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Roadway Analysis

Evaluation and analysis of roadway demand and system performance.
Stable, Reliable, and Robust

TOVA fully implements proportionality conditions to ensure stable and reliable traffic-assignment results.

Whether Proportionality
conditions are met
Traffic Assignment
Analysis
Reliability
accuracy of the model

The reliability of roadway traffic-assignment results depends on both model accuracy
and the satisfaction of proportionality conditions.

TOVAensures full compliance with proportionality conditions
by applying a Balancing Process and Warm-Start mechanism.
  • All-or-Nothing Assignment
  • Incremental Assignment
  • Equilibrium Assignment
  • Frank-Wolfe Algorithm
  • Steepest Origin-Based
    Algorithm(SOBA)
Traffic Assignment
Models
Equilibrium
Assignment
Algorithms
Warm-Start
SOBA enhances model
precision and
delivers optimal
convergence for the
Perfect Solution.
Frog-Jump elimination
through implementation
of the Warm-Start
function
Application of SOBA (Steepest Origin-Based Algorithm)
  • Significantly improved accuracy and processing speed compared to conventional software
  • Reduced analysis time, enhancing overall efficiency in traffic-demand analysis
  • Customizable editing and convenient modification tools for each analysis target
  • Efficient and diverse path-analysis capabilities based on Bush-File methodology
Performance comparison (execution time and usability) between roadway assignment software using metropolitan-scale datasets
Warm-Start Implementation
  • Uses previously executed assignment results as initial values to improve execution speed and produce more stable assignment outputs.
  • This ensures stable benefit evaluations across different scenarios during project implementation.
Comparison of execution time between
TOVA’s Warm-Start and Cold-Start.
Comparison of benefit-calculation results
by software used in domestic road-investment projects.
Frog-Jump Elimination
  • Frog-Jump : abnormal traffic shifts appearing in areas unrelated to a project
  • Main cause of unstable traffic assignment result caused by model inaccuracy or inconsistent inputs.
TOVA removes frog‑jump effects using warm‑start and precision improvements, minimizing irregular volume swings for accurate assignment result

Reduces increasesdecreases

Frank-Wolfe (Relative-gap : 10⁻¹⁴ convergence)
SOBA (Relative-gap : 10⁻¹² convergence)
Failure to satisfy proportionality conditions
significantly reduces the reliability of
four-step traffic-demand forecasts and benefit-analysis results.
For all origins, destinations, and vehicle types, traffic-volume ratios across PAS (Pair of Alternative Segments) must remain consistent

If proportionality is not satisfied, certain routes may receive disproportionately high volumes from specific vehicle types, causing Frog-Jump effects and reducing the reliability of forecast and benefit-analysis results.

PAS(Lake Shore Drive, Chicago)
TOVA Proportionality Implementation
Results
Therefore, Therefore, In ,
traffic-assignment analysis

does not require defining an influence area related to the project site.