This paper deals with the problem of improving the existing optimization techniques for Data Envelopment Analysis (DEA). The algorithm proposed herein is a combination of the “quickhull algorithm” and a DEA algorithm written in Python programming language. To the best of the authors’ knowledge no prior effort has been made to date to propose a methodology for reducing the running time of a DEA problem that incorporates multiple inputs and outputs. The algorithmic implementation is applied on the existing problem of driving efficiency evaluation by exploiting a driving data sample of 10,088 trips collected from smartphone devices. Results indicate that the proposed algorithm is performing relatively well for Big Data compared to other existing DEA algorithmic methodologies that yield the same optimal solution such as Standard DEA and RBE DEA methodologies. The results obtained are calculated for the test sets of 100, 500, 1000, 5,000 and 10,088 Decision-Making-Units (DMUs) and compared in terms of running time of each of the algorithms applied. The results of per trip analysis can be exploited in order to classify trips into different efficiency categories (such as efficient, less efficient, non-efficient) and present their main characteristics.
|Tags||big data, driver behaviour, machine learning, naturalistic driving, statistical modelling, telematics|