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Driver route and destination prediction

机译：驾驶员路线和目的地预测

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摘要

A method is proposed for estimating driver's intended route and destination. Probabilistic Bayesian models are employed to analyze the history of driving for individuals, where data attributes are GPS traces captured during trips from fleet of cars. The proposed probabilistic model is built up in the road graph level which is associated with its corresponding destination/origin and additional data describing characteristics of each trip. The proposed prediction model is built upon destination clustering [1]. To avoid overfitting of the predictive model for multiple destinations corresponding to the same physical location, we use a modified DBSCAN method to cluster the destinations. Low computational complexity, flexibility, and simplicity of the proposed algorithms that can be adapted and trained with time series data are the main advantages of our predictive model. Preliminary results evaluated for the destination prediction and short range path prediction indicate the accuracy and reliability of the proposed method.
机译：提出了一种用于估计驾驶员的预期路线和目的地的方法。贝叶斯概率模型用于分析个人驾驶的历史，其中数据属性是在从车队出行期间捕获的GPS轨迹。所提出的概率模型建立在路线图级别上，该路线图级别与其相应的目的地/原点以及描述每个行程特征的其他数据相关联。所提出的预测模型基于目标聚类[1]。为了避免对与同一物理位置相对应的多个目的地的预测模型过度拟合，我们使用一种经过修改的DBSCAN方法对这些目的地进行聚类。可以使用时间序列数据进行调整和训练的拟议算法的低计算复杂性，灵活性和简单性是我们预测模型的主要优势。评估的目的地预测和短距离路径预测的初步结果表明了该方法的准确性和可靠性。

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