Enabling Time-Dependent Uncertain Eco-Weights For Road Networks

Abstract

Reduction of greenhouse gas (GHG) emissions from transportation is an essential part of the efforts to prevent global warming and climate change. Eco-routing, which enables drivers to use the most environmentally friendly routes, is able to substantially reduce GHG emissions from vehicular transportation. The foundation of eco-routing is a weighted-graph representation of a road network in which road segments, or edges, are associated with eco-weights that capture the GHG emissions caused by traversing the edges. Due to the dynamics of traffic, the eco-weights are typically time dependent and uncertain.We formalize the problem of assigning a time-dependent, uncertain eco-weight to each edge in a road network. In particular, a sequence of histograms are employed to describe the uncertain eco-weight during different time intervals for each edge. Various compression techniques, including histogram merging and buckets reduction, are proposed to maintain compact histograms while achieving good accuracy. Histogram aggregation methods are proposed that use these to accurately estimate GHG emissions for routes. A comprehensive empirical study is conducted based on two years of GPS data from vehicles in order to gain insight into the effectiveness and efficiency of the proposed approach.