High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS
Despite the achievements of academic research on data-driven k-nearest neighbour nonparametric regression (KNN-NPR), the low-speed computational capability of the KNN-NPR method, which can occur during searches involving enormous amounts of historical data, remains a major obstacle to improvements o...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/5728042 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560235656511488 |
|---|---|
| author | Hyun-ho Chang Byoung-jo Yoon |
| author_facet | Hyun-ho Chang Byoung-jo Yoon |
| author_sort | Hyun-ho Chang |
| collection | DOAJ |
| description | Despite the achievements of academic research on data-driven k-nearest neighbour nonparametric regression (KNN-NPR), the low-speed computational capability of the KNN-NPR method, which can occur during searches involving enormous amounts of historical data, remains a major obstacle to improvements of real-system applications. To overcome this critical issue successfully, a high-speed KNN-NPR framework, capable of generating short-term traffic volume predictions, is proposed in this study. The proposed method is based on a two-step search algorithm, which has the two roles of building promising candidates for input data during nonprediction times and identifying decision-making input data for instantaneous predictions at the prediction point. To prove the efficacy of the proposed model, an experimental test was conducted with large-size traffic volume data. It was found that the performance of the model not only at least equals that of linear-search-based KNN-NPR in terms of prediction accuracy, but also shows a substantially reduced execution time in approximating real-time applications. This result suggests that the proposed algorithm can be also effectively employed as a preprocess to select useful past cases for advanced learning-based forecasting models. |
| format | Article |
| id | doaj-art-35ec913071da43908f46853a2324a5b5 |
| 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-35ec913071da43908f46853a2324a5b52025-02-03T01:28:06ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/57280425728042High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITSHyun-ho Chang0Byoung-jo Yoon1School of Environmental Studies, Seoul National University, Seoul, Republic of KoreaDepartment of Urban Engineering, Incheon National University, Incheon, Republic of KoreaDespite the achievements of academic research on data-driven k-nearest neighbour nonparametric regression (KNN-NPR), the low-speed computational capability of the KNN-NPR method, which can occur during searches involving enormous amounts of historical data, remains a major obstacle to improvements of real-system applications. To overcome this critical issue successfully, a high-speed KNN-NPR framework, capable of generating short-term traffic volume predictions, is proposed in this study. The proposed method is based on a two-step search algorithm, which has the two roles of building promising candidates for input data during nonprediction times and identifying decision-making input data for instantaneous predictions at the prediction point. To prove the efficacy of the proposed model, an experimental test was conducted with large-size traffic volume data. It was found that the performance of the model not only at least equals that of linear-search-based KNN-NPR in terms of prediction accuracy, but also shows a substantially reduced execution time in approximating real-time applications. This result suggests that the proposed algorithm can be also effectively employed as a preprocess to select useful past cases for advanced learning-based forecasting models.http://dx.doi.org/10.1155/2018/5728042 |
| spellingShingle | Hyun-ho Chang Byoung-jo Yoon High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS Journal of Advanced Transportation |
| title | High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS |
| title_full | High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS |
| title_fullStr | High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS |
| title_full_unstemmed | High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS |
| title_short | High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS |
| title_sort | high speed data driven methodology for real time traffic flow predictions practical applications of its |
| url | http://dx.doi.org/10.1155/2018/5728042 |
| work_keys_str_mv | AT hyunhochang highspeeddatadrivenmethodologyforrealtimetrafficflowpredictionspracticalapplicationsofits AT byoungjoyoon highspeeddatadrivenmethodologyforrealtimetrafficflowpredictionspracticalapplicationsofits |