On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis
On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis
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DOI：
10.1016/j.tranpol.2020.05.023
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发表时间：
2020-11-01
期刊：
影响因子：
6.8
通讯作者：
Watanabe, Ryuki
中科院分区：
文献类型：
--
作者：
Chikaraishi, Makoto;Garg, Prateek;Watanabe, Ryuki
由于传统的短期流量预测模型（例如流量模拟器）中的芬太纳参数所需的成本和时间非常高，因此已经开发了预测模型，主要是用于管理经常性拥塞，而不是由灾难引起的非循环拥塞。机器学习模型是有希望在非转变拥塞期间进行交通预测的候选人，因为它们在没有APRIORI知识的情况下调整参数的能力，而其适用性很少探索。为了填补这一差距，本研究对运输网络中断期间各种机器学习模型的适用性进行了探索性分析，特别关注其预测交通状态的能力和结果的可解释性。该分析是通过使用大规模运输网络中断期间在广岛发生的大规模运输网络中断进行的数据进行的，这是由于大雨和随后的滑坡。测试的模型包括随机森林，支持向量机，Xgboost，浅馈送前馈神经网络和深层馈送神经网络。结果表明，就预测准确性而言，随机森林和XGBoost方法产生了最佳结果。另一方面，深度神经网络模型在结果的可解释性方面产生了更好的结果，即可以从现有的交通流理论的角度从逻辑上解释结果。这些发现表明，产生最佳预测准确性的模型并不总是最适合实际使用的模型，因为它不能模仿充血的机理。
Since the cost and time required to finetune parameters in traditional short-term traffic prediction models such as traffic simulators are very high, the prediction models have been developed mainly for managing recurrent congestion, rather than non-recurrent congestion caused, for example, by disaster. Machine learning models are promising candidates for traffic prediction during non-recurrent congestion due to their ability to tune parameters without a-priori knowledge, while their applicability to non-recurrent conditions has rarely been explored. To fill in this gap, this study conducts an exploratory analysis on the applicability of various machine learning models during a transportation network disruption with particular focuses on their ability to predict traffic states and the interpretability of the results. The analysis is conducted by using data obtained during the massive transport network disruption which occurred in Hiroshima in July 2018 due to heavy rain and subsequent landslides. The models tested include random forest, support vector machine, XGBoost, shallow feed-forward neural network, and deep feed-forward neural network. The results indicate that random forest and XGBoost methods produced the best results in terms of prediction accuracy. On the other hand, deep neural network models produce better results in terms of the interpretability of the results, i.e., the results can be logically explained from the perspective of existing traffic flow theory. These findings indicate that the model which produces the best prediction accuracy is not always the best for practical use since it does not mimic the mechanisms of congestion occurrence.