Yunran Di, Weihua Zhang, Haotian Shi, Heng Ding, Jinbiao Huo, Bin Ran
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引用次数: 0
Abstract
With the advancement of urbanization, cities are constructing expressways to meet complex travel demands. However, traditional link‐based road network design methods face challenges in addressing large‐scale expressway network design problems. This study proposes an expressway network design method tailored for multi‐subregion road networks. The method employs the macroscopic fundamental diagram to model arterial dynamics and the cell transmission model to capture expressway dynamics. A stochastic user equilibrium model is further established for route choice, and a decision model is developed to minimize total time spent. Simulations show that new expressways alleviate network congestion, with significant effects in the initial stages. Moreover, route guidance strategies and driver compliance also influence the schemes.
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.