Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction
Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction
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DOI：
10.1177/0361198120917668
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发表时间：
2020-05
影响因子：
1.7
通讯作者：
Weiran Yao;Sean Qian
中科院分区：
文献类型：
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作者：
Weiran Yao;Sean Qian
在本文中要解决的主要问题是建议最佳的信号配时计划在真实的时间下的事件，结合领域知识开发的交通信号配时计划调整为可能的事件，并从交通和实施信号配时的历史数据进行学习。交通事故管理的有效性往往受到交通运营商延迟响应时间和过度工作量的限制。本文提出了一种新的决策框架，从数据和领域知识学习实时推荐应急信号计划，以适应非经常性的交通，从实时交通预测的输出至少提前30分钟。具体而言，考虑到罕见的事件的应急信号计划的参与，建议分解成两个层次模型实时交通预测和计划关联的端到端的推荐任务。这两个模型之间的联系是通过度量学习来学习的，这加强了从历史信号参与记录中观察到的偏序偏好。通过在美国宾夕法尼亚州蔓越莓镇的交通网络上对该框架进行测试，证明了该方法的有效性，2019年实验结果表明，该推荐系统在测试计划上的准确率为96.75%，召回率为87.5%，推荐时间平均比Waze警报提前22.5分钟。结果表明，这个框架是能够给交通运营商一个显着的时间窗口，以访问的条件，并作出适当的反应。
The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing. The effectiveness of traffic incident management is often limited by the late response time and excessive workload of traffic operators. This paper proposes a novel decision-making framework that learns from both data and domain knowledge to real-time recommend contingency signal plans that accommodate non-recurrent traffic, with the outputs from real-time traffic prediction at least 30 min in advance. Specifically, considering the rare occurrences of engagement of contingency signal plans for incidents, it is proposed to decompose the end-to-end recommendation task into two hierarchical models—real-time traffic prediction and plan association. The connections between the two models are learnt through metric learning, which reinforces partial-order preferences observed from historical signal engagement records. The effectiveness of this approach is demonstrated by testing this framework on the traffic network in Cranberry Township, Pennsylvania, U.S., in 2019. Results show that the recommendation system has a precision score of 96.75% and recall of 87.5% on the testing plan, and makes recommendations an average of 22.5 min lead time ahead of Waze alerts. The results suggest that this framework is capable of giving traffic operators a significant time window to access the conditions and respond appropriately.