Predicting traffic demand during hurricane evacuation using Real-time data from transportation systems and social media
Predicting traffic demand during hurricane evacuation using Real-time data from transportation systems and social media
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DOI：
10.1016/j.trc.2021.103339
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发表时间：
2021-10
影响因子：
8.3
通讯作者：
K. Roy;Samiul Hasan;A. Culotta;Naveen Eluru
中科院分区：
文献类型：
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作者：
K. Roy;Samiul Hasan;A. Culotta;Naveen Eluru
最近，飓风马修，哈维和伊尔玛已经扰乱了美国多个州数百万人的生活。在飓风疏散下，高效的交通运营可以最大限度地利用交通基础设施，减少疏散时间和大规模拥堵造成的压力。疏散交通预测对于规划有效的交通管理策略至关重要。然而，由于疏散参与的复杂性和动态性，预测疏散交通需求远提前于实际疏散是一个非常具有挑战性的任务。来自各种来源的实时信息可以显著帮助我们可靠地预测疏散需求。在这项研究中，我们使用交通传感器和Twitter的数据在飓风马修和厄玛预测交通需求在疏散更长的预测范围（大于1小时）。我们提出了一种使用长短期记忆神经网络（LSTM-NN）的机器学习方法，该方法使用输入特征和预测范围的不同组合在飓风疏散（飓风Irma和Matthew）期间对真实世界的交通数据进行了训练。我们将我们的预测结果与基线预测和现有的机器学习模型进行比较。结果表明，该模型可以很好地预测疏散期间的交通需求提前24小时。建议的LSTM-NN模型可以显着有利于未来的疏散交通管理。
In recent times, hurricanes Matthew, Harvey, and Irma have disrupted the lives of millions of people across multiple states in the United States. Under hurricane evacuation, efficient traffic operations can maximize the use of transportation infrastructure, reducing evacuation time and stress due to massive congestion. Evacuation traffic prediction is critical to plan for effective traffic management strategies. However, due to the complex and dynamic nature of evacuation participation, predicting evacuation traffic demand long ahead of the actual evacuation is a very challenging task. Real-time information from various sources can significantly help us reliably predict evacuation demand. In this study, we use traffic sensor and Twitter data during hurricanes Matthew and Irma to predict traffic demand during evacuation for a longer forecasting horizon (greater than 1 h). We present a machine learning approach using Long-Short Term Memory Neural Networks (LSTM-NN), trained over real-world traffic data during hurricane evacuation (hurricanes Irma and Matthew) using different combinations of input features and forecast horizons. We compare our prediction results against a baseline prediction and existing machine learning models. Results show that the proposed model can predict traffic demand during evacuation well up to 24 h ahead. The proposed LSTM-NN model can significantly benefit future evacuation traffic management.