Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1).
When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility.
We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:82520 |
| Date | 02 January 2023 |
| Creators | Yaqoob, Shumayla, Cafiso, Salvatore, Morabito, Giacomo, Pappalardo, Giuseppina |
| Publisher | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | urn:nbn:de:bsz:14-qucosa2-813602, qucosa:81360 |
Page generated in 0.0018 seconds