E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction

Electric bicycles (e-bikes) are rapidly increasing in use, raising safety concerns due to a rise in accident reports. However, e-bike incident reports often use unstructured narrative formats, which hinders quantitative safety analysis. This study introduces E-bike agents, a framework that uses large language models (LLM) powered agents to classify and extract safety variables from unstructured incident reports. Our framework consists of four LLM agents, handling data classification, information extraction, injury cause determination, and component linkage, to extract the key factors that could lead to E-bike accidents and cause varying severity levels. Furthermore, we used an ordered logit model to examine the relationship between the severity of the incident and the factors retrieved, such as gender, the type of cause, and environmental conditions. Our research shows that equipment issues are slightly more common than human-related ones, but human-related incidents are more often fatal. Specifically, pedals, tires, and brakes are frequent contributors to accidents. The model achieves a high weighted F1 score of 0.87 in classification accuracy, highlighting the potential of using LLMs to extract unstructured data in niche domains, such as transportation. Our method offers a scalable solution to improve e-bike safety analytics and provides actionable information for policy makers, designers, and regulators.

Copula-based synthetic population generation

Population synthesis consists of generating synthetic but realistic representations of a target population of micro-agents for the purpose of behavioral modeling and simulation. We introduce a new framework based on copulas to generate synthetic data for a target population of which only the empirical marginal distributions are known by using a sample from another population sharing similar marginal dependencies. This makes it possible to include a spatial component in the generation of population synthesis and to combine various sources of information to obtain more realistic population generators. Specifically, we normalize the data and treat them as realizations of a given copula, and train a generative model on the normalized data before injecting the information on the marginals. We compare the copulas framework to IPF and to modern probabilistic approaches such as Bayesian networks, variational auto-encoders, and generative adversarial networks. We also illustrate on American Community Survey data that the method proposed allows to study the structure of the data at different geographical levels in a way that is robust to the peculiarities of the marginal distributions.