Eco Routing

In Kooperation mit TomTom

Rückfragen zum Thema: Natalia.Kliewer@fu-berlin.de

With rising fuel prices and focus on minimizing the CO2 footprint more and more drivers are interested in optimizing their fuel consumption. Expected fuel consumption becomes another important property when choosing a route next to travel time, length, toll charges, etc. Hence, drivers demand navigation systems which

  • provide visibility on the expected fuel consumption and CO2 emissions for a given route and
  • offer environmentally friendly routes (eco routes) that minimize fuel consumption.

TomTom navigation devices can already calculate routes that represent a suitable trade-off between minimizing travel time and fuel consumption. Such routes are often similar to the fastest route but they also take additional properties of the route into account. For example, they avoid long detours that save little time or short-cuts with many turns and crossings that are likely to result in the need to brake and accelerate frequently.

However, TomTom navigation devices do not yet provide an estimate of the expected fuel consumption. A fuel consumption model that delivers such an estimate would benefit drivers twofold by increasing the awareness of expected fuel consumption and by forming the basis of a route calculation model. A route calculation model that directly optimizes expected fuel consumption is likely to result in routes that are more environmentally friendly than current models which indirectly achieve the same effect by avoiding long detours, frequent acceleration, etc.

TomTom navigation devices make use of data derived from anonymous position logs of millions of drivers world-wide, e.g, to avoid routes through areas that are typically congested at a given time. This data source provides rich information about the behavior of drivers in given parts of the road network and can also be used to estimate fuel consumption and to calculate environmentally friendly routes.

The goal of this project is to give answers to the following questions:

  • How much does the accuracy of the predicted fuel consumption improve when historic data on average driver behavior is taken into account?
  • Is the average predicted fuel consumption practically meaningful despite of significant stochastic influence by traffic etc. and systematic bias due to vehicle characteristics and driver behavior?
  • How much fuel could be saved by using routes that minimize the predicted fuel consumption?
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