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 best modeled as being time dependent and uncertain. We formalize the problem of assigning a time-dependent, uncertain eco-weight to each edge in a road network based on historical GPS records. In particular, a sequence of histograms is employed to describe the uncertain eco-weight of an edge at different time intervals. Compression techniques, including histogram merging and buckets reduction, are proposed to maintain compact histograms while retaining their accuracy. In addition, to better model real traffic conditions, virtual edges and extended virtual edges are proposed in order to represent adjacent edges with highly dependent travel costs. Based on the techniques above, different histogram aggregation methods are proposed to accurately estimate time-dependent GHG emissions for routes. Based on a 200-million GPS record data set collected from 150 vehicles in Denmark over two years, a comprehensive empirical study is conducted in order to gain insight into the effectiveness and efficiency of the proposed approach.