Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Abstract

Background 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. Methods 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). Result 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. Conclusion The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior.

Bicycle free-flow speed estimation based on GPS data – comparison of bikesharing system and Strava data

“…In recent years, bicycle speed was also estimated using GPS data. Bicycle GPS data from units installed on bikes are generally obtained from a limited number of cyclists (a few or a dozen) (El-Geneidy, Krizek and Iacono, 2007; Parkin and Rotheram, 2010; Cubells, Miralles-Guasch and Marquet, 2023; Yaqoob et al, 2023). Sample size can be significantly increased by using GPS data from the mobile app.…”

“…The most popular method of GPS data filtering is a Kalman filter (Kalman, 1960). The description and implementation of other signal filtering approaches in bicycle safety analysis can be found in (Murgano et al, 2021) and (Yaqoob et al, 2023). Irrespective of the source of bicycle GPS data, when estimating bicycle speed relationship between the speed of all cyclists and the group of cyclists from whom GPS data was collected has to be determined.…”