Estimating Bicycle Volume Levels at Urban Intersections using Large Language Models

Bicycle volume estimation is essential for effective city planning and ensuring the safety of vulnerable road users. Traditional approaches often involve resource-intensive data collection, such as short-term counts expanded using continuous counts (CCs), or the development of direct-demand models from site-specific data such as geometry, socioeconomic, and land use. Although recent efforts have explored crowdsourced data, such sources may be biased and not consistently available across jurisdictions. This study explores the potential of large language models (LLMs), specifically ChatGPT-4o mini, as a low-cost and accessible alternative for estimating bicycle volume levels at intersections. A data set of CCs over 12 months and site characteristics for 158 signalized intersections in the Region of Waterloo, Ontario, Canada, was compiled. From this, the average annual daily bicycle volume was computed, and intersections were categorized into low, medium, or high volume levels. Eight LLMs and three benchmark approaches, including a Naïve (random), an ordered logit (OL) model, and human survey responses, were applied to the data set. Accuracy in predicting the correct volume category was the evaluation metric. The best-performing LLM requires only two input features: (1) bike lane length (km); and (2) the presence of schools, and achieves an average accuracy of 60%, just 4% below the OL model, and significantly better than the Naïve model and human survey respondents. Of interest, providing satellite images or additional quantitative input variables decreased the LLM performance. The results suggest LLMs can offer a scalable, data-efficient alternative for jurisdictions lacking extensive bicycle count data or modeling expertise.