FFY27 Model Support RFP - Bidders Conference.pdf
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- Attached to
- Modeling consultant to support modeling for TDM
- Solicitation number
- BD-27-1217-MAP05-MAP05-133310
- Issued by
- Suffolk County, Massachusetts
About this file
This is a Bidders Conference document from the Boston Region Metropolitan Planning Organization (MPO) regarding the FFY27 Model Support RFP, which solicits a modeling consultant to develop and enhance the region's travel demand modeling capabilities. The RFP encompasses five primary tasks organized under two tracks: Track 1 focuses on TDM28 Development, including project management and coordination, model design review and refinement, model estimation and calibration, model implementation, and systemwide validation and testing; Track 2 addresses next-generation model (TDMx) advancement, TransCAD software integration, and demonstration applications. The TDM28 model will serve as the travel demand model of record for the Boston Region MPO and will be calibrated by October 1, 2027, to support Long-Range Transportation Plan development. The model incorporates a 2026 base year with 2050 forecasts and includes major updates such as updated TAZ boundaries, aggregate socioeconomic data inputs, enhanced trip distribution with a two-step structure, and an additional EV time period in the time-of-day component. Questions from potential bidders were due September 17, 2026, at 5:00 p.m. ET, while proposals are due October 9, 2026, at 5:00 p.m. ET, submitted separately for technical and cost components. Award is anticipated in November 2026.
The contract will be funded through federal transportation planning grants. The development plan is divided between CTPS (Center for Transportation and Pavement Studies), which will prepare input data and validation data, and the selected consultant, who will finalize model design, conduct software implementation, and perform re-estimation and calibration of parameters. The current modeling platform builds upon TDM23.3.0 and TDMx, with resources available on GitHub. Base year inputs include socioeconomic data, network files, costs (parking, transit fares, and auto operating costs), and special generator inputs such as airport enplanements and casino trips. Selection criteria include technical competence, relevant experience with regional travel demand models, understanding of the Boston region's transportation system, proposed methodology, cost-effectiveness, and collaborative capability with MPO staff.
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Text version
Boston Region MPO Model Support Bidder’s Conference - September 14, 2026
RFP Tasks
Task 1 — Project Management & Coordination
Track 1: TDM28 Development
● Task 2 Model Design Review and Refinement
● Task 3 Model Estimation and Calibration
● Task 4 Model Implementation
● Task 5 Systemwide Validation and Sensitivity Testing
Track 2: TDMx Support
● Task 6 Next-Generation Model (TDMx) Advancement, TransCAD Software Integration, and Demonstration Application
TDM28 vs. TDMx
TDM28 - Statutory
● Users / Uses: traditional modelers for statutory requirements
● Highly accessible, easily maintained
● Best estimates of inputs, assumptions, relationships, outcomes
● Consistent System
○ Repeated tests of alternatives
○ Tests of multiple projects
TDMx - Exploratory
● Users / Uses: decision support modelers for new questions and/or context to statutory predictions
● Highly flexible, easily adapted, understandable
● Reasonable / Possible inputs, assumptions, relationships, outcomes
● Purpose built system
○ Address question at hand
○ Evolve with insights
TDM28 Motivation and Plan
Purpose and Goal
● Travel demand model of record for Boston Region MPO, other RPAs, and MassDOT
● Highly accessible, robust, clear, understood, and documented
● Calibrated model by Oct 1, 2027 to support LRTP build / no-build development
Continuation of TDM23
● Trip based model
● TransCAD with python components
Major Updates
● Base year 2026, Forecast 2050
● Model $ consistent with base year
● Updated TAZ boundaries
● Aggregate SE data inputs
TDM28 Design and Potential Enhancements
● Vehicle Availability - maintain alternatives, re-estimate to aggregation
● Trip Generation - maintain purposes, re-estimate to aggregation
● Work from Home - removed
● Trip Distribution - enhance with two-step structure to better control flows
● Mode Choice - maintain alternatives
● Time of Day - add EV time period
● Transit Assignment - no change
● Highway Assignment - no change Potential Improvements
● Improve relation of NHB to HB trips
● On-campus students
● Light truck representation
● TNC data to refine RS representation
TDM28 Development Schedule
TDM28 Development Plan
CTPS
● Prepare Input Data
○ Updated TAZ and Model Extent
○ Roadway, Transit, Non-Motorized networks
○ SE data
○ Externals, Special Generators
● Prepare Validation Data
○ MTS Summaries
○ Roadway volumes, trip distribution
○ Transit boardings
Consultant
● Finalize model design
● Software Implementation
● Re-estimate / Calibrate parameters
● Overall calibration / validation
TDM28 / X Model Platforms
Current Status: TDM23.3.0 & TDMx
● https://github.com/CTPSSTAFF/lighthouse
● 3.0 Release early November
2024 Base Year Inputs Inputs
● Socioeconomic Data
● Network
● Costs (parking, transit fares, auto operating ) - still
2010$
● Special generator inputs
● Airport enplanements
● Casino trips
● External trips
Capabilities / Scripts
● Replace Anaconda with UV
● Light truck demand
● Add truck special generator
● Skimming utilities
Calibration / Validation
● Re-estimate Trip Generation
● Re-calibrate all demand components
● Validate to OD Flows, Transit Boardings, Highway
Volumes https://github.com/CTPSSTAFF/lighthouse
Key Dates
● Questions Due: September 17, 5:00 p.m ET to model.support@ctps.org
● Proposals Due: October 9, 2026, 5:00 p.m. ET to model.support@ctps.org
● Separate technical and cost proposals
● Anticipated Award: November 2026 mailto:model.support@ctps.org mailto:model.support@ctps.org
Questions?
Vision: Deliberation with TDMx Analysis
Planning Decision Needs Help
Structure Question in Modelable Terms
Represent question in model platform
Translate model outputs into decision insights
Refine Question based on insights
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