부산대학교 도서관

Overspeeding is a significant cause of road accidents, especially when the target vehicle is a two-wheeler. Coupled with infrastructural limitations and the general reckless driving behavior, it becomes challenging to reduce the problem of overspending, mainly because the optimal speed depends not only on road types but also on several spatiotemporal contexts. To mitigate this, in this paper, we propose Pathik, which uses multimodal contextual information to accurately predict the speeding behavior of a bike driver for the next road segment. Pathik then aggregates this information with the demographic and map-based information for the next road segment and recommends decelerating if the bike speed exceeds. Principled evaluation on an in-house dataset with different bike types (both geared and gearless) shows that Pathik can accurately predict the speed for the next patch with a mean $\mathrm{R}_{2} -$score of $0.92 (\pm 0.015)$.