This study investigates the potential of link-and path-based incentives to mitigate congestion in urban transportation networks. Both incentive schemes are formulated as non-linear optimisation problems with complementarity constraints. Mathematically, it is demonstrated that the feasible region of the link-based model is a subset of the feasible region of the path-based model. Consequently, path-based incentives exhibit greater potential for shifting the user equilibrium flow pattern toward system optimum compared to link incentives. A column generation-based iterative solution technique, which generates new paths at each iteration, is devised to efficiently solve both optimisation problems. Numerical experiments conducted for various transport networks also highlight the superiority of path-based incentives in reducing total travel time in urban transportation networks.@en

This study investigates the potential of link-and path-based incentives to mitigate congestion and reduce emissions in urban transportation networks. Both incentive schemes are formulated as non-linear optimisation problems with complementarity constraints. Mathematically, it is demonstrated that the feasible region of the link-based model is a subset of the feasible region of the path-based model. Consequently, path-based incentives exhibit a higher potential in pushing the user equilibrium flow pattern toward system optimum, compared to link incentives. A column generation-based iterative solution technique, which generates new paths at each iteration, is devised to efficiently solve both optimisation problems. The numerical results in the Sioux Falls network also highlight the superiority of path-based incentives in reducing total travel time and emissions in urban transportation networks.@en

This study investigates the potential of path-based incentives to mitigate congestion and reduce emissions in urban transport networks using a multi-objective optimisation problem with a budget limit. A column generation-based solution technique is developed that finds a new path between each origin-destination pair at each iteration, and stops when the objective value does not change more than a threshold at two consecutive iterations. Three different scenarios are defined based on the objective function: minimising total travel time (TTT), total emissions (TE), and integrated minimisation of both. Numerical results in the Sioux Falls network show that TTT and TE are conflicting objectives under our modeling assumptions: improving one worsens the other. Nonetheless, the integrated scenario demonstrates the capacity to harmonize both objectives, thereby achieving a reduction in both TTT and TE.@en