Optimizing route choice for lowest fuel consumption – Potential effects of a new driver support tool

Today, driver support tools intended to increase traffic safety, provide the driver with convenient information and guidance, or save time are becoming more common. However, few systems have the primary aim of reducing the environmental effects of driving. The aim of this project was to estimate the potential for reducing fuel consumption and thus the emission of CO₂ through a navigation system where optimization of route choice is based on the lowest total fuel consumption (instead of the traditional shortest time or distance), further the supplementary effect if such navigation support could take into account real-time information about traffic disturbance events from probe vehicles running in the street network. The analysis was based on a large database of real traffic driving patterns connected to the street network in the city of Lund, Sweden. Based on 15 437 cases, the fuel consumption factor for 22 street classes, at peak and off-peak hours, was estimated for three types of cars using two mechanistic emission models. Each segment in the street network was, on a digitized map, attributed an average fuel consumption for peak and off-peak hours based on its street class and traffic flow conditions. To evaluate the potential of a fuel-saving navigation system the routes of 109 real journeys longer than 5 min were extracted from the database. Using Esri’s external program ArcGIS, Arcview and the external module Network Analysis, the most fuel-economic route was extracted and compared with the original route, as well as routes extracted from criterions concerning shortest time and shortest distance. The potential for further benefit when the system employed real-time data concerning the traffic situation through 120 virtual probe vehicles running in the street network was also examined. It was found that for 46% of trips in Lund the drivers spontaneous choice of route was not the most fuel-efficient. These trips could save, on average, 8.2% fuel by using a fuel-optimized navigation system. This corresponds to a 4% fuel reduction for all journeys in Lund. Concerning the potential for real-time information from probe vehicles, it was found that the frequency of disturbed segments in Lund was very low, and thus so was the potential fuel-saving. However, a methodology is presented that structures the steps required in analyzing such a system. It is concluded that real-time traffic information has the potential for fuel-saving in more congested areas if a sufficiently large proportion of the disturbance events can be identified and reported in real-time.