SEAHORSE is a unified framework that standardizes training and evaluation of neural STPP models via a common interface and pairs it with the HawkesNest synthetic stress-test suite to expose model inductive biases.
A Countrywide Traffic Accident Dataset
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
Reducing traffic accidents is an important public safety challenge. However, the majority of studies on traffic accident analysis and prediction have used small-scale datasets with limited coverage, which limits their impact and applicability; and existing large-scale datasets are either private, old, or do not include important contextual information such as environmental stimuli (weather, points-of-interest, etc.). In order to help the research community address these shortcomings we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. US-Accidents currently contains data about $2.25$ million instances of traffic accidents that took place within the contiguous United States, and over the last three years. Each accident record consists of a variety of intrinsic and contextual attributes such as location, time, natural language description, weather, period-of-day, and points-of-interest. We present this dataset in this paper, along with a wide range of insights gleaned from this dataset with respect to the spatiotemporal characteristics of accidents. The dataset is publicly available at https://smoosavi.org/datasets/us_accidents.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Defines analogical proportions on probabilities and distributions, then experimentally checks inheritance from profile-level analogies in classification settings.
citing papers explorer
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From numerical proportions to analogical proportions between probabilities
Defines analogical proportions on probabilities and distributions, then experimentally checks inheritance from profile-level analogies in classification settings.