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Open Access

PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310000, China
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Abstract

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.

Keywords

traffic flow predictionk-Nearest Neighbor (KNN)License Plate Recognition (LPR) dataspatio-temporal context

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Big Data Mining and Analytics
Volume 7 Issue 1,
March 2024
Pages 171-187
DOI: 10.26599/BDMA.2023.9020017
Cite this article:
Zhang S, Jiang Q, Li H, et al. PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data. Big Data Mining and Analytics, 2024, 7(1): 171-187. https://doi.org/10.26599/BDMA.2023.9020017

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Received: 27 November 2022
Revised: 15 June 2023
Accepted: 25 June 2023
Published: 25 December 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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