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

Enrico Angelelli, Andrea Mor, Carlotta Orsenigo, M. Grazia Speranza, Carlo Vercellis

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.

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

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.
Paper Structure (14 sections, 1 equation, 17 figures, 6 tables)

This paper contains 14 sections, 1 equation, 17 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Forecasting for BSSs.
  4. Forecasting and relocation.
  5. Forecasting approaches and models
  6. Simulation framework
  7. A case study: Bicimia
  8. The data
  9. The current forecasting model
  10. Results
  11. Forecasting
  12. Feature importance
  13. Simulation
  14. Conclusions

Figures (17)

  • Figure 1: The structure of the modified simulation framework.
  • Figure 2: An example of the data defining an instance.
  • Figure 3: The geographical layout and elevation of the stations of the Bicimia service.
  • Figure 4: The monthly number of trips by year.
  • Figure 5: The average number of trips for each half-hour interval by day of the week.
  • ...and 12 more figures