This document serves as a technical report for the analysis of on-demand transport dataset. Moreover we show how the dataset can be used to develop a market formation algorithm based on machine learning. Data used in this work comes from Liftago, a Prague based company which connects taxi drivers and customers through a smartphone app. The dataset is analysed from the machine-learning perspective: we give an overview of features available as well as results of feature ranking. Later we propose the SImple Data-driven MArket Formation (SIDMAF) algorithm which aims to improve a relevance while connecting customers with relevant drivers. We compare the heuristics currently used by Liftago with SIDMAF using two key performance indicators.
Cite
@article{arxiv.1608.02858,
title = {Liftago On-Demand Transport Dataset and Market Formation Algorithm Based on Machine Learning},
author = {Jan Mrkos and Jan Drchal and Malcolm Egan and Michal Jakob},
journal= {arXiv preprint arXiv:1608.02858},
year = {2016}
}
Comments
9 pages, 2 figures, supplemental information for a journal paper