Heliyon (Jul 2024)
Understanding state-of-the-art situation of transport planning strategies in earthquake-prone areas by using AI-supported literature review methodology
Abstract
Aim: This review aims to explore earthquake-based transport strategies in seismic areas, providing state-of-the-art insights into the components necessary to guide urban planners and policymakers in their decision-making processes. Outputs: The review provides a variety of methodologies and approaches employed for the reinforcement planning and emergency demand management to analyze and evaluate the impact of seismic events on transportation systems, in turn to develop strategies for preparedness, mitigation, response, and recovery phases. The selection of the appropriate approach depends on factors such as the specific transport system, urbanization level and type, built environment, and critical components involved. Originality and value: Besides providing a distinctive illustration of the integration of transportation and seismic literature as a valuable consolidated resource, this article introduces a novel methodology named ALARM for conducting state-of-the-art reviews on any topic, incorporating AI through the utilization of large language models (LLMs) built upon transformer deep neural networks, along with indexing data structures (in this study mainly OPEN-AI DAVINCI-003 model and vector-storing index). Hence, it is of paramount significance as the first instance of implementing LLMs within academic review standards. This paves the way for the potential integration of AI and human collaboration to become a standard practice under enhanced criteria for comprehending and analyzing specific information.
