Cycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis. This research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach “BeST-DAD” to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA). BeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user’s trained models. The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior.

中文翻译：

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使用卷积神经网络和深度学习检测骑行行为异常

骑自行车一直被认为是一种可持续和健康的交通方式。随着人们对温室气体和污染的日益关注，政策制定者打算支持骑自行车作为通勤交通方式。此外，在 Covid-19 期间，骑自行车作为个人出行的机会而受到市民的进一步赞赏。不幸的是，随着 21 世纪越来越多的骑车人，骑车人的安全已成为一项挑战。与传统的道路安全网络筛查相比，针对自行车事故获取合适的数据更加困难。在这样的框架下，基于新技术的智慧城市可能需要新的数据收集和分析机会。本研究提出了自行车数据要求和处理方法，以便通过使用 GPS 设备获取合适的信息。主要是 本文提出了一种基于深度学习的方法“BeST-DAD”来检测异常并在地图上发现危险点，以便骑车人避免发生关键安全事件 (CSE)。BeST-DAD 遵循卷积神经网络和自动编码器 (AE) 进行异常检测。通过测试不同的数据特​​征和 BeST-DAD 参数设置来进行所提出的模型优化，同时在 BeST-DAD 和主成分分析 (PCA) 之间进行另一个性能比较。BeST-DAD 通过实现 77% 的 F 分数，在异常检测方面优于传统的 PCA 统计方法。当使用来自不同用户的数据测试经过训练的模型时，会记录单个用户经过训练的模型的 100% 召回率。