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22 May 2023 Spatio-temporal characteristics of highway toll stations based on K-means++
Jiahao Zhan, Shengwen Yang, Xueyin Wang, Jun Li, Fuze Chen
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Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 1264015 (2023) https://doi.org/10.1117/12.2673518
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China
Abstract
The spatial and temporal characteristics of regional traffic flows and their socio-economic significance are investigated by using machine learning and ArcGIS technology with multiple attributes of highway toll station data. The study found that: (1) the overall characteristics of the traffic flow at 10~11, 14~16 and 17~19 hours show a "three-peak" structure, while the spatial distribution of high, medium and low traffic types has obvious clustering characteristics. (2) The specific features of the K-means++ based highway toll station classification are "point-line surface" structure in space, with Kunming West Toll Station, Dujiaying Toll Station, Kunming North Toll Station and Liangmiansi Toll Station as unique Node, Kunchu line and other toll stations along the axis, the rest of the toll stations constitute the surface; time, each type of toll station traffic flow also shows the "three peaks" structure, but there are "peak" and "sub-peak The "peak" and "sub-peak" are divided. (3) Based on ArcGIS technology, the dynamic visualization spatial expression of traffic flow "three peaks" separated by time series reflects the distinctive "day-night" pattern of human travel activities across regions.
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Jiahao Zhan, Shengwen Yang, Xueyin Wang, Jun Li, and Fuze Chen "Spatio-temporal characteristics of highway toll stations based on K-means++", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 1264015 (22 May 2023); https://doi.org/10.1117/12.2673518
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KEYWORDS
Machine learning
Transportation
Reflection
Error analysis
Data communications
Roads
Visualization
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