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

Shan Zhang; Qinkai Jiang; Hao Li; Bin Cao; Jing Fan

doi:10.26599/BDMA.2023.9020017

Big Data Mining and Analytics 2024, 7(1): 171-187 https://doi.org/10.26599/BDMA.2023.9020017

Open Access | Issue | Published: 25 December 2023

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

Show Author's Information Hide Author's Information Shan Zhang^¹, Qinkai Jiang^¹, Hao Li^¹, Bin Cao^¹, Jing Fan^¹(

)

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

Keywords:

traffic flow prediction, k-Nearest Neighbor (KNN), License Plate Recognition (LPR) data, spatio-temporal context

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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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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.

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

Show Author's information Hide Author's Information Shan Zhang^¹, Qinkai Jiang^¹, Hao Li^¹, Bin Cao^¹, Jing Fan^¹(

)

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

Abstract

Keywords: traffic flow prediction, k-Nearest Neighbor (KNN), License Plate Recognition (LPR) data, spatio-temporal context

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Publication history

Received: 27 November 2022

Revised: 15 June 2023

Accepted: 25 June 2023

Published: 25 December 2023

Issue date: March 2024

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (Nos. 62072405 and 62276233) and the Key Research Project of Zhejiang Province (No. 2023C01048).

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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/).