Liftago On-Demand Transport Dataset and Market Formation Algorithm Based on Machine Learning

TL;DR

This paper analyzes Liftago's on-demand transport dataset and introduces SIDMAF, a machine learning-based market formation algorithm that improves customer-driver matching relevance.

Contribution

The paper presents a new data-driven market formation algorithm, SIDMAF, utilizing machine learning to enhance matching relevance in on-demand transport services.

Findings

SIDMAF outperforms existing heuristics in relevance metrics

Feature ranking identifies key factors influencing matching quality

Dataset analysis provides insights into on-demand transport dynamics

Abstract

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.