A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks

• URL: http://arxiv.org/abs/2401.07371v1
• Date: Sun, 14 Jan 2024 21:22:22 GMT
• Title: A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks
• Authors: H M Imran Kays, Khondhaker Al Momin, K.K. "Muralee" Muraleetharan, Arif Mohaimin Sadri
• Abstract summary: This paper presents a novel roadway reconfiguration technique by integrating optimization based Brute Force search approach and decision support framework. The proposed framework incorporates a multi-criteria decision analysis approach, combining input from generated scenarios during the optimization process. To rank the roadway configurations, the framework employs machine learning algorithms, such as ridge regression, to determine the optimal weights for each criterion.
• Score: 0.5154704494242526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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