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Predicting Road Accidents Based on Current and Historical Spatio-temporal Traffic Flow Data

机译：基于当前和历史时空交通流数据的道路交通事故预测

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This paper presents research work towards a novel decision support system that predicts in real time when current traffic flow conditions, measured by induction loop sensors, could cause road accidents. If flow conditions that make an accident more likely can be reliably predicted in real time, it would be possible to use this information to take preventive measures, such as changing variable speed limits before an accident happens. The system uses case-based reasoning, an artificial intelligence methodology, which predicts the outcome of current traffic flow conditions based on historical flow data cases that led to accidents. This study focusses on investigating if case-based reasoning using spatio-temporal flow data is a viable method to differentiate between accidents and non-accidents by evaluating the capability of the retrieval mechanism, the first stage in a case-based reasoning system, to retrieve a traffic flow case from the case base with the same outcome as the target case. Preliminary results from experiments using real-world spatio-temporal traffic flow data and accident data are promising.
机译：本文介绍了针对新型决策支持系统的研究工作，该系统实时预测由感应回路传感器测量的当前交通流量状况可能导致道路交通事故的发生。如果可以实时可靠地预测出更可能发生事故的流量条件，则可以使用此信息采取预防措施，例如在事故发生之前更改变速极限。该系统使用基于案例的推理（一种人工智能方法），可以基于导致事故的历史流量数据案例来预测当前交通流状况的结果。这项研究的重点是调查通过使用时空流数据进行基于案例的推理是否是一种可行的方法，通过评估基于案例的推理系统中第一阶段的检索机制的能力来区分事故和非事故。来自案例库的流量案例，其结果与目标案例相同。使用真实的时空交通流数据和事故数据进行实验的初步结果是有希望的。

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