Estimating urban freight flow using limited data: The case of Delhi, India. (May 2021)

• Record Type: Journal Article
• Authors: Malik, Leeza; Tiwari, Geetam; Biswas, Udayin; Woxenius, Johan
• Is Part Of: Transportation research. Volume 149(2021)
• Publication Date: 2021-05
• Subjects: Freight origin destination synthesis -- Spatio-temporal Kriging -- Machine learning -- Google application programming interfaces; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2021.102316
• Languages: English
• ISSNs: 1366-5545

Abstract

Highlights: Urban freight flow estimation for developing urban settings like India. Integration of direct flow estimation models with origin destination synthesis model. Spatio-temporal Kriging and machine learning direct flow estimation models performance comparison. Demonstration of road link speed estimation using map application programming interfaces. 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.