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A unique deep learning approach for accident prediction

Chunguang (Wayne) Zhang

This post is a unique empirical study using ConvLSTM deep-learning model and ArcPy to predict next-day crash risk locations with time sequences of crash feature data. The traffic crashes are formulated as a spatial-temporal sequence forecasting problem in which both the input and the prediction data are spatial-temporal sequences. The convolutional LSTM (ConvLSTM) approach is to build an end-to-end trainable model for the crash prediction. The result has shown that the ConvLSTM network can capture spatial-temporal correlations of traffic accidents when and where happening.

Problems: Traffic accidents lead to severe human injuries and casualties and huge economic losses. The ability to predict the risk of traffic accidents in a spatial-temporal context is important to prevent the occurrence of accidents not only for public citizens but also government officials. However, it is a very challenging task to predict traffic accidents not only the causes of multiple factors e.g. human, time, geometric and environmental but also the rare factors and sparse data sets. Traditional accident prediction commonly applies statistic regression such as Poisson, Negative Binomial (NB) and multivariate regression. But they often fail when dealing with complex and highly nonlinear data such as spatial-temporal correlations of the accidents e.g. when, where and why. There are classic machine learning approaches e.g. XGBoost, SVM and RandomForest classifiers which engineer features into the models to seek feature importance for the probability. We are talking about features in machine learning which are the arrays of numbers in multi dimensional space. To know more about feature engineering in Machine Learning, please read one of the best stories from Daniel Wilson.

Moreover, time and seasonality can play an important role in deciding the probability of accidents of when and where. Using ArcPy in ArcGIS Pro, we can aggregate crash locations in certain time windows. Click to see examples of crash locations in 3D scenes.

Solution: So, can we apply the classic machine learning models to search important causal features and then propose a practical deep-learning network to predict when and where the accidents happen? The answer is yes. The ConvLSTM model is one of the most interesting deep-learning models that is used to predict next-frame video or image. The original research was done for Precipitation…

Continue reading: https://towardsdatascience.com/spatial-temporal-convlstm-for-crash-prediction-411909ed2cfa?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com