Commodity flow estimation for a metropolitan scale freight modeling system: supplier selection considering distribution channel using an error component logit mixture model
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11116_2018_Article_9932.pdf
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Author(s)
Sakai, Takanori
Alho, André
Hyodo, Tetsuro
Bhavathrathan, B. K.
Ben-Akiva, Moshe E
Date Issued
October 2018
Journal
Transportation
Publisher
Springer US
Citation
Sakai, Takanori, et al. “Commodity Flow Estimation for a Metropolitan Scale Freight Modeling System: Supplier Selection Considering Distribution Channel Using an Error Component Logit Mixture Model.” Transportation, Oct. 2018.
Version
Final published version
Abstract
Freight forecasting models have been significantly improved in recent years, especially in the field of goods vehicle behavior modeling. On the other hand, the improvements to commodity flow modeling, which provide inputs for goods vehicle simulations, were limited. Contributing to this component in urban freight modeling systems, we propose an error component logit mixture model for matching a receiver to a supplier that considers two-layers in supplier selection: distribution channels and specific suppliers. The distribution channel is an important element in freight modeling, as the type of distribution channel is relevant to various aspects of shipments and vehicle trips. The model is estimated using the data from the Tokyo Metropolitan Freight Survey. We demonstrate how typical establishment survey data (i.e. establishment and outbound shipment records) can be used to develop the model. The model captures the correlation structure of potential suppliers defined by business function and provides insights on the differences in the supplier choice by distribution channel. The reproducibility tests confirm the validity of the proposed approach, which is currently integrated into a metropolitan-scale agent-based freight modeling system, for practical use. Keywords: Freight demand model, Urban freight, Commodity fow, Error component, logit mixture, Disaggregate
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Intelligent Transportation Systems Laboratory
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DOI of Published Version
https://doi.org/10.1007/s11116-018-9932-1
