Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear relationships between traffic accident characteristics and land use properties, which are further interpreted to form local and general rules. More variables are taken into account as the input land use properties and the output traffic accident characteristics. Five types of traffic accident characteristics are simultaneously predicted with higher accuracy, and three levels of interpretation, including the hidden factor-traffic potential, the potential-determine factors, which varies between grid cells, and the general rules across the whole study area are analyzed. Based on the model, some interesting insights were revealed including the division line in the potential traffic accidents in Shenyang (China). It is also purposed that the relationship between land use and accidents differ from previous researches in the neighboring and regional aspects. Neighboring grids have strong spatial connections so that the relationship of accidents in a continuous area is relatively similar. In a larger region, the spatial location is found to have a great influence on the traffic accident and has a strong directionality.
The prediction of trip generation is an essential problem for effective traffic engineering and urban management. Traditional methods are on the large spatial scale (e.g. Traffic analysis Zone, TAZ), based on the single source and fewer types data. It is difficult to carry out refined research on smaller spatial units, due to the high aggregation of personal trip survey data. In addition, the experience-based models cannot easily capture complex non-linear relationship, which leads to lower accuracy. Multi-sources data provides the possibility to improve the prediction accuracy of trip generation. Based on the point of interest data (POI), more disaggregate spatial unit are subdivided, and grid-scale spatial correlations are taken into consideration. This paper proposes a Convolutional Neural Network-Multidimensional Long-short term memory neural network (CNN-MDLSTM) model to analyze the spatial correlation between trip generation and land use features, capture prominent features in a spatial range through the convolution structure and describe the spatial interaction using the sequence transfer structure of Long-short term memory neural network (LSTM). The deformed Multi-Dimensional Long-Short Term Memory neural network (MDLSTM) is used to adapt to the two-dimensional spatial relationship. Through case analysis and comparative analysis between models, it is shown that CNN-MDLSTM characterizes the quantitative law of trip generation and land use features better than the other neural network models. In addition, this study also discusses the output prediction accuracy of different land use grid cells and the impact of different land use characteristics on the prediction accuracy.
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