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:2609.16415v1 Announce Type: cross Abstract: Pedestrian-count forecasting supports pedestrian-oriented Intelligent Transportation Systems (ITS), including crowd monitoring, pedestrian-traffic st...
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
arXiv:2603. 14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities.
arXiv:2608. 14660v1 Announce Type: cross Abstract: This study proposes a ring-based SpatialTransformer to learn how building uses at different distances from a railway station interact to generate pedestrian flow.
The 10th AI City Challenge, held alongside ECCV 2026, celebrates a decade of benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 inception focused on vehicle detection, classification, and tracking, the challenge has expanded into a comprehensive benchmark suite covering multi‑camera perception, multimodal reasoning, synthetic‑to‑real learning, generative forecasting, and privacy‑preserving evaluation. The 2026 edition saw 325 registered teams from 26 countries, with six main tracks—spanning multi‑camera 3D perception, transportation safety captioning and VQA, traffic anomaly reasoning, text‑based person anomaly search, generative traffic video forecasting, and cross‑city object detection—plus two out‑of‑domain leaderboards for fisheye traffic‑violation understanding and pedestrian situated‑intent VQA.
arXiv:2607. 16156v1 Announce Type: new Abstract: Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists.
arXiv:2609.14383v1 Announce Type: new Abstract: Real-time pedestrian detection in driving scenes is constrained by three coupled failure modes: tiny targets lose discriminative evidence, occlusion we...
arXiv:2606. 10938v1 Announce Type: new Abstract: Trajectory data augmentation is a promising approach to mitigate data scarcity in machine learning applications, but its utility has been limited by the complexity of preserving spatio-temporal coherence.
arXiv:2604. 28065v2 Announce Type: replace-cross Abstract: Although intersections are the most complex parts of the roadway network, pedestrian crashes at non-intersection locations are disproportionately frequent, highlighting a serious traffic safety concern.
The paper introduces the Multi-Context Fusion Transformer (MFT), a model that predicts pedestrian crossing intentions in urban settings by integrating four types of contextual information—pedestrian behavior, environment, localization, and vehicle motion—through a progressive fusion strategy. MFT uses intra-context attention for reciprocal interactions within each context, cross-context attention to combine these contexts into a global representation, and guided attention mechanisms to refine both context tokens and the global token. Experiments on JAADbeh, JAADall, and PIE datasets show MFT outperforms existing methods with accuracies of 73%, 93%, and 90% respectively, and ablation studies confirm the importance of each network component and input context.
arXiv:2606. 12500v1 Announce Type: cross Abstract: Traffic microsimulation combined with surrogate safety measures has increasingly been used as a proactive alternative to historical crash data for predicting crash frequency for current or planned road infrastructure designs.
The paper proposes a modular training pipeline for zero‑shot cross‑city object detection that combines a multi‑dataset pre‑training strategy with class‑agnostic objectness distillation and a domain‑resilient augmentation stream featuring a Grayworld transformation. Applied to the RF‑DETR detector, the approach reduces cross‑city distribution gaps while using only 16 GB GPU memory, achieving a 24.29‑point mAP improvement and 1st place on the AI City Challenge Track 6 leaderboard. The authors provide code and data at the referenced GitHub repository.