Liftago On-Demand Transport Dataset and Market Formation Algorithm Based on Machine Learning
This work addresses market formation for taxi services in Prague, but it is incremental as it builds on existing data and methods.
The authors tackled the problem of connecting customers with relevant drivers in on-demand transport by proposing the SIDMAF algorithm, which improved relevance compared to existing heuristics using two key performance indicators.
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.