Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model
Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance i...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/2578480 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832565338439417856 |
|---|---|
| author | Yongyi Li Ming Zhang Yixing Ding Zhenghua Zhou Lingyu Xu |
| author_facet | Yongyi Li Ming Zhang Yixing Ding Zhenghua Zhou Lingyu Xu |
| author_sort | Yongyi Li |
| collection | DOAJ |
| description | Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model’s parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance. |
| format | Article |
| id | doaj-art-e448fe6981f54ae3a4bffe786299b9b8 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e448fe6981f54ae3a4bffe786299b9b82025-02-03T01:08:46ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/2578480Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning ModelYongyi Li0Ming Zhang1Yixing Ding2Zhenghua Zhou3Lingyu Xu4Nanjing Tech UniversityNanjing Tech UniversityZTE CorporationNanjing Tech UniversityNanjing Tech UniversityUrban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model’s parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance.http://dx.doi.org/10.1155/2022/2578480 |
| spellingShingle | Yongyi Li Ming Zhang Yixing Ding Zhenghua Zhou Lingyu Xu Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model Journal of Advanced Transportation |
| title | Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model |
| title_full | Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model |
| title_fullStr | Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model |
| title_full_unstemmed | Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model |
| title_short | Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model |
| title_sort | real time travel time prediction based on evolving fuzzy participatory learning model |
| url | http://dx.doi.org/10.1155/2022/2578480 |
| work_keys_str_mv | AT yongyili realtimetraveltimepredictionbasedonevolvingfuzzyparticipatorylearningmodel AT mingzhang realtimetraveltimepredictionbasedonevolvingfuzzyparticipatorylearningmodel AT yixingding realtimetraveltimepredictionbasedonevolvingfuzzyparticipatorylearningmodel AT zhenghuazhou realtimetraveltimepredictionbasedonevolvingfuzzyparticipatorylearningmodel AT lingyuxu realtimetraveltimepredictionbasedonevolvingfuzzyparticipatorylearningmodel |