Estimating urban freight flow using limited data: The case of Delhi, India

Abstract

This paper presents an innovative methodological framework for determining urban freight flows using a multimodal origin–destination synthesis (ODS) model, which we ran with a novel combination of primary and secondary data. Primary data included classified traffic volume counts at limited locations, whereas secondary data, enriched with certain techniques, came from (a) map application programming interfaces used to extract real-time speeds and calibrate a disaggregated speed–volume relationship and (b) advanced techniques for estimating direct flow models (e.g. spatio-temporal Kriging and machine learning models). The paper also discusses the sensitivity of multimodal ODS models to variations in base–seed matrices.