Simultaneous optimization of road intervention and overloading penalty policies for user cost minimization

This research develops a framework to simultaneously optimize road pavement intervention and overloading penalty policies to minimize total user costs. Possible road user route choices from origin to destination (OD) are modeled using a stochastic user equilibrium (SUE). Specifically, truck route choice subject to SUE in which trucks face a penalty if they exceed the acceptable weight limit on faster routes or endure longer routes at no extra charge is looked at. A pavement intervention model is built to propose multiple road pavement intervention (condition improvement) decisions and overloading policies at discrete time points over a finite time horizon. Near–optimal decisions and policies for the entire road network are determined concurrently and heuristically using simulated annealing (SA). Despite generating a near–optimal solution, SA facilitates solving the complex non–differentiable problem involving agency actions (network improvements and overloading penalties) and user reactions (comply or change route). The framework presented in this article seeks to aid multifaceted user and agency decision–making amidst multiple constraints simultaneously. Its applicability is demonstrated through an empirical study on selected OD pairs along interurban routes between the Ugandan capital, Kampala, and regional urban centers.