As practitioners at any MPO would likely tell you, an enormous amount of time, effort, and resources go into creating a calibrated, trustworthy, travel demand model for a specific base year. Once it’s complete, it becomes the foundation for hundreds of analyses.
The high cost of creating the base year model means it’s not possible to just build a new one each year. This leads to two related downstream impacts. First, when an agency does invest in a new base year model, it wants to calibrate and validate against as much data as possible to ensure the highest quality. Second, because population, land use, and travel patterns all change more rapidly than base years are generated, agencies conduct interim model updates with recent data that reflects the latest changes in the real world. The significant change in travel behavior during and after the pandemic only underscored the importance of this second use case.
Replica data has distinct advantages in serving both of these use cases, which is why it has been utilized for model updates and validations across the country. In this post, we’re highlighting a few of those Replica differentiators and sharing use cases from some real world applications.
Modelers around the country have relied on Replica to support model updates because:
- Replica’s disaggregate synthetic population is updated seasonally, so it provides recent calibration data for attributes like household size and employment. It also includes industry- and home-location-specific work-from-home estimates, based on Replica’s proprietary WFH model.
Because Replica’s population data starts with ACS and other census inputs (like most MPO models), and then is augmented with consumer marketing data and other sources, it can efficiently improve existing model populations without requiring agencies to do complete overhaul. - Replica’s trip rate, time of day, and trip distance distributions can be used to calibrate both activity-based and trip-based models. Because Replica’s data is disaggregate, modelers can set custom bucketing for each of these distribution curves. Different curves can also be created for different cohorts (for instance, income or race/ethnicity), which can help mitigate survey response bias, and generate higher-quality outputs for equity-based analyses.
- By offering network speeds, network-level volumes in 15-minute increments for 100% of roadways, and transit ridership, boardings, and alightings by line and stop for over 400 transit agencies, Replica provides unparalleled coverage for traffic assignment validation data.
- Replica includes two types of data that are traditionally harder to factor into regional travel demand models. First, because it’s produced nationwide, Replica data offers much more detail when it comes to long distance trips that start, end, or pass through an MPO’s jurisdiction. In fact, these long-distance trips have just as much detail as Internal-to-Internal (I-I) trips. Second, Replica includes commercial freight ODs and volumes, with just as much detail as personal travel.
Here’s a few examples of how agencies have utilized Replica in their own model updates and validations.
- SANDAG (San Diego MPO) used our volumes, speeds, and travel time data, in developing their ABM for a 2025 regional model.
- CAMPO (Austin MPO) in Texas used Replica data to validate their TDM using custom geographies and external stations for their OD flows.
- Boston MPO (“CTPS”) has validated Replica’s freeflow speed data and found it more realistic than previously relied upon data and will utilize it in a creation of a newer model. The agency is currently studying other Replica metrics and change factors that can be applied to their model.
- A number of other agencies including MPOs in Rochester and Syracuse in NY, SCAG and Fresno in California, as well as AEC firms like WSP and AECOM, have used Replica data to validate their models and to update their TDM’s base year data.
