsmodelbasedcascadedfuzzylogicsystems AT yuxuanzheng highwaytraveltimeforecastingwithgreenshieldsmodelbasedcascadedfuzzylogicsystems |