Facilitating Eco-Routing via Spatial Big Data: A Case-Study on Temporally-Detailed Roadmaps

Routing and navigation services are a set of ideas and technologies that transform lives by understanding the physical world, knowing and communicating relations to places in that world, and navigating through those places. From Google Maps [1] to consumer Global Positioning System (GPS) devices, society is benefiting immensely from routing services. Scientists use GPS to track endangered species to better understand animal behavior, and farmers use GPS for precision agriculture to increase crop yields while reducing costs. We’ve reached the point where a hiker in Yellowstone, a biker in Minneapolis, and a taxi driver in Manhattan know precisely where they are, their nearby points of interest, and how to reach their destinations. Increasingly, however, the size, variety, and update rate of spatial datasets exceed the capacity of commonly used spatial computing and database technologies to learn, manage, and process the data with reasonable effort. We believe that this data, which we call Spatial Big Data (SBD), represents the next frontier in routing services. Examples of emerging SBD datasets include temporally detailed (TD) roadmaps that provide speeds every minute for every road-segment, GPS track data from cell-phones, and engine measurements of fuel consumption, greenhouse gas (GHG) emissions, etc. Harnessing SBD has transformative potential. For example, a 2011 McKinsey Global Institute report estimates savings of “about $600 billion annually by 2020” in terms of fuel and time saved [2] by helping vehicles avoid congestion and reduce idling at red lights or left turns. Preliminary evidence for the transformative potential includes the experience of UPS, which saves millions of gallons of fuel by simply avoiding left turns (Figure 1(a)) and associated engine-idling when selecting routes [3]. Immense savings in fuel-cost and GHG emission are possible if other fleet owners and consumers avoided hot spots of idling, low fuel-efficiency, and congestion. In this chapter we discuss ideas likely to facilitate ‘eco-routing’ to help identify routes which reduce fuel consumption and GHG emissions, as compared to traditional route services reducing distance traveled or travel-time. It has the potential to significantly reduce US consumption of petroleum, the dominant source of energy for transportation (Figure 1(b)). It may even reduce the gap between domestic petroleum consumption and production (Figure 1(c)), helping bring the nation closer to the goal of energy independence [4]. However, SBD raises new challenges for the state of the art in spatial computing for routing services. First, it requires a change in frame of reference, from a global snapshot perspective to the perspective of the individual object traveling through a road network. Second, SBD increases the impact of the partial nature of traditional route query specification. It significantly increases computation cost due to the tremendous growth in the set of preference functions beyond travel-distance and travel-time to include fuel consumption, GHG emissions, travel-times for thousands of possible start-times, etc. Third, the growing diversity of SBD sources makes it less likely that single algorithms, working on specific spatial datasets, will be sufficient to discover answers appropriate for all situations. The rest of this chapter is organized as follows: Section 2 discusses traditional routing services. Section 3