Evaluating impacts of coastal flooding on the transportation system using an activity-based travel demand model: a case study in Miami-Dade County, FL
Evaluating impacts of coastal flooding on the transportation system using an activity-based travel demand model: a case study in Miami-Dade County, FL
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DOI：
10.1007/s11116-021-10172-w
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发表时间：
2021-01
期刊：
影响因子：
4.3
通讯作者：
Yu Han;Changjie Chen;Z. Peng;Pallab Mozumder
中科院分区：
文献类型：
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作者：
Yu Han;Changjie Chen;Z. Peng;Pallab Mozumder
最近的气候灾害显示了交通基础设施在自然灾害面前的脆弱性。为了了解沿海灾害对城市出行活动的影响，本研究提出了一种基于活动的建模方法来评估海平面上升下迈阿密-戴德县风暴潮对交通网络的影响。在模型模拟中，采用基于马尔可夫链蒙特卡罗（MCMC）算法生成人口属性和出行日志。2045年的洪水情景是基于百年一遇风暴潮的不同适应标准，人口预测来自土地使用冲突识别策略（LUCIS）模型。我们的分析表明，当选择低水平适应标准时，大约29.3%的交通基础设施将在风暴潮下遭到破坏，包括美国1号高速公路，县南部和西南部的道路以及连接迈阿密海滩地区的桥梁。然而，高水平的适应标准将使脆弱基础设施减少到12.4%。此外，在早高峰时段，低水平适应标准增加的总出行时间可能高达高水平适应标准的两倍。我们的模型结果还显示，由于未来风暴潮的破坏，每次旅行平均增加的旅行时间在14.2到62.8分钟之间。
Recent climatic disasters have shown the vulnerability of transportation infrastructures against natural hazards. To understand the risk of coastal hazards on urban travel activities, this study presents an activity-based modeling approach to evaluate the impacts of storm surge on the transportation network under sea-level rise in Miami-Dade County, FL. A Markov-Chain Monte Carlo (MCMC) based algorithm is applied to generate population attributes and travel diaries in the model simulation. Flooding scenarios in 2045 are developed based on different adaptation standards under the 100-year storm surge and population projections are from the land-use conflict identification strategy (LUCIS) model. Our analysis indicates that about 29.3% of the transportation infrastructure, including areas of the US No. 1 highway, roadways in the south and southwest of the county, and bridges connecting Miami Beach area, will be damaged under the storm surge when a low-level adaptation standard is chosen. However, the high-level adaptation standard will reduce the vulnerable infrastructures to 12.4%. Furthermore, the total increased travel time of the low-level adaptation standard could be as high as twice of that in the high-level adaptation standard during peak morning hours. Our model results also reveal that the average increased travel time due to future storm surge damage ranges between 14.2 and 62.8 min per trip.