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Operational route choice methodologies for practical applications

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Abstract

This paper focuses on the application of tractable route choice models and presents a set of methods for deriving relevant disaggregate and aggregate route choice indicators, namely link and route flows. Tractability is achieved at the disaggregate level by the recursive logit model and at the aggregate level by the mental representation item (\(\mathrm {MRI}\)) approach. These two approaches are analyzed here, and extensions of the \({\mathrm {MRI}}\) approach are presented. The analysis elaborates on the features of each model and allows to draw insights into the use of a specific model, depending on the needs of the application and the data availability. The performance of the two models is tested on real data. The results demonstrate the validity of the \({\mathrm {MRI}}\) model that is intended for aggregate analysis.

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Notes

  1. The simplest case concerns a deterministic association of each observation with a \({\mathrm {MRI}}\) sequence. That is, \({\text{P}}({\text{y}} \, | \, {\text{r}}) = 1\) if the y traverses the geographical span of all its elements in the correct order, and zero otherwise. An illustrative example involving one \({\mathrm {MRI}}\) in the sequence is presented in Fig. 12.

  2. The assumption of \(\mathcal {U}\) is not necessary, but important as discussed in “Introduction” section. Hence, we adopt it throughout the paper, when dealing with the choice set of a disaggregate model.

  3. In this example, \(\mathcal {M} = {\mathcal {C}}_n\), as there are only two mutually exclusive \(\mathrm {MRIs}\) and no possibility to follow a sequence of them.

  4. Under the assumption that most observations do not contain loops, the disaggregate model should assign very low probabilities to paths with loops.

  5. The same dataset is used for the application of the RL model and its extension in Fosgerau et al. (2013), Mai et al. (2015) and Mai (2016).

  6. The \({\mathrm {MRI}}\)-based models presented in this section are estimated using Biogeme (Bierlaire 2003).

  7. In this paper we consider observations with a minimum length of 2 km, hence we have a smaller data sample.

  8. The variables associated with the travel time and length are highly correlated due to the fact that the former is computed based on the latter. Therefore, only one of them is included in the specification.

  9. 500000 paths are sampled for each \({ od }\) pair, by simulating a random walk on the network to draw from the RL link transition probabilities.

  10. The log likelihood converges to the unconditional value as long as \(t \rightarrow \infty\). 100 samples are used so that the analysis can be conducted in a reasonable computational time.

  11. The null model predicts equal probabilities for all alternatives, as all its parameters are fixed to zero.

  12. http://energy.jrc.ec.europa.eu/transtools/index.html.

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Acknowledgements

This research is supported by the Swiss National Science Foundation Grant \(\#200021-146621\) “Capturing latent concepts with non invasive sensing systems”. We thank Gunnar Flötteröd for his suggestions and fruitful discussions, and Mai Tien and Emma Frejinger for providing the code for, and insights into, the recursive logit model.

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Authors and Affiliations

  1. Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Evanthia Kazagli & Michel Bierlaire

  2. School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences and Arts Western Switzerland (HES-SO), Yverdon-les-Bains, Switzerland

    Matthieu de Lapparent

Authors
  1. Evanthia Kazagli
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  2. Michel Bierlaire
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  3. Matthieu de Lapparent
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Contributions

EK: Literature search and review, manuscript writing, content planning; MB: Manuscript editing, content planning; M.L.: Manuscript editing.

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Correspondence to Evanthia Kazagli.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

Appendix

Appendix

See Figs. 12, 13, 14, 15, 16 and 17 and Table 5.

Fig. 12
figure 12

Illustration of the measurement equation

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Fig. 13
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The network of Borlänge

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Fig. 14
figure 14

The \(\textit{OD}\) zones in Borlänge

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Fig. 15
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The geographical span of the \(\mathrm {MRIs}\) in Borlänge

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Fig. 16
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The representative points of the \({\mathrm {MRI}}\) sequences in Borlänge

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Fig. 17
figure 17

Example of \({\mathrm {MRI}}\) sequences in Borlänge

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Table 5 List of alternatives of the \({\mathrm {MRI}}\) model (Borlänge)
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Kazagli, E., Bierlaire, M. & de Lapparent, M. Operational route choice methodologies for practical applications. Transportation 47, 43–74 (2020). https://doi.org/10.1007/s11116-017-9849-0

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