Oct 2026· Journal of Transportation Engineering Part A Systems· 0 citations· 33 references
TL;DR
A spatial proximity weighted eXtreme gradient boosting (XGBoost) ensemble to predict daily metro ridership under holidays, weekdays, and weekends is proposed, providing strong generalizability and practical value for urban transit planning and demand forecasting.
Abstract
Accurate metro ridership prediction is crucial for transit planning, particularly as urban rail networks expand and travel patterns vary across different travel scenarios. Existing models often rely on historical ridership data and may have limited adaptability to temporal and spatial heterogeneity. This study proposes a spatial proximity weighted eXtreme gradient boosting (XGBoost) ensemble to predict daily metro ridership under holidays, weekdays, and weekends. By integrating built environment variables and spatial adjacency, the proposed framework constructs local subdata sets using a nearest-neighbor approach and independently trains submodels. The model reduces dependence on continuous historical ridership sequences and is suitable for limited-data conditions. Using urban rail transit (URT) data from Nanjing, the proposed framework is compared with multiscale geographically weighted regression (MGWR) and global XGBoost. The results show that it achieves higher prediction accuracy in terms of
R
2
, mean absolute error (MAE), and root mean squared error (RMSE). Feature importance analysis confirms the model’s interpretability, revealing temporal differences in the effects of variables. The proposed method effectively captures spatiotemporal heterogeneity in ridership dynamics, providing strong generalizability and practical value for urban transit planning and demand forecasting.
An integrated prediction-and-visualisation pipeline that transforms complex data distributions into actionable visual analytics, such as interpretable station-to-station demand heatmaps via interactive GIS Folium layers is implemented, providing an operationally robust framework to support smart-city transportation management and build more sustainable urban transit systems.
Berna Çalışkan· Journal of Data Analytics an...· 0 citations
A multi-source machine learning framework for segment-direction-level prediction in the Denizli city center showed that temporal and traffic-state variables dominate predictions, while weather and public transport provide complementary value.
Muhammed Enes Karaoğlan, Yetis Sazi Murat· Sustainability· 0 citations
This study investigates the integration of temporal and spatial modeling techniques to improve sales forecast accuracy for commodities influenced by seasonal, climatic, and geographic factors. In competitive markets, businesses dealing with cold beverages, frozen treats, air conditioners, and outdoor goods require precise forecasts to optimize sales strategies. A temporal model captures seasonality and time-based dependencies, while a spatial model accounts for geographic relationships among nearby stations. These components are fused using a linear regression framework to construct a robust spatio-temporal model. Model performance is evaluated using the Mean Squared Error (MSE), showing that the integrated model consistently achieves lower errors than its individual components. To assess robustness, the proposed framework is benchmarked against advanced machine learning algorithms—Random Forest, XGBoost, and LightGBM. Comparative results and relative efficiency rankings demonstrate that the spatio-temporal hybrid performs competitively, often attaining the lowest prediction errors across multiple locations. The findings confirm the value of combining spatial–temporal dependencies with modern learning approaches for accurate sales forecasting, offering actionable insights for data-driven decision-making, inventory control, and resource allocation in dynamic, climate-sensitive markets.
S. Zeeshan, Nitij Taneja, Feeroz Babu· Journal of Decision Making a...· 0 citations
This study proposes an explainable spatio-temporal machine learning framework that integrates periodic temporal features with graph-based spatial representations and highlights a critical distinction between feature importance and actual predictive utility, referred to in this study as misleading importance.
Accurate regional demand forecasting supports reliable operation of urban electric vehicle (EV) charging networks. However, public charging demand exhibits spatial heterogeneity, multi-scale periodicity, and uncertainty. This paper proposes Urban-CSTPNet, a multi-relational spatio-temporal probabilistic forecasting framework. Five semantically explicit graphs represent geographical adjacency, spatial distance, historical demand correlation, pricing-pattern similarity, and static regional attributes. Sample-level time-conditioned graph gating fuses these relations using historical demand states and calendar context. Independent recent, daily, and weekly branches capture short-term variation, daily repetition, and weekly regularity, and are combined through temporal gating. The model produces multiple conditional quantiles and applies horizon-specific conformalized quantile regression using an independent calibration set. Experiments on the Shenzhen UrbanEV dataset at 1, 3, 6, 12, and 24 h horizons achieve a mean absolute error (MAE) of 60.44, root mean squared error (RMSE) of 192.44, and Pinball Loss of 16.96. These values are 11.51%, 6.04%, and 9.50% lower than those of ST-MGF-Q. For calibrated 90% intervals, the prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and Interval Score are 0.9024, 0.0175, and 349.60. Results confirm improved forecasting accuracy, probabilistic quality, and empirical interval reliability.
Lili Zheng, Hengrui Ma, Bo Wang et al.· Electronics· 0 citations
Taxi tipping behavior provides a useful case for studying passenger payment decisions in urban mobility services. Using New York City Yellow Taxi trip records from January to March 2025, this study examines two related tasks: predicting tipping occurrence and estimating tip amount for trips with positive tips. The input feature system includes trip and payment variables, temporal variables, spatial and operational variables, and fare-structure variables. Logistic regression and ordinary least squares regression are used as statistical baselines, while GBDT, XGBoost, LightGBM, CatBoost, and FT-Transformer are compared as tabular prediction models. Models trained on January data are evaluated on a January holdout set and on February and March external test sets. The results show that FT-Transformer achieves the highest AUC for tipping occurrence prediction, while CatBoost achieves the strongest January holdout performance for conditional tip amount regression and remains close to the best external-test results. Significance tests support the classification gains of CatBoost over the logistic regression baseline and the GBDT benchmark, whereas regression gains are more limited. Sensitivity analysis shows that credit-card payment is a strong predictive marker for tipping occurrence but has little additional value for tip amount once tipping has occurred. SHAP analysis further indicates that payment context is more important for tipping occurrence, while trip scale and fare-related variables are more relevant to payment intensity. These findings support a dual-layer interpretation of taxi tipping behavior.
Hui He, Kefeng Wang· E3S Web of Conferences· 0 citations
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