Modeling Day-to-day Flow Dynamics on Degradable Transport Network

Area of Science:

• Transportation Science
• Network Dynamics
• Behavioral Economics

Background:

• Stochastic link capacity degradations are common in transport networks, leading to travel time variations.
• These variations significantly influence travelers' daily route choice behaviors.

Purpose of the Study:

• To formulate a deterministic dynamic model for day-to-day (DTD) flow evolution under degraded link capacities.
• To capture how travelers' study of uncertain travel times and choice of risky routes drive network flow dynamics.

Main Methods:

• Application of an exponential-smoothing filter to model travelers' perception of travel time variations.
• Formulation of a risk attitude parameter updating equation to reflect endogenous evolution of risk preferences.
• Theoretical analysis of the DTD model's mathematical properties (fixed point existence, uniqueness, stability, irreversibility).

Main Results:

• The proposed DTD model effectively captures flow evolution under capacity degradations.
• Numerical experiments validate the model's effectiveness and dynamic system properties.
• The study demonstrates how traveler learning and risk attitude influence network dynamics.

Conclusions:

• The developed DTD model provides a robust framework for analyzing transport network dynamics with capacity fluctuations.
• Understanding traveler behavior, including risk perception, is crucial for managing network performance.
• The model's findings have implications for network design and traffic management strategies.