A Dual-Graph Spatio-Temporal Network for Urban Rail Transit Ridership Prediction

A Spatio-Temporal Decision-Support Framework for Station-Level Rail Transit Ridership Forecasting Using XGBoost

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.

An Explainable Spatio-Temporal Framework for Traffic Forecasting Using Graph-Based Features

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.

Urban-CSTPNet: Time-Conditioned Multi-Relational Spatio-Temporal Probabilistic Forecasting for Smart Urban Electric Vehicle Charging Networks

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.

A Feature-Enhanced Informer Model with Complex Network Representation for Multi-Step Short-Term Passenger Flow Forecasting in Urban Rail Transit

A feature-enhanced Informer framework that embeds Complex Network Representation (CNR) into the Informer architecture to jointly capture both the topological characteristics of the subway network and spatiotemporal passenger flow dynamics is developed, thereby enabling efficient multi-step passenger flow forecasting.

Accurate ETA Prediction Using Dynamic Route-Aware Graph Neural Networks for Improved Urban Mobility and Well-Being

GSPINN: A Graph Sequential Physics-Informed Surrogate for Trip Travel Time Prediction

Accurate and computationally efficient traffic prediction remains a fundamental challenge for transportation systems, as microscopic simulators are often too expensive for large-scale applications. This paper addresses this limitation by proposing a physics-consistent surrogate modeling framework, the Graph Sequential Physics-Informed Neural Network (GSPINN). The approach integrates graph-based spatial representation with sequence-aware path aggregation to model trip travel time. It introduces a physics-informed learning formulation that encourages monotonic relationships between travel time and key traffic variables through input-output gradient constraints. To assess robustness across varying traffic conditions, the framework is applied to four heterogeneous road networks, each characterized by distinct topology, demand patterns, and control regimes. The results show consistent predictive performance and stable behavioral properties across all settings. Complementary SHAP-based interpretability further indicates that the model captures network-specific feature dependencies in each case, providing evidence that it adapts to local traffic dynamics rather than overfitting to a single environment. In addition to accuracy and reliability, the proposed surrogate provides substantial computational advantages at inference time, achieving speed-ups of 3x to 55x. This work therefore shows that embedding physically meaningful structure into learning objectives is an effective strategy for traffic surrogate modeling, yielding models that maintain competitive predictive accuracy while substantially improving directional behavioral consistency.