Scholarlink

<p>Traffic congestion has become a major issue in growing Smart Cities. Bike-sharing systems offer an eco-friendly solution to reduce congestion, but predicting bike demand accurately is still a difficult task because it depends on many changing factors like weather, season, and time of day. Many existing models either use all features without checking their importance or fail to adapt to real-time data, leading to less reliable predictions. To overcome these challenges, this study introduces a hybrid Grey Wolf Optimization-based Incremental Extreme Learning Machine (GWO–IELM) model for predicting bike-sharing demand. In this framework, GWO selects the most important features to reduce unnecessary data and computation, while IELM makes fast and accurate predictions by learning incrementally. The model is evaluated on the Kaggle London bike-sharing dataset, which includes over 17,000&#xa0;hly records with meteorological, temporal, and holiday-related features. The experimental results show that GWO-IELM consistently outperforms standard models including Linear Regression, Support Vector Regressor, AdaBoost Regressor, and Bagging Regressor with an R<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({^{2}}\)</EquationSource> </InlineEquation> of 0.9897, MAE of 0.0859, RMSE of 0.1547, and RMSLE of 0.0431. The robustness is further validated using the Diebold-Mariano test. Further, the interpretability of model is supported through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), providing insights into the contribution of selected features. These results demonstrate the potential of integrating optimization, lightweight learning, and explainability for improving demand prediction in bike-sharing systems and supporting smarter urban mobility strategies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Towards smarter mobility: a Grey Wolf Optimization-based incremental extreme learning machine framework for explainable bike-sharing demand forecasting

  • 期刊论文

  • 发表日期：

    2025年12月22日

查看全文
  • Ravneet Kaur,
  • Rajendra Kumar Roul,
  • Shalini Batra

摘要

Traffic congestion has become a major issue in growing Smart Cities. Bike-sharing systems offer an eco-friendly solution to reduce congestion, but predicting bike demand accurately is still a difficult task because it depends on many changing factors like weather, season, and time of day. Many existing models either use all features without checking their importance or fail to adapt to real-time data, leading to less reliable predictions. To overcome these challenges, this study introduces a hybrid Grey Wolf Optimization-based Incremental Extreme Learning Machine (GWO–IELM) model for predicting bike-sharing demand. In this framework, GWO selects the most important features to reduce unnecessary data and computation, while IELM makes fast and accurate predictions by learning incrementally. The model is evaluated on the Kaggle London bike-sharing dataset, which includes over 17,000 hly records with meteorological, temporal, and holiday-related features. The experimental results show that GWO-IELM consistently outperforms standard models including Linear Regression, Support Vector Regressor, AdaBoost Regressor, and Bagging Regressor with an R \({^{2}}\) of 0.9897, MAE of 0.0859, RMSE of 0.1547, and RMSLE of 0.0431. The robustness is further validated using the Diebold-Mariano test. Further, the interpretability of model is supported through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), providing insights into the contribution of selected features. These results demonstrate the potential of integrating optimization, lightweight learning, and explainability for improving demand prediction in bike-sharing systems and supporting smarter urban mobility strategies.