- What segmentations have been used to implement the frequency and mode-destination models that have been developed for the eight home-based and six non-home-based travel purposes?
- What methodology has been employed to make projections of the future South East Wales population by the segmentations used in the models?
- How have the frequency and mode-destination models been combined into 'Travel Demand models' that are able to predict demand for park-and-ride sites and for new public transport modes?
- How well do the implemented models replicate observed travel patterns in the 2015 base year?
This report documents the implementation of the demand models that form part of the South East Wales Transport Model (SEWTM). The model implementation structure and the mode-destination choice model parameters have been transferred from the strategic model developed for the West Midland region, PRISM West Midlands.
The SEWTM travel demand forecasting system comprises three principal components that are either identical or closely related in structure to those used in the PRISM West Midlands model:
- The Population Model contains the prototypical sampling procedure and the car ownership models. This component produces detailed projections of the future South East Wales population, which are not influenced by accessibility. The projections are made for all 964 zones in the fully modelled area, i.e. both the area of detailed modelling and the rest of the fully modelled area.
- The Travel Demand Models apply the frequency, mode, destination, PT access mode and station choice, and time period choice models. In summary, these components predict the future travel choices of the South East Wales population resident in the fully modelled area projected by the Population Model.
- Final Processing converts the predicted trip matrices for fully modelled origin zones for each purpose, mode and time period into the more aggregate segmentations represented in the highway and public transport assignments, and then applies a pivoting procedure to predict changes in demand relative to the base matrices.
How well do the implemented models predict the mode shares observed in the local household interview data?
- Overall the predicted mode shares are close to the observed values. For some purposes the car driver share is under-predicted which is related to how the model predicts the distribution of persons across different car availability segments.
How well do the implemented models predict tour lengths compared to National Travel Survey data for South East Wales?
- Overall the model validated reasonably well against the tour length information observed in the National Travel Survey, however there was some tendency to over-predict car passenger tour lengths and under-predict train tour lengths.
- Subsequent to this validation step, adjustments were made to the models so that the predicted proportions of tours by distance band exactly matches the National Travel Survey distributions.
- We recommend that realism tests are undertaken to verify that the model sensitivities (elasticities) are WebTAG compliant.
The research described in this report was prepared for the Welsh Government and conducted by RAND Europe.
This report is part of the RAND Corporation research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity.
Permission is given to duplicate this electronic document for personal use only, as long as it is unaltered and complete. Copies may not be duplicated for commercial purposes. Unauthorized posting of RAND PDFs to a non-RAND Web site is prohibited. RAND PDFs are protected under copyright law. For information on reprint and linking permissions, please visit the RAND Permissions page.
The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.