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MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models

Antonios Tziorvas, George S. Theodoropoulos, Yannis Theodoridis

TL;DR

This paper proposes two gradient boosting model variations, one for classiffication and one for regression, both capable of generating demand forecasts at various temporal horizons, from 5 minutes up to one hour, enabling accurate predictions that are essential for improving the efficiency of shared (micro-) mobility services.

Abstract

Urban demand forecasting plays a critical role in optimizing routing, dispatching, and congestion management within Intelligent Transportation Systems. By leveraging data fusion and analytics techniques, traffic demand forecasting serves as a key intermediate measure for identifying emerging spatial and temporal demand patterns. In this paper, we tackle this challenge by proposing two gradient boosting model variations, one for classiffication and one for regression, both capable of generating demand forecasts at various temporal horizons, from 5 minutes up to one hour. Our overall approach effectively integrates temporal and contextual features, enabling accurate predictions that are essential for improving the efficiency of shared (micro-) mobility services. To evaluate its effectiveness, we utilize open shared mobility data derived from e-scooter and e-bike networks in five metropolitan areas. These real-world datasets allow us to compare our approach with state-of-the-art methods as well as a Generative AI-based model, demonstrating its effectiveness in capturing the complexities of modern urban mobility. Ultimately, our methodology offers novel insights on urban micro-mobility management, helping to tackle the challenges arising from rapid urbanization and thus, contributing to more sustainable, efficient, and livable cities.

MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models

TL;DR

This paper proposes two gradient boosting model variations, one for classiffication and one for regression, both capable of generating demand forecasts at various temporal horizons, from 5 minutes up to one hour, enabling accurate predictions that are essential for improving the efficiency of shared (micro-) mobility services.

Abstract

Urban demand forecasting plays a critical role in optimizing routing, dispatching, and congestion management within Intelligent Transportation Systems. By leveraging data fusion and analytics techniques, traffic demand forecasting serves as a key intermediate measure for identifying emerging spatial and temporal demand patterns. In this paper, we tackle this challenge by proposing two gradient boosting model variations, one for classiffication and one for regression, both capable of generating demand forecasts at various temporal horizons, from 5 minutes up to one hour. Our overall approach effectively integrates temporal and contextual features, enabling accurate predictions that are essential for improving the efficiency of shared (micro-) mobility services. To evaluate its effectiveness, we utilize open shared mobility data derived from e-scooter and e-bike networks in five metropolitan areas. These real-world datasets allow us to compare our approach with state-of-the-art methods as well as a Generative AI-based model, demonstrating its effectiveness in capturing the complexities of modern urban mobility. Ultimately, our methodology offers novel insights on urban micro-mobility management, helping to tackle the challenges arising from rapid urbanization and thus, contributing to more sustainable, efficient, and livable cities.
Paper Structure (26 sections, 10 equations, 7 figures, 8 tables)

This paper contains 26 sections, 10 equations, 7 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. MoDE-Boost: a Gradient Boosting-based, Edge-ready Framework for Shared Micro-mobility Demand Forecasting
  4. Problem Definition
  5. Prerequisites
  6. Forecasting task
  7. The MoDE-Boost Framework Pipeline
  8. Feature Extraction
  9. Temporal Features
  10. Lagged features
  11. Rolling features
  12. Exponentially Weighted features
  13. Statistical features
  14. Fourier‑based (spectral) features
  15. Contextual Features
  16. ...and 11 more sections

Figures (7)

  • Figure 1: Impact of forecast‑driven fleet rebalancing in Rotterdam. Left panel: pre‑rebalancing fleet with red circles (demand $>$ supply) and blue circles (demand $<$ supply) indicating shortages and surpluses, respectively. Right panel: post‑rebalancing after moving scooters (orange curved arrows) – shortage areas turn light orange, surplus circle fade to teal
  • Figure 2: Two indicative timeseries for two regions of Rotterdam: Overschie (top) and Rotterdam Centrum (bottom). The x-axis denotes time, while the y-axis indicates the measured demand values in number of shared vehicles.
  • Figure 3: Daily demand across different districts in the Rotterdam dataset. Each row represents a district, and each column corresponds to a day in the observation period. Color intensity reflects the total daily demand (aggregated from minute-level data), with warmer colors indicating higher activity.
  • Figure 4: Average hourly demand across the week in Rotterdam Centrum. The coloring indicates the relative demand intensity and, along with the peaks, aids in highlighting daily and weekly usage patterns.
  • Figure 5: Temporal evolution of the shared mobility demand (in hourly basis) in Rotterdam Centrum. Seasons are color-coded to highlight the different behaviors. During fall and winter seasons, demand patterns are more stable, whereas during spring and summer seasons, short-term spikes are observed often at “random” periods.
  • ...and 2 more figures