Taximeter verification with GPS and soft computing techniques

Abstract

Until recently, local governments in Spain were using machines with rolling cylinders for verifying taximeters. However, the condition of the tires can lead to errors in the process and the mechanical construction of the test equipment is not compatible with certain vehicles. Thus, a new measurement device needs to be designed. In our opinion, the verification of a taximeter will not be reliable unless measurements taken on an actual taxi run are used. GPS sensors are intuitively well suited for this process, because they provide the position and the speed with independence of those car devices that are under test. But there are legal problems that make difficult the use of GPSbased sensors: GPS coordinate measurements do not match exactly real coordinates and, generally speaking, we are not given absolute tolerances. We can not know whether the maximum error is always lower than, for example, 7 m. However, we might know that 50% of the measurements lie on a circle with a radius of 7 m, centered on the real position. In this paper we describe a practical application where these legal problems have been solved with soft computing based technologies. In particular, we propose to characterize the uncertainty in the GPS with fuzzy techniques, so that we can reuse certain recent algorithms, formerly intended for being used in genetic fuzzy systems, to this new context. Specifically, we propose a new method for computing an upper bound of the length of the trajectory, taking into account the vagueness of the GPS data. This bound will be computed using a modified multiobjective evolutionary algorithm, which can optimize a fuzzy valued function. The accuracy of the measurements will be improved further by combining it with restrictions based on the dynamic behavior of the vehicles.

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

“…SCADA provides the minimum and maximum values of a signal in a certain interval. Strictly, despite being able to measure a magnitude, its exact value will be never known; it is only known that the measurement is somewhere in a certain range, bounded by the precision of the measurement instrument or measurement procedure itself [221]. In applications with these kinds of inputs, it makes sense to include the uncertainty associated to the measure itself in the representation of the values.…”

“…This process is particularly natural when data coming from certain technological devices provide uncertain punctual measurements together with some evaluation of the uncertainty itself, like variability, error bounds, occurrence regions, and so on. Radar data, GPS data or SCADA data are particularly suitable for this kind of treatment [221].…”

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“…The ideas and principles previously shown have been used in several applications with low quality data, with both realistic and real world data sets. [14,15,19].…”