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Title:A Logit mixture model estimating the heterogeneous mode choice preferences of shippers based on aggregate data
Authors:A. Nicolet, R.R. Negenborn, B. Atasoy

Journal:IEEE Open Journal of Intelligent Transportation Systems

Abstract:Understanding the modal split in freight transportation is a key factor for implementing successful sustainable policies. Mode choice models should then be as representative of reality as possible. The use of disaggregate shipment data can help to achieve it. However, shipment data are often unavailable due to confidentiality issues. As a result, numerous models using aggregate data only have been developed, but their capacity to capture preferences' heterogeneity remains limited. In this paper, we propose a Weighted Logit Mixture model to estimate heterogeneous mode choice preferences of shippers directly from aggregate data. The proposed Weighted Logit Mixture is applied to a case study along the European Rhine-Alpine corridor and allows to estimate the probability distribution of the cost sensitivity among the population. The estimation results show that there exists a substantial variation of the cost sensitivity regarding intermodal transport, but that no significant variation is revealed considering road transport only. The proposed methodology is also compared to a state-of-the-art Weighted Logit model to assess its potential. This reveals that the proposed Weighted Logit Mixture exhibits at least a similar predictive power to the benchmark while achieving a better description of the population's preferences that enables policy-makers to take better informed decisions and appropriate actions.

Reference:A Logit mixture model estimating the heterogeneous mode choice preferences of shippers based on aggregate data. A. Nicolet, R.R. Negenborn, B. Atasoy. IEEE Open Journal of Intelligent Transportation Systems, 2022. Open access.
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