The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems

TL;DR

This paper demonstrates how machine learning forecasts can improve strategic decision-making in bike sharing systems by accurately predicting station demand and integrating these predictions into simulation models for better operational planning.

Contribution

It introduces a novel approach combining machine learning forecasts with simulation models to enhance long-term strategic decisions in bike sharing systems.

Findings

ML forecasts outperform alternative methods in accuracy.

Forecast integration improves simulation decision quality.

Real-world data validates the approach.

Abstract

In this paper, machine learning techniques are used to forecast the difference between bike returns and withdrawals at each station of a bike sharing system. The forecasts are integrated into a simulation framework that is used to support long-term decisions and model the daily dynamics, including the relocation of bikes. We assess the quality of the machine learning-based forecasts in two ways. Firstly, we compare the forecasts with alternative prediction methods. Secondly, we analyze the impact of the forecasts on the quality of the output of the simulation framework. The evaluation is based on real-world data of the bike sharing system currently operating in Brescia, Italy.