  Roadway reconfiguration is a crucial aspect of transportation planning,
aiming to enhance traffic flow, reduce congestion, and improve overall road
network performance with existing infrastructure and resources. This paper
presents a novel roadway reconfiguration technique by integrating optimization
based Brute Force search approach and decision support framework to rank
various roadway configurations for better performance. The proposed framework
incorporates a multi-criteria decision analysis (MCDA) approach, combining
input from generated scenarios during the optimization process. By utilizing
data from optimization, the model identifies total betweenness centrality
(TBC), system travel time (STT), and total link traffic flow (TLTF) as the most
influential decision variables. The developed framework leverages graph theory
to model the transportation network topology and apply network science metrics
as well as stochastic user equilibrium traffic assignment to assess the impact
of each roadway configuration on the overall network performance. To rank the
roadway configurations, the framework employs machine learning algorithms, such
as ridge regression, to determine the optimal weights for each criterion (i.e.,
TBC, STT, TLTF). Moreover, the network-based analysis ensures that the selected
configurations not only optimize individual roadway segments but also enhance
system-level efficiency, which is particularly helpful as the increasing
frequency and intensity of natural disasters and other disruptive events
underscore the critical need for resilient transportation networks. By
integrating multi-criteria decision analysis, machine learning, and network
science metrics, the proposed framework would enable transportation planners to
make informed and data-driven decisions, leading to more sustainable,
efficient, and resilient roadway configurations.
