PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data
Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time, consequently helping them avoid congestion and accidents to a certain extent. However, the existing methods for real-time urban traffic flow prediction focus on improving the model pr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-03-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020017 |
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| _version_ | 1832568870983958528 |
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| author | Shan Zhang Qinkai Jiang Hao Li Bin Cao Jing Fan |
| author_facet | Shan Zhang Qinkai Jiang Hao Li Bin Cao Jing Fan |
| author_sort | Shan Zhang |
| collection | DOAJ |
| description | Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time, consequently helping them avoid congestion and accidents to a certain extent. However, the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency, which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure. To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining, herein, we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data (PURP). First, to ensure prediction accuracy, PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition (LPR) data as effective characteristics. Subsequently, to utilize the recent data without retraining the model online, PURP uses the nonparametric method k-Nearest Neighbor (namely KNN) as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online. The experimental results show that PURP retains strong prediction efficiency as the prediction period increases. |
| format | Article |
| id | doaj-art-c890b1b182b44e5cb62244332f5163f6 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-c890b1b182b44e5cb62244332f5163f62025-02-03T00:17:03ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-03-017117118710.26599/BDMA.2023.9020017PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition DataShan Zhang0Qinkai Jiang1Hao Li2Bin Cao3Jing Fan4School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310000, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310000, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310000, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310000, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310000, ChinaAccurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time, consequently helping them avoid congestion and accidents to a certain extent. However, the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency, which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure. To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining, herein, we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data (PURP). First, to ensure prediction accuracy, PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition (LPR) data as effective characteristics. Subsequently, to utilize the recent data without retraining the model online, PURP uses the nonparametric method k-Nearest Neighbor (namely KNN) as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online. The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.https://www.sciopen.com/article/10.26599/BDMA.2023.9020017traffic flow predictionk-nearest neighbor (knn)license plate recognition (lpr) dataspatio-temporal context |
| spellingShingle | Shan Zhang Qinkai Jiang Hao Li Bin Cao Jing Fan PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data Big Data Mining and Analytics traffic flow prediction k-nearest neighbor (knn) license plate recognition (lpr) data spatio-temporal context |
| title | PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data |
| title_full | PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data |
| title_fullStr | PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data |
| title_full_unstemmed | PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data |
| title_short | PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data |
| title_sort | purp a scalable system for predicting short term urban trafficflow based on license plate recognition data |
| topic | traffic flow prediction k-nearest neighbor (knn) license plate recognition (lpr) data spatio-temporal context |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020017 |
| work_keys_str_mv | AT shanzhang purpascalablesystemforpredictingshorttermurbantrafficflowbasedonlicenseplaterecognitiondata AT qinkaijiang purpascalablesystemforpredictingshorttermurbantrafficflowbasedonlicenseplaterecognitiondata AT haoli purpascalablesystemforpredictingshorttermurbantrafficflowbasedonlicenseplaterecognitiondata AT bincao purpascalablesystemforpredictingshorttermurbantrafficflowbasedonlicenseplaterecognitiondata AT jingfan purpascalablesystemforpredictingshorttermurbantrafficflowbasedonlicenseplaterecognitiondata |