  Falling oil revenues and rapid urbanization are putting a strain on the
budgets of oil producing nations which often subsidize domestic fuel
consumption. A direct way to decrease the impact of subsidies is to reduce fuel
consumption by reducing congestion and car trips. While fuel consumption models
have started to incorporate data sources from ubiquitous sensing devices, the
opportunity is to develop comprehensive models at urban scale leveraging
sources such as Global Positioning System (GPS) data and Call Detail Records.
We combine these big data sets in a novel method to model fuel consumption
within a city and estimate how it may change due to different scenarios. To do
so we calibrate a fuel consumption model for use on any car fleet fuel economy
distribution and apply it in Riyadh, Saudi Arabia. The model proposed, based on
speed profiles, is then used to test the effects on fuel consumption of
reducing flow, both randomly and by targeting the most fuel inefficient trips
in the city. The estimates considerably improve baseline methods based on
average speeds, showing the benefits of the information added by the GPS data
fusion. The presented method can be adapted to also measure emissions. The
results constitute a clear application of data analysis tools to help decision
makers compare policies aimed at achieving economic and environmental goals.
