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首页> 外文会议>AIIT International Congress on Transport Infrastructure and Systems >Back-propagation neural networks and generalized linear mixed models to investigate vehicular flow and weather data relationships with crash severity in urban road segments
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Back-propagation neural networks and generalized linear mixed models to investigate vehicular flow and weather data relationships with crash severity in urban road segments

机译：背部传播神经网络和广义线性混合模型，调查城市道路段碰撞严重程度的车辆流量和天气数据关系

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The paper deals with the identification of variables and models that can explain why a certain Severity Level (SL) may be expected in the event of a certain type of crash at a specific point of an urban road network. Two official crash records, a weather database, a traffic data source, and information on the characteristics of the investigated urban road segments of Turin (Italy) for the seven years from 2006 to 2012 were used. Examination of the full database of 47,592 crash events, including property damage only crashes, reveals 9,785 injury crashes occurring along road segments only. Of these, 1,621 were found to be associated with a dataset of traffic flows aggregated in 5 minutes for the 35 minutes across each crash event, and to weather data recorded by the official weather station of Turin. Two different approaches, a back-propagation neural network model and a generalized linear mixed model were used. Results show the impact of flow and other variables on the SL that may characterize a crash; differences in the significant variables and performance of the two modelling approaches are also commented on in the manuscript.
机译：本文涉及可以解释为什么在城市道路网络特定点的特定类型的崩溃情况下可以预期某个严重性级别（SL）的变量和模型。从2006年到2012年，使用了两个官方碰撞记录，天气数据库，交通数据来源和有关调查城市公路段的特征的特征。审查47,592次碰撞事件的完整数据库，包括财产损失仅崩溃，揭示了9,785次沿着道路段发生的伤害崩溃。其中，发现1,621个与在每次碰撞事件的35分钟内在5分钟内聚合的交通流量数据集，以及都灵的官方气象站记录的天气数据。使用两种不同的方法，使用反向传播神经网络模型和广义的线性混合模型。结果显示流量和其他变量对崩溃的影响;在手稿中也发表评论了两个建模方法的显着变量和性能的差异。

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