2 research outputs found
A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks
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.Comment: 103rd Transportation Research Board (TRB) Annual Meetin
Spatio-temporal Correlations of Betweenness Centrality and Traffic Metrics
Graph-based analysis has proven to be a good approach to study topological vulnerabilities of road networks through specific metrics, such as betweenness centrality (BC). Even though BC of unweighted, undirected graphs has been widely adopted to identify critical road segments and intersections, given the very high number of potentially highly-traversed paths flowing through them, congestion and vulnerability are strongly influenced also by static and dynamic context factors, such as road capacity, speed limits, travellers' behaviors, accidents, social gatherings and maintenance operations. In this paper, we focus on the analysis of BC on dynamically weighted graphs, used as a model of a road network and associated dynamic information (e.g. travel time). The aim is to discover correlations between the centrality metric and vehicle flows, both in space and in time. The analysis proves the existence of relevant spatio-temporal correlations that provide useful information about the characteristics of road networks and the behavior of drivers. In particular, we identify the existence of anti-correlations that point out forecasting properties of BC when computed on dynamic graphs.These properties justify the usage of the metric for the implementation of next-generation proactive, data-driven urban monitoring systems. These systems are expected to empower urban planners and traffic operators with novel intelligent solutions to reduce traffic congestion and vulnerability risks, therefore contributing to implement the vision of a more resilient and sustainable city