With rapid urbanization and the proliferation of motor vehicles, traffic congestion in small towns featuring renowned scenic areas has escalated, posing challenges for roads that must accommodate both local commuters and tourist vehicles. Tourist vehicles frequently idling on roads for parking exacerbate traffic inefficiencies. To address this, this study innovatively excludes statutory holiday traffic (e.g., Tomb Sweeping Day, Labor Day) to focus on a targeted dataset from April 8th to April 21st, 2024. By integrating the K-means clustering algorithm, this research introduces a novel temporal segmentation approach to analyze traffic flow patterns. This method dynamically partitions time periods into clusters, estimates traffic volume variations across phases within each cluster, and reveals distinct temporal traffic profiles. Unlike traditional static analyses, this study provides granular, time-sensitive insights that enable precise traffic management strategies, such as adaptive signal control and parking optimization, to alleviate congestion around scenic spots in small towns. The findings offer a robust framework for addressing traffic challenges in tourism-dependent small towns, bridging gaps in existing literature on dynamic traffic flow modeling.

Kai Jiang, Yujie Long, Zejian Li, Zhilan Zhou, Junwei Liang. Time Period Analysis of Traffic Flow around Scenic Spots in Small Towns Based on K-Means Clustering. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 5: 79-85. https://doi.org/10.25236/AJCIS.2025.080509.

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