Validation of a multi-modal transit route choice model using smartcard data

Datos Bibliográficos

ID19338165
AutoresMalvika Dixit (0000-0003-4483-7438, Delft University of Technology, autor de correspondencia), Oded Cats (0000-0002-4506-0459, Delft University of Technology), Niels van Oort (0000-0002-4519-2013, Delft University of Technology), Ties Brands (0000-0002-4939-5934, Delft University of Technology), Serge Hoogendoorn (0000-0002-1579-1939, Delft University of Technology)
Año2024
Volumen51
Número5
Páginas1809-1829
Fecha de publicación2024-10-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaTransportation (JOURNAL)
Identificadores de la revistaISSN: 0049-4488 • E-ISSN: 1572-9435
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11116-023-10387-z
OpenAlexW4372294500
IdiomaEN
Citas recibidas2
Referencias citadas29

Validation of travel demand models, although recognised as important, is seldom undertaken. This study adds to the scarce literature in this field by undertaking an external validation of a multi-modal transit route choice model. The model was estimated using smart card data for the urban transit network of Amsterdam before the introduction of a new metro line and is used to predict changes in travel behaviour after the network change. To validate, the model was checked for changes in estimated parameters between the two time periods, and predictive ability was evaluated at different aggregation levels. Although most model parameters were found to be unstable between the two contexts, the predictive performance at all levels was similar to the locally estimated model. Moreover, individual choices and transit mode-share predictions were found to be close to the observed ones. The errors were relatively larger for the link and route-level predictions, some of which could be attributed to the assumptions made regarding consideration choice set given as input to the model. On comparing alternative model specifications, using generic instead of mode-specific travel attributes lead to a strong degradation in predictive performance. Conversely, a model incorporating overlap between routes, with a better model fit in the base period, did not offer a clear improvement in prediction performance. The study highlights the need to validate transit route choice models before using them for deriving policy recommendations, especially in this data-rich age in which it can often be undertaken at a relatively low additional cost

Econometrics · Field (mathematics) · Machine learning · Modal · Mode (computer interface) · Mode choice · Operations research · Predictive modelling · Public transport · Set (abstract data type) · Smart card · Transit (satellite) · Transport engineering · Travel behavior · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Mathematics · Transportation Planning and Optimization · Urban Transport and Accessibility