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Categorizing Bicycling Environment Quality Based on Mobile Sensor Data and Bicycle Flow Data

Author

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  • Yang Bian

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Ling Li

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Huan Zhang

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Dandan Xu

    (Beijing Municipal Institute of City Planning and Design, Beijing 100045, China
    School of Science, Beijing Jiaotong University, Beijing 100044, China)

  • Jian Rong

    (Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Jiachuan Wang

    (Beijing Transportation Information Center, Beijing 100073, China)

Abstract

The bicycle is a healthy and sustainable transport mode due to its emission-free characteristics. To increase bicycle use, it is fundamental to provide bicycle-friendly environments. To better monitor bicycle environments, this study proposed the concept of bicycling environment quality (BEQ), which was defined by perceived satisfaction and conflict level. Data collection was conducted at 19 road segments in five sites located in Beijing, China. Then, speed-related and acceleration-related bicycling behavior indicators (BBIs) were extracted from data collected using sensors on mobile phones, while bicycling environment indicators (BEIs), such as bicycle flow, were extracted from recorded data. Taking the BBIs and BEIs as input attributes, a two-level BEQ classification assessment model based on a random forest (RF) algorithm was constructed. The proposed RF-based classification assessment model was able to produce approximately 77.35% overall correct classification. The results demonstrate the feasibility of using GPS data in evaluating BEQ. In addition, a novel dockless bicycle-sharing system (DBS)-based framework for bicycle traffic monitoring is discussed, which is of great significance in the sustainable development of bicycles. This study provides a theoretical method for objective BEQ assessment. It can further be used by planners and road administrators to monitor and improve BEQ and by individual cyclists for optimal route choice.

Suggested Citation

  • Yang Bian & Ling Li & Huan Zhang & Dandan Xu & Jian Rong & Jiachuan Wang, 2021. "Categorizing Bicycling Environment Quality Based on Mobile Sensor Data and Bicycle Flow Data," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4085-:d:531241
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    References listed on IDEAS

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