Data-driven approaches for emissions-minimized paths in urban areas
We present a new way of constructing expected emissions-minimized paths in urban areas. We discuss the usage of sampling to determine expected emissions-minimized paths. We compare the sampling-based method to a deterministic path generation method. We discuss how to overcome technical challenges of using large data sets in shortest path construction. We analyze the performance and the quality of the proposed methods based on 230 million real speed observations.
emissions stochastic shortest path green logistics floating car data big data