Abstract
In Mexico, car accidents are the leading cause of death among young people. Thus, the identification of drivers that can be potentially involved in car accidents is of particular interest. There are certain risky driving behaviors that are highly correlated to car accidents, including speeding, overtaking, and tailgating. In this work, we present a preliminary approach for automated detection of risky driving in urban environments. The system, Tracko, makes use of GPS data to compute mobility traces, which are used to preliminarily characterize driving behaviors. This work presents the design of the system as well as preliminary data to be used for automated identification of risky driving behaviors.
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© 2013 Springer International Publishing Switzerland
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Cruz, L.C., Macías, A., Domitsu, M., Castro, L.A., Rodríguez, LF. (2013). Risky Driving Detection through Urban Mobility Traces: A Preliminary Approach. In: Urzaiz, G., Ochoa, S.F., Bravo, J., Chen, L.L., Oliveira, J. (eds) Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction. Lecture Notes in Computer Science, vol 8276. Springer, Cham. https://doi.org/10.1007/978-3-319-03176-7_51
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DOI: https://doi.org/10.1007/978-3-319-03176-7_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03175-0
Online ISBN: 978-3-319-03176-7
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