Longest path estimation from inherently fuzzy data acquired with GPS using genetic algorithms

Abstract

Measuring the length of a path that a taxi must fare for is not an obvious task. When driving lower than certain threshold the fare is time dependent, but at higher speeds the length of the path is measured, and the fare depends on such measure. When passing an indoor MOT test, the taximeter is calibrated simulating a cab run, while the taxi is placed on a device equipped with four rotating steel cylinders in touch with the drive wheels. This indoor measure might be inaccurate, as information given by the cylinders is affected by tires inflating pressure, and only straight trajectories are tested. Moreover, modern vehicles with driving aids such as ABS, ESP or TCS might have their electronics damaged in the test, since two wheels are spinning while the others are not. To overcome these problems, we have designed a small, portable GPS sensor that periodically logs the coordinates of the vehicle and computes the length of a discretionary circuit. We show that all the legal issues with the tolerance of such a procedure (GPS data are inherently imprecise) can be overcome if genetic and fuzzy techniques are used to preprocess and analyze the raw data