Data underlying the publication: A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data
DOI:10.4121/3630e859-15ed-400b-b4ca-6b7eacb392b7.v1
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DOI: 10.4121/3630e859-15ed-400b-b4ca-6b7eacb392b7
DOI: 10.4121/3630e859-15ed-400b-b4ca-6b7eacb392b7
Datacite citation style
Nicolet, Adrien (2024): Data underlying the publication: A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/3630e859-15ed-400b-b4ca-6b7eacb392b7.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
This research proposes a way to estimate heterogeneous mode choice preferences in the context of intermodal transport directly from aggregate data. To do so, we develop a Weighted Logit Mixture (WLM) methodology , which is compared to a benchmark consisting of a Multinomial Logit (MNL) model. In the WLM, we estimate the Lognormal distribution of the intermodal cost coefficient within the shippers' population. Then, we compare the performance of our WLM against the benchmark in terms of mode share predictions, elasticities, and correlations. Finally, we also investigate the influence of adding the Value Of Time (VOT) in the WLM on the model's performance.
History
- 2024-07-04 first online, published, posted
Publisher
4TU.ResearchDataFormat
csv/py/ipynb/htmlAssociated peer-reviewed publication
A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate DataOrganizations
TU Delft, Faculty of Mechanical Engineering, Department of Maritime and Transport TechnologyDATA
Files (20)
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2b4b47a56ca0a3a4d3445f247cfe0403LOGN_COSTinter.html - 13,004 bytesMD5:
1edfb93ef09902cbf55ced269fbb7a1fLOGN_COSTVOTinter.html - 21,596 bytesMD5:
e66e426109c51133323c4857e8c5b22cLognormal_distribution_plot.ipynb - 22,794 bytesMD5:
cd85a8bc3d4ef70d803dcd5b90ac1d24LognormalVOT_distribution_plot.ipynb - 6,943 bytesMD5:
43f035bb735bf3c72e150206d6ac34d9MNL-ASTRA.ipynb - 2,951,477 bytesMD5:
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a5ec318b59b826b919258d563dacb494Results_WLM.csv - 1,841 bytesMD5:
6c0b1f9af07371c3a035fd4430382694Results_WLM_otherOD.csv - 1,846 bytesMD5:
0c4c9d68a6be44260daaa9a90ce7bcc5Results_WLM_ROTANT.csv - 2,254 bytesMD5:
bbb0d196494a1064d96adc7ad6cb189bWLM_COSTinter_lognormal.py - 2,286 bytesMD5:
242a16eab446816e14c2e958b1a4bd8dWLM_COSTVOTinter_lognormal.py - 2,946,130 bytesMD5:
d7f7a8ce5d936fd9ba80e8ba768a3253WML_withPredictions.csv -
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