Commodity flow estimation for a metropolitan scale freight modeling system: supplier selection considering distribution channel using an error component logit mixture model
Author(s)Sakai, Takanori; Alho, André; Hyodo, Tetsuro; Bhavathrathan, B. K.; Ben-Akiva, Moshe E
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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
DepartmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering; Massachusetts Institute of Technology. Intelligent Transportation Systems Laboratory
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.
Final published version