Traffic assignment is used for infrastructure planning, based on metrics like total system travel time (TSTT), vehicle-miles traveled (VMT) and link or path flows. Algorithms for traffic assignment converge to an equilibrium solution over multiple iterations, but these metrics converge at different rates. Current guidance indicates that freeway link flows stabilize at a relative gap of roughly ${10}^{-4}$. This study generalizes this guidance by testing additional networks and metrics, in more experimental settings. Our results reveal that aggregate metrics (VMT and TSTT) stabilize earlier (relative gap ${10}^{-4}$) than link flows (relative gap ${10}^{-5}$), which in turn stabilize slightly before the set of most likely used paths and flows on these paths (relative gap ${10}^{-6}$). These results are stable across the TAPAS and Algorithm B methods for solving assignment. Our results also show strong linear correlations between alternative gap measures, allowing for the translation of stabilization results across other gap definitions as well.

We consider a budget-constrained rail network electrification problem with associated changes in costs of energy usage (via path gradient and curvature), operations, and long-term maintenance. In particular, we consider that freight flows on such a network form a user equilibrium. Interactions between electric and diesel trains on the same corridor are represented with nonseparable link performance functions that nevertheless have a symmetric Jacobian. This bilevel formulation is solved for the North American railroad network using a genetic algorithm (GA), incorporating domain-specific insights to reduce the number of solutions that must be considered. We analyze solution characteristics and decision-making implications. Results show that broad connectivity would be beneficial for the most impact. Increasing demand shifts electrified corridors toward the more populous east and gulf coasts, while increased operational costs results in the electrification of routes through mountainous terrains.

We consider a budget constrained rail network electrification problem with associated changes in costs of energy usage (via path gradient and curvature), operations, and long-term maintenance. In particular, we consider that freight flows on such a network form a user equilibrium. Interactions between electric and diesel trains on the same corridor are represented with nonseparable link performance functions, which nevertheless have a symmetric Jacobian. This bi-level formulation is solved for the North American railroad network using a genetic algorithm (GA), incorporating domain-specific insights to reduce the number of solutions which must be considered. We analyze solution characteristics and decision-making implications. Results show that broad connectivity would be beneficial for most impact. Increasing demand shifts electrified corridors towards the more populous east and gulf coasts, while increased operational costs results in electrification of routes through mountainous terrains.