@article{ref1,
title="Deep transfer learning-based anomaly detection for cycling safety",
journal="Journal of safety research",
year="2023",
author="Yaqoob, Shumayla and Cafiso, Salvatore and Morabito, Giacomo and Pappalardo, Giuseppina",
volume="87",
number="",
pages="122-131",
abstract="INTRODUCTION: Despite the general improvements in road safety, with the growing number of bicycle users, cycling safety is still a challenge as demonstrated by the fact that it is the only road transport mode with an increase in the number of fatalities in EU cities. PROBLEM: Moreover, to analyze the problem to improve the road transport system, the traditional network screening based on crash statistics is a reactive approach and less effective due to the lack of suitable bicycle data availability, as well. In such a framework, new opportunities for data collection in smart cities and communities are emerging as proactive approaches to identify critical locations where safety treatments can be effectively applied to prevent bicycle crashes. <br><br>METHOD: This research applied a deep transfer learning model to detect anomalies in cycling behavior that can be associated with traffic conflicts or near-miss crashes. <br><br>RESULTS: The paper presents how to build a users' tailored riding model named DTL AD to detect and localize riding anomalies by using a set of data in the National Marine Electronics Association (NMEA) string of Global Navigation Satellite System (GNSS) recorded with instrumented bicycles by different cyclists. <br><br>CONCLUSION: More specifically, DTL AD exploits a convolutional autoencoder (CAE) with transfer learning to reduce data labelling and training effort.   PRACTICAL APPLICATION: A case study demonstrates the identification of anomalies in cycling behavior visually represented on Geographic Information Systems (GIS) maps, showing how data clustering is well located in high-risk areas.<p /> <p>Language: en</p>",
language="en",
issn="0022-4375",
doi="10.1016/j.jsr.2023.09.010",
url="http://dx.doi.org/10.1016/j.jsr.2023.09.010"
}