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Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data

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  • Kyu Soo Chong

    (Korea Institute of Civil Engineering and Building Technology, 283 Goyang-daero, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Republic of Korea)

Abstract

Precise and detailed speed information is indispensable for ensuring safe and efficient transportation. This is particularly true within unstable flow (UF) segments, which are especially prone to accidents due to the significant speed variations between vehicles and across lanes, and in the context of evolving transportation systems, where autonomous and non-autonomous vehicles are increasingly mixing. To address the limitations of existing methods in providing such data, this study aims to improve the detail, accuracy, and granularity of road information for micro-segments by leveraging individual vehicle big data. The proposed approach utilizes the geohash algorithm for spatial segmentation and introduces a novel criterion for identifying UF segments based on the relationship between space mean speed (SMS) and time mean speed (TMS). The presented strategy was validated through a comprehensive analysis of DTG (Digital Tachograph) data from freight vehicles on Expressway No. 50 in the Gyeonggi region in the Republic of Korea. As a result, a total of 301 segments were identified, including 178 eastbound and 123 westbound segments. UF segments corresponded to partitions falling beyond the reference standard deviation range. Compared with VDS (Vehicle Detection System) and conzone speeds, the proposed method provided more precise and continuous speed information, surpassing those obtained from conventional link-based approaches.

Suggested Citation

  • Kyu Soo Chong, 2023. "Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data," Sustainability, MDPI, vol. 15(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14684-:d:1256790
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    References listed on IDEAS

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    1. Yang Liu & Xuedong Yan & Yun Wang & Zhuo Yang & Jiawei Wu, 2017. "Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data," Sustainability, MDPI, vol. 9(4), pages 1-15, March.
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