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Effects of Future Connected Autonomous Vehicles on Freeway Congestion Using Fuzzy Cognitive Mapping

机译:未来连接自动车辆对使用模糊认知映射的高速公路拥堵的影响

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Continuing population growth and urbanization are projected to add 2.5 billion people to the world's urban population by 2050 [1]. It is evident that this will increase traffic congestion especially in the urban areas, which will bring economic, safety, environmental and quality of life challenges. There are various organizations looking for possible solutions to reduce the impact of future congestion by long term planning [2], most of these studies don't take into account emergence of disruptive technologies. The concept of vehicles with autonomous driving and online connectivity capabilities, namely, connected autonomous vehicles (CAVs) is an emerging technology [3] which may contribute to the solution of this problem through adoption. This paper aims to shed light on effect of different levels of CAV adoption on congestion through scenario planning with fuzzy cognitive mapping. Different future scenarios on CAV adoption based on research and development being done on CAV technology [3] are run through a fuzzy cognitive model of congestion developed through detailed literature review. Results indicate CAV adoption provides an opportunity for reducing congestion. Therefore suggesting, investing in CAV enabling upgrades of existing roads, and giving incentives for CAV adoption, is a viable option for city planners' and local governments' project portfolios to reduce congestion.
机译:预计持续人口增长和城市化将预计将在2050年下载世界城市人口25亿人口[1]。很明显,这将增加交通拥堵,特别是在城市地区,将带来经济,安全,环境和生活质量挑战。有各种组织正在寻找可能的解决方案,以减少长期规划的未来拥塞的影响[2],大多数研究没有考虑破坏性技术的出现。具有自主驾驶和在线连接能力的车辆的概念,即连接的自治车辆(CAVE)是一种新兴技术[3],这可能通过采用来解决这个问题的解决方案。本文旨在通过模糊认知映射通过场景规划对不同级别的CAVE采用效果的阐明。基于研究和开发的CAVE采用的不同未来情景在CAV技术[3]中通过详细文献综述开发的拥堵模糊认知模型进行。结果表明CAV采用为减少拥堵提供了机会。因此,暗示,投资CAV升级现有道路,并为CAVE采用提供激励,是城市规划者和地方政府项目组合减少拥堵的可行选择。

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