基于多源数据的出行安全时空评价模型研究

Research on the spatial-temporal evaluation model of travel safety based on multi-source data

准确评估城市安全态势是保障居民出行安全的关键。针对现有的单一领域静态评价方法难以应对复杂多变的出行安全问题,考虑出行安全的时空相关特性,提出一种基于多源数据的出行安全时空评价模型。针对时间类特征的马尔科夫特性,构建基于条件随机场的时间域评价模型以模拟安全指数的时序相关性;针对空间维度中不同栅格之间的特征相关性和地理位置邻近性与安全指数之间复杂的非线性映射关系,构建基于神经网络的空间域评价模型以模拟安全指数的空间相关性。在模型训练阶段,考虑样本数据稀疏性问题,采用基于协同训练的半监督学习方法使2个模型相互迭代增强,共同作用得到最终评价结果。实验结果表明,该方法分类评价精确率达82.3%,召回率达70.4%,模型性能优于其他几种常用的分类算法。

Accurate evaluation of urban security status is an effective guarantee for residents’ travel safety. The existing single field static safety assessment method is difficult to deal with complex and varied travel security issues, considering the temporal smoothness and spatial correlation of the safety index, a spatial temporal evaluation model of travel safety based on multi-source data is proposed. Firstly, considering the Markov characteristic of the temporally-related features, the temporal evaluation model based on a conditional random field is proposed, involving temporally-related features to model the temporal dependency of safety index in a location. Then, a spatial evaluation model based on an improved BP neural network is proposed, which takes spatially-related features as input to model the spatial correlation between safety indexes of different locations. In the training phase, a semi-supervised learning method based on co-training is used to deal with the sparseness of sample data. In the prediction stage, the two evaluation models predict independently and then the final evaluation results are obtained by dynamic aggregation. Experiments show that the precision of this method is 82.3%, and the recall is 70.4%, so the advantage of the model over three well-known classification algorithms is examined.