  The advent of abundant on-board sensors and electronic devices in vehicles
populates the paradigm of participatory sensing to harness crowd-sourced data
gathering for intelligent transportation applications, such as
distance-to-empty prediction and eco-routing. While participatory sensing can
provide diverse driving data, there lacks a systematic study of effective
utilization of the data for personalized prediction. There are considerable
challenges on how to interpolate the missing data from a sparse dataset, which
often arises from participatory sensing. This paper presents and compares
various approaches for personalized vehicle energy consumption prediction,
including a blackbox framework that identifies
driver/vehicle/environment-dependent factors and a collaborative filtering
approach based on matrix factorization. Furthermore, a case study of
distance-to-empty prediction for electric vehicles by participatory sensing
data is conducted and evaluated empirically, which shows that our approaches
can significantly improve the prediction accuracy.
