Bi-objective optimization models for mitigating traffic congestion in urban road networks

Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world.In this context,this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation.Four congestion mitigation strategies are identified based on deviation and relative deviation of link volume from the corresponding capacity.Consequently,four biobjective mathematical programming optimal flow distribution(OFD)models are proposed.The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volumeto-capacity links compared to UE and SO models.Among the models,the system optimality with minimal sum and maximum absolute relative-deviation models(SO-SAR and SO-MAR)showed superior results for different performance measures.The SO-SAR model yielded 50%and 30%fewer links at higher link utilization factors than UE and SO models,respectively.Also,it showed more than 25%improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of1.04 compared to the other OFD and UE models.Conversely,the SO-MAR model yielded the least total distance and total system travel time,resulting in lower fuel consumption and emissions,thus contributing to sustainability.The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.

A Two-layer Framework for Mitigating the Con-gestion of Urban Power Grids Based on Flexible Topology with Dynamic Thermal Rating

The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load balancing.This situation is expected to worsen with the proliferation of renewable energy and electric vehicles.In this paper,a two-layer congestion mitigation framework is proposed,one which considers the congestion of the UPG with flexible topologies.In the upper-layer,the particle swarm optimization algorithm is employed to optimize the power supply distribution(PSD)of substation transformers.This is known as the upper-layer PSD.The lower-layer model recalculates the new PSD,known as the lower-layer PSD,based on the topology candidates.A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the smallest.This optimum topology is tested by standard power flow to ascertain its feasibility.The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal rating.The proposed framework is tested on a 56-node test system.Results show that the proposed framework can significantly reduce congestion,maintain safety margins,and determine the optimum transitioning sequence.