arXiv AI
How Good Are Time-Series Foundation Models for Pedestrian Crowd Count Forecasting? A Cross-Dataset Comparative Study
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arXiv Machine Learning
Jul 21
Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting
arXiv:2607. 17758v1 Announce Type: new Abstract: Managing massive crowds during infrequent special events requires reliable real-time pedestrian-flow forecasting to ensure public safety and operational efficiency.
arXiv Machine Learning
Sep 14
Estimating Pedestrian Volumes from GIS-Derived Built-Environment Features: A Machine Learning Framework
The paper introduces a machine learning pipeline that predicts 2‑hour peak pedestrian volumes at 101 intersections in Portland, Oregon, using built‑environment, land‑use, and street‑network features from open GIS data. Starting from a Negative Binomial GLM, the authors add feature selection, count‑aware gradient boosting, and repeated cross‑validation, ultimately selecting a histogram‑based gradient boosting model with Poisson loss and L1 Lasso feature selection. This model reduces cross‑validated RMSE by 12% and holdout RMSE by 19% compared to the GLM baseline.
arXiv AI
Jul 17
Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting
arXiv:2607. 14871v1 Announce Type: cross Abstract: In many operational time-series forecasting applications, such as crowd demand forecasting, the risk related to under-prediction is substantially higher than that of over-prediction.
arXiv Machine Learning
Aug 19
Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data
The paper proposes a proactive approach to road safety in Greater Sydney by using connected vehicle telemetry to predict risky driving events before crashes occur. It quantifies risky driving with g‑force thresholds and builds spatio‑temporal heatmaps to locate high‑risk zones. Eight predictive models were compared, with ARIMA achieving the lowest error and showing that simple time‑series methods can rival deep learning when data are limited, highlighting the value of IoT data for targeted safety interventions.
arXiv AI
Jun 15
A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health
arXiv:2606. 14604v1 Announce Type: cross Abstract: Wearable devices and smartphones generate rich behavioural time series that can support proactive health interventions, yet systematic comparisons of modern forecasting architectures for these data are lacking.
arXiv Machine Learning
Jun 18
Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting
arXiv:2606. 18367v1 Announce Type: new Abstract: Standard benchmarks evaluate time series foundation models (TSFMs) using aggregate metrics, but these can mask severe failures in critical operating regimes.