Abstract
Urban mobility studies are critical to acquiring insights about traffic management, urban mobility evolution, planning of urban spaces, and many other applications. However, the study and mitigation of recurrent problems have been postponed in real cases, given the difficulty of gathering mobility data. This paper presents a method to detect potential hazard driving zones. We mapped bus GPS positions into road segments, and we used bus speed, bus maximum allowed speed, and bus acceleration to classify the driving behavior. We develop an interactive web application that maps tracking data and provides rich visual insights on potentially problematic areas. Side-by-side visualizations help with the comparison of the traffic behavior in selected periods. We show that we can map the most problematic zones in the city and the time of the day. From the developed analysis, it is observed that some roads in the city present a daily seasonality (problems occur in the same period of the day); however, other roads present circulation issues independently of the time period or day.
This work is supported by FEDER, through POR LISBOA 2020 and COMPETE 2020 of the Portugal 2020 Project CityCatalyst POCI-01-0247-FEDER-046119, and by the Urban Innovation Action EU/H2020 Aveiro STEAM City. Ana Almeida acknowledges the Doctoral Grant from Fundação para a Ciência e Tecnologia, with reference 2021.06222.BD. Susana Brás is funded by national funds, European Regional Development Fund, FSE, through COMPETE2020 and FCT, in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. Ilídio Oliveira is funded by National Funds through the FCT - Foundation for Science and Technology, in the context of the project UIDB/00127/2020.
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Almeida, A., Brás, S., Sargento, S., Oliveira, I. (2022). Using Bus Tracking Data to Detect Potential Hazard Driving Zones. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_53
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