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

• URL: http://arxiv.org/abs/2603.14901v1
• Date: Mon, 16 Mar 2026 07:04:18 GMT
• Title: The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems
• Abstract summary: 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.<n>The forecasts are integrated into a simulation framework that is used to support long-term decisions.<n>The evaluation is based on real-world data of the bike sharing system currently operating in Brescia, Italy.
• Score: 0.5872014229110213
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Causal World Modeling for Robot Control [56.31803788587547]
  Video world models provide the ability to imagine the near future by understanding the causality between actions and visual dynamics.<n>We introduce LingBot-VA, an autoregressive diffusion framework that learns frame prediction and policy execution simultaneously.<n>We evaluate our model on both simulation benchmarks and real-world scenarios, where it shows significant promise in long-horizon manipulation, data efficiency in post-training, and strong generalizability to novel configurations.
  arXiv  Detail & Related papers  (2026-01-29T17:07:43Z)
• Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach [54.84957282120537]
  We present the first study to utilize a Graph Convolutional Network (GCN) architecture to model link-level bicycling volumes.<n>We benchmark it against traditional machine learning models, such as linear regression, support vector machines, and random forest.<n>Our results show that the GCN model outperforms these traditional models in predicting Annual Average Daily Bicycle (AADB) counts.
  arXiv  Detail & Related papers  (2024-10-11T04:53:18Z)
• Bike3S: A Tool for Bike Sharing Systems Simulation [2.1778360174438047]
  Bike3S is a simulator for a station-based bike sharing system. It performs semi-realistic simulations of the operation of a bike sharing system.
  arXiv  Detail & Related papers  (2024-01-24T17:33:40Z)
• Physics Informed Shallow Machine Learning for Wind Speed Prediction [66.05661813632568]
  We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in Italy. We train supervised learning algorithms using the past history of wind to predict its value at a future time. We find that the optimal design as well as its performance vary with the location.
  arXiv  Detail & Related papers  (2022-04-01T14:55:10Z)
• Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment [3.704832909610284]
  We study a dynamic traffic assignment model, where agents base their instantaneous routing decisions on real-time delay predictions. We formulate a mathematically concise model and derive properties of the predictors that ensure a dynamic prediction equilibrium exists.
  arXiv  Detail & Related papers  (2021-09-14T14:27:09Z)
• Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management [8.441020454345932]
  We introduce a variational Poisson recurrent neural network model (VP-RNN) to forecast future pickup and return rates. We empirically evaluate our approach against both traditional and learning-based forecasting methods on real trip travel data from the city of New York, USA.
  arXiv  Detail & Related papers  (2021-07-28T14:11:34Z)
• Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning [52.2663102239029]
  We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle on idle-hailing platforms. Our approach learns ride-based state-value function using a batch training algorithm with deep value. We benchmark our algorithm with baselines in a ride-hailing simulation environment to demonstrate its superiority in improving income efficiency.
  arXiv  Detail & Related papers  (2021-03-08T05:34:05Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)
• Network and Station-Level Bike-Sharing System Prediction: A San Francisco Bay Area Case Study [12.477331187546216]
  The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System. It applies machine learning two levels: network and station.
  arXiv  Detail & Related papers  (2020-09-20T06:46:41Z)
• Modeling bike counts in a bike-sharing system considering the effect of weather conditions [13.537141566893126]
  The paper develops a method that quantifies the effect of weather conditions on the prediction of bike station counts in the San Francisco Bay Area Bike Share System.
  arXiv  Detail & Related papers  (2020-06-13T05:32:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.