Effective parking policy is essential for cities to reduce the demand their road networks experience and to combat their carbon footprints. Existing research in the application of machine learning to understand parking behavior assumes that cities have prohibitively expensive stationary parking sensors installed, while no research has yet attempted to use machine learning to impute for parking behavior using mobile probe data of sparsely monitored areas. To this end, this paper shows that it is indeed feasible to impute parking pressure (occupation as a percentage). Gradient boosted trees were found to perform the best with an R² score of 0.20 and root mean squared error (RMSE) score of 0.087. This paper also found that three unique parking occupancy patterns exist in Amsterdam and that this information, in combination with neighborhood characteristics, has an impact on imputation under certain conditions.

有效的停车政策对于城市减少其道路网络的需求和减少碳足迹至关重要。现有的应用机器学习来理解停车行为的研究假设城市安装了昂贵的固定停车传感器，而还没有研究尝试使用机器学习来使用监测稀疏区域的移动探测数据来估算停车行为。为此，本文表明推算停车压力（占用率）确实是可行的。发现梯度提升树在 R ^{2 下}表现最好得分为 0.20，均方根误差 (RMSE) 得分为 0.087。本文还发现阿姆斯特丹存在三种独特的停车位占用模式，这些信息与邻里特征相结合，在某些条件下对插补产生影响。