Aggregated Time Series based Vehicular Traffic Path Recommendation

Periodic data related to vehicular traffic information have been flare-up and entered the era of big data. Vehicular traffic network is monitored continuously by motion detectors and video cameras. Advanced information about a travelling path is being used as an extraneous intervention tool to positively influence recommendation system performance. This situation directs us to think vehicular traffic path recommendation problem based on time series analysis. In this paper, a graph processing based vehicular traffic path recommendation method is proposed, which considers the spatial and temporal attributes. We cast a problem as an optimal path selection problem for the fixed origin and destination based on various data points acquired at a different time interval. Rigorous experimental evaluation on publicly available dataset shows the efficacy of the proposed method.