Emerging transportation technologies and shared mobility services, or new mobility options (NMOs), are impacting travel behavior and demand. Examples include micromobility (bicycles and scooters), connected and automated vehicles (CAVs), transportation network companies (ride sourcing, hailing, and sharing), car sharing, and micro-transit. As these NMOs proliferate, transportation planners and decision-makers need to be able to understand how to harness positive and mitigate negative impacts.
One of the primary tools available to understand potential impacts and future uncertainty are travel demand forecasting models (TDFMs). However, many of the current generation TDFMs do not explicitly include these NMOs. Concurrently, decision-makers are requesting insight into areas for which TDFMs were not originally designed, such as equity, energy use, and greenhouse gas emissions (GHG), for which NMOs are suggested to positively impact. As such, modelers at state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) are presented with a number of challenges in incorporating NMOs into TDFMs and estimating their benefits.
The challenges include, but are not limited to:
- Navigating data issues (acquisition, collection, sharing, sourcing, etc.);
- Building model structures that explicitly account for these new mobility options;
- Estimating, calibrating, and validating models, especially for options that may not yet be available in the market (such as CAVs);
- Examining the transferability of model parameters between geographies;
- Considering approaches and methods for the type of model in use at their organization (trip-based, tour-based, or activity-based); and
- Considering uncertainty of future forecasts with changing travel behavior when making investment selection and decision-making.
Research is needed to identify best practices, case studies, and strategies to incorporate NMOs in travel demand modeling to better inform decision-making and investment selection processes.
The objective of this research is to develop a practitioners’ guide for implementation of best practices for DOTs and MPOs looking to incorporate NMOs into TDFMs. The research will focus on the identification of key TDFM data, specifications, methods, and approaches related to travel behavior; the proclivity of travelers to adopt NMOs; and performance measures that estimate the benefits from deployment of NMOs.