This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale aggregate level mobility forecasts. Such large-scale forecasts are a crucial underpinning for many transport infrastructure investment decisions. We show, in the particular context of (linear-additive) utility maximization (RUM) and regret minimization (RRM) rules, that the decision rule matters for aggregate level mobility forecasts. We find non-trivial differences between the RUM-based and RRM-based transport model in terms of aggregate forecasts of passenger kilometers, demand elasticities, and monetary benefits of transport policies. This opens up new opportunities for policy analysts to enrich their sensitivity analysis toolbox.
|Number of pages||16|
|Journal||Transportation Research Part A: Policy and Practice|
|Publication status||Published - Aug 2018|
- Decision rules
- Discrete choice modelling
- Large-scale transport models
- Random Regret Minimization