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Transportation
Published in: Transportation 1/2020

06-01-2018

Operational route choice methodologies for practical applications

Authors: Evanthia Kazagli, Michel Bierlaire, Matthieu de Lapparent

Published in: Transportation | Issue 1/2020

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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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Appendix
Available only for authorised users
Footnotes
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.
 
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Metadata
Title
Operational route choice methodologies for practical applications
Authors
Evanthia Kazagli
Michel Bierlaire
Matthieu de Lapparent
Publication date
06-01-2018
Publisher
Springer US
Published in
Transportation / Issue 1/2020
Print ISSN: 0049-4488
Electronic ISSN: 1572-9435
DOI
https://doi.org/10.1007/s11116-017-9849-0

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