Infoscience

Thesis

# Aggregate route choice models: the mental representation item approach

Route choice analysis concerns the understanding, modeling and prediction of the itinerary of an individual who travels from one position to another. In this thesis we elaborate on aggregate route choice analysis. The objective is the development of a flexible framework for analysing and predicting route choice behavior. The research is motivated by the need to reduce the structural complexity of the state of the art route choice models and aims at facilitating their practical applications. Our approach is inspired by the environmental images of the physical space that individuals form in their minds. The framework is based on elements designed to mimic these representations. In this context, we introduce the concept of mental representation item ($\mathrm{MRI}$) in route choice analysis. The $\mathrm{MRIs}$ represent the strategic decisions of individuals and constitute the building blocks of the alternatives of the aggregate model. They play the same role as the links do in the specification of a disaggregate model. In contrast to the links, the $\mathrm{MRIs}$ are not dictated by the definition of the network model. Their definition depends on the analyst, allowing her to control the trade-off between complexity and realism, according to the needs of the specific application and the data availability. We start by presenting a methodology for the definition of operational random utility models based on $\mathrm{MRIs}$. As a proof of concept, we define a simple model for the town of Borl&quot;ange, in Sweden, and test it using real data. We further discuss applications of the proposed model to traffic assignment and route guidance. The results demonstrate that the use of simple methods leads to a meaningful model that can be estimated and used in practice. We then investigate the capability of the proposed $\mathrm{MRI}$ model to derive route choice indicators for practical applications, through comparison with a state of the art disaggregate model. The recursive logit (RL) model is selected as the representative of the existing disaggregate approaches. An extension of the $\mathrm{MRI}$ framework with the definition of a graph of $\mathrm{MRI}$ elements is presented and methods to derive route choice indicators from a model that does not correspond to the intended level of analysis are proposed. The evaluation of the models' performance at the aggregate level shows that the $\mathrm{MRI}$ model should be preferred against a disaggregate model that is subjected to aggregation, if an aggregate analysis is of interest. To demonstrate the generalization and applicability of the framework, we use a dataset from the city of Qu\'ebec, in Canada. Our approach is motivated by (i) the additional complexity in the definition of the model due to the size of the city, and (ii) the lack of a detailed disaggregate network model. The proposed model is (i) operationalized using simple techniques, (ii) compatible with the standard estimation procedures and (iii) by integration with the RL model, readily applied to the prediction of flows on the major segments of the network. This model is not as simple as the first $\mathrm{MRI}$ model, yet still of much lower structural complexity in comparison with the disaggregate approach, allowing for fast computation times. The results demonstrate its capability to reproduce the patterns in the observed flows. This thesis contributes by (i) gradually addr

Thèse École polytechnique fédérale de Lausanne EPFL, n° 8004 (2017)
Programme doctoral en génie civil et environnement
Faculté de l'environnement naturel, architectural et construit
Institut du développement territorial
Laboratoire transport et mobilité
Jury: Prof. Alain Nussbaumer (président) ; Prof. Michel Bierlaire (directeur de thèse) ; Prof. Nikolaos Geroliminis, Prof. Gunnar Flötteröd, Prof. Otto Anker Nielsen (rapporteurs)

Public defense: 2017-11-10

#### Reference

Record created on 2017-11-13, modified on 2017-11-13

### Fulltext

• Thesis submitted - Forthcoming publication