Travel Model Two Assignment System ¶

Overview ¶

The Travel Model Two (TM2) assignment system converts travel demand into traffic flows and transit ridership by simulating traveler route choice on transportation networks. The assignment system consists of two primary components: highway assignment and transit assignment, each using equilibrium-based algorithms to model realistic travel patterns.

System Architecture ¶

graph TD
    A[Travel Demand
From CT-RAMP] --> B{Assignment Type}
    B -->|Auto Trips| C[Highway Assignment]
    B -->|Transit Trips| D[Transit Assignment]

    C --> E[Highway Network Loading]
    E --> F[Volume-Delay Functions]
    F --> G[Updated Travel Times]
    G -->|Feedback| C

    D --> H[Transit Network Loading]
    H --> I[Capacity Constraints]
    I --> J[Updated Transit Times]
    J -->|Feedback| D

    C --> K[Highway Skims]
    D --> L[Transit Skims]
    K --> M[Level-of-Service Feedback
to CT-RAMP]
    L --> M

Highway Assignment ¶

Highway Assignment Overview ¶

The highway assignment component simulates vehicle route choice on the road network, accounting for congestion effects and traveler preferences for time, cost, and convenience.

Highway Assignment Features ¶

Multi-Class Assignment: Supports different vehicle types with distinct characteristics:

Vehicle Class	Description	Network Access
Drive Alone (da)	Single-occupant vehicles	All facilities
Shared Ride 2 (sr2)	Two-person carpools	Includes HOV-2 lanes
Shared Ride 3+ (sr3)	Three+ person carpools	Includes HOV-3+ lanes
Very Small Trucks (vsm)	Light commercial vehicles	Most facilities
Small Trucks (sml)	Small commercial vehicles	Truck-restricted access
Medium Trucks (med)	Medium commercial vehicles	Truck-restricted access
Large Trucks (lrg)	Heavy commercial vehicles	Truck routes only

Equilibrium Algorithm: Uses Emme’s SOLA (Second Order Linear Approximation) algorithm for:

Traffic Assignment: Route choice based on generalized cost minimization
Convergence: Iterative process until stable flow patterns
Volume-Delay Functions: Dynamic link travel times based on traffic volumes

Network Representation ¶

Geographic Coverage: 9-county San Francisco Bay Area Network Size: ~180,000 directional links, ~85,000 nodes (approximate - varies by model version) Temporal Resolution: Five time periods (AM peak, PM peak, midday, evening, early AM)

Link Attributes:

Capacity: Maximum vehicle throughput
Free-Flow Speed: Uncongested travel speed
Volume-Delay Function: Congestion relationship
Facility Type: Freeway, arterial, local, ramp
Toll Information: Bridge tolls, express lane tolls
Vehicle Restrictions: HOV requirements, truck restrictions

Generalized Cost ¶

Highway assignment uses value of time to convert monetary costs to time-equivalent units:

Value of Time in Highway Assignment - Detailed documentation of VOT parameters and implementation

Volume Delay Functions - Mathematical specifications for link travel time calculations

Components:

Travel Time: Link-specific travel times (minutes)
Distance Cost: Operating cost × distance (cents/mile)
Toll Cost: Bridge tolls, express lane tolls (cents)
Value of Time Conversion: $18.93/hour (2010 dollars)

Transit Assignment ¶

Transit Assignment Overview ¶

The transit assignment component simulates passenger route choice on the public transportation network, including buses, rail, ferry, and multi-modal connections.

Transit Assignment Features ¶

Capacity-Constrained Assignment:

Initial Assignment: Unconstrained shortest path assignment
Capacity Evaluation: Check vehicle capacity limits
Crowding Effects: Increased perceived travel time for overcrowded vehicles
Reassignment: Iterative process with capacity constraints

Multi-Modal Network:

Mode Type	Examples	Special Considerations
Bus	AC Transit, Muni Bus, VTA	Frequency-based headways
Rail	BART, Caltrain, VTA Light Rail	Schedule-based operations
Ferry	Golden Gate, SF Bay Ferry	Weather and capacity constraints
Cable Car	San Francisco Cable Car	Tourist vs. commuter usage

Access/Egress Modes ¶

Walk Access: Direct walking to/from transit stops Drive Access: Park-and-ride facilities Kiss-and-Ride: Drop-off/pick-up access Bike Access: Bicycle access to transit (bike parking/bike-on-board)

Temporal Representation ¶

Service Periods: Coordinated with highway time periods Headway-Based: Frequent services represented by average wait times Schedule-Based: Lower frequency services with specific departure times

Level-of-Service Feedback ¶

Highway-Transit Integration ¶

Competitive Modes: Highway congestion affects transit competitiveness Park-and-Ride: Highway access costs influence transit ridership Cordon Pricing: Area-based tolls affect mode choice

CT-RAMP Integration ¶

Skim Generation: Assignment produces level-of-service matrices Accessibility Measures: Network performance influences activity location and mode choice Feedback Loops: Multi-iteration process between demand and assignment

Technical Implementation ¶

Software Platform ¶

Emme: Primary assignment platform

Highway: SOLA traffic assignment algorithm (confirmed in code)
Transit: Strategy-based transit assignment (uses OPTIMAL_STRATEGY)
Integration: Python API for TM2 coordination

TM2 Python Framework:

tm2py/components/
├── network/
│   ├── highway/           # Highway assignment components
│   │   ├── highway_assign.py
│   │   ├── highway_maz.py  
│   │   └── highway_emme_spec.py
│   └── transit/           # Transit assignment components
│       ├── transit_assign.py
│       ├── transit_skims.py
│       └── transit_capacity.py

Performance and Scalability ¶

Computational Requirements (estimated based on typical model performance):

Highway Assignment: ~15-30 minutes per time period (varies by network size and convergence criteria)
Transit Assignment: ~10-20 minutes per time period (varies by service complexity and capacity constraints)
Total Runtime: 2-4 hours for complete assignment set (including all time periods and iterations)
Memory Usage: 8-16 GB RAM recommended (depends on network size and number of zones)
Parallel Processing: Multi-core CPU utilization (configurable via emme.num_processors)

Validation and Calibration ¶

Highway Assignment Validation ¶

Traffic Counts: Comparison with observed link volumes Travel Time Validation: GPS-based travel time comparisons Toll Facility Usage: Express lane and bridge crossing validation

Transit Assignment Validation ¶

Ridership Counts: Operator-provided boardings data Load Profiles: Peak-direction, peak-period capacity utilization Transfer Patterns: Multi-operator journey validation

Configuration and Customization ¶

Assignment Parameters ¶

Convergence Criteria: Gap functions and iteration limits Volume-Delay Functions: Facility-specific congestion relationships
Value of Time: Mode and income-specific time valuations Capacity Constraints: Transit vehicle capacity and crowding effects

Scenario Analysis ¶

Infrastructure Projects: New facilities and capacity improvements Service Changes: Transit service modifications Pricing Policies: Tolling and fare policy evaluation Land Use Scenarios: Development pattern impacts

Technical Documentation ¶

Highway Value of Time - VOT parameters and calculations
Network Summary Component - Assignment result analysis
Configuration Guide - Assignment parameter setup

Model Integration ¶

CT-RAMP Overview - Demand model integration
Model Workflow - Complete model execution process
Outputs - Assignment result interpretation

Last Updated: November 2025
Model Version: Travel Model Two v2.2
Authors: MTC Staff & GitHub Copilot