Route choice modeling: past, present and future research directions

By Carlo G. Prato. 2009

488 citations recorded by the source corpus.

Modeling route choice behavior is problematic, but essential to appraise travelers' perceptions of route characteristics, to forecast travelers' behavior under hypothetical scenarios, to predict future traffic conditions on transportation networks and to understand travelers' reaction and adaptation to sources of information. This paper reviews the state of the art in the analysis of route choice behavior within the discrete choice modeling framework. The review covers both choice set generation and choice process, since present research directions show growing interest in understanding the role of choice set size and composition on model estimation and flow prediction, while past research directions illustrate larger efforts toward the enhancement of stochastic route choice models rather than toward the development of realistic choice set generation methods. This paper also envisions future research directions toward the improvement in amount and quality of collected data, the consideration of the latent nature of the set of alternatives, the definition of route relevance and choice set efficiency measures, the specification of models able to contextually account for taste heterogeneity and substitution patterns, and the adoption of random constraint approaches to represent jointly choice set formation and choice process.

Subfields: Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/b3c47e78af4626b901841f0c395bab2cb86d3154

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Transportation

Citations in the corpus

A nested recursive logit model for route choice analysis (2015, methodology): “The dynamic network is suitable for modeling congested networks, the RL model has been used for this purpose by Ramos et al. (2012). The challenge lies in the size of the state space, which is considerably larger than a static network since it is the number of links multiplied by the number of time ”
Finding the most reliable path with and without link travel time correlation: A Lagrangian substitution based approach (2011, methodology): “For the first path generation method, 1,000 random draws are extracted from a normal distribution for each link, with mean and variance equal to the link travel time (Prato, 2009).”
The Welfare Effects of Transportation Infrastructure Improvements (2019, unknown): “Prato, C. G. (2009): “Route choice modeling: past, present and future research directions,” Journal of choice modelling, 2(1), 65–100. Redding, S. J., and E. Rossi-Hansberg (2017): “Quantitative spatial economics,” Annual Review of Economics, 9, 21–58.”