Predicting Road Accidents Based on Current and Historical Spatio-temporal Traffic Flow Data
Predicting Road Accidents Based on Current and Historical Spatio-temporal Traffic Flow Data
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基于当前和历史时空交通流数据预测道路事故
DOI：
10.1007/978-3-642-41019-2_7
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发表时间：
2013
影响因子：
5.3
通讯作者：
Matthew Roberts
中科院分区：
文献类型：
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作者：
Rupa Jagannathan;S. Petrovic;G. Powell;Matthew Roberts
本文介绍了一种新的决策支持系统的研究工作，该系统可以实时预测当前的交通流状况，由感应环路传感器测量，可能导致道路事故。如果能够可靠地实时预测导致事故发生的流动状况，就有可能利用这些信息采取预防措施，例如在事故发生前改变可变速度限制。该系统使用基于案例的推理，这是一种人工智能方法，可以根据导致事故的历史流量数据案例预测当前交通流量状况的结果。本研究的重点是通过评估检索机制（基于案例推理系统的第一阶段）从案例库中检索具有与目标案例相同结果的交通流案例的能力，来研究使用时空流量数据的基于案例推理是否是区分事故和非事故的可行方法。使用真实时空交通流数据和事故数据进行的初步实验结果是有希望的。
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.
基于案例的推理研究与开发
DOI：
10.1007/978-3-642-39056-2_11
发表时间：
2013
期刊：
--
影响因子：
--
作者：
Horsburgh B
通讯作者：
Horsburgh B