Evaluation of large-scale cycling environment by using the trajectory data of dockless shared bicycles: A data-driven approach

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Intelligent Transport Systems Pub Date : 2024-09-10 DOI:10.1049/itr2.12565
Ying Ni, Shihan Wang, Jiaqi Chen, Bufan Feng, Rongjie Yu, Yilin Cai
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Abstract

Cycling is increasingly promoted worldwide, but many urban areas lack satisfactory cycling environments. Assessing these environments is crucial, but existing methods face data challenges for large urban networks. This study proposes a data-driven framework using dockless shared bicycle data to efficiently evaluate large-scale cycling environments. First, critical cycling behaviour features that reflect cyclists’ perceptions are identified applying the fuzzy C-means and random forest model. Then, a distribution-oriented evaluation method is developed, ensuring the incorporation of cyclist heterogeneity and quantifying the quality differences among road segments by combining statistical analysis with a hierarchical clustering model. The evaluation framework is applied to Yangpu District, Shanghai, using Mobike data covering 114.9 km of cycling roads. Results show that indicators related to speed magnitude and fluctuation are critical, and an experimental study validates the effectiveness of the data-driven feature extraction method. A minimum trajectory sample size of 260 is required to account for cyclist heterogeneity for one road segment to be evaluated. Further analysis of lower-performing segments identifies vehicle-bicycle separation, on-street parking, and traffic volume as key influencing factors. The rationality of these findings further supports the reliability of the evaluation framework.

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利用无桩共享单车的轨迹数据评估大规模骑行环境:数据驱动法
自行车运动在全球范围内日益得到推广,但许多城市地区缺乏令人满意的自行车运动环境。评估这些环境至关重要,但现有方法在大型城市网络中面临数据挑战。本研究提出了一个数据驱动框架,利用无桩共享单车数据有效评估大规模骑行环境。首先,利用模糊 C-means 和随机森林模型识别出反映骑车人感知的关键骑车行为特征。然后,开发了一种以分布为导向的评估方法,通过将统计分析与分层聚类模型相结合,确保纳入骑车人的异质性并量化不同路段的质量差异。评价框架应用于上海市杨浦区,使用摩拜单车数据,覆盖 114.9 公里的骑行道路。结果表明,与速度大小和波动相关的指标至关重要,实验研究验证了数据驱动特征提取方法的有效性。考虑到一个待评估路段的骑车人异质性,至少需要 260 个轨迹样本。对表现较差的路段进行进一步分析后发现,车辆与自行车分离、路边停车和交通流量是关键的影响因素。这些发现的合理性进一步证明了评估框架的可靠性。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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