Searching for parking is a significant contributor to urban road congestion leading to additional costs for the driver emerging from the increased time spent traveling and fuel consumption. The present work attempts to model the duration for searching for parking space monitored with smartphone sensing using the widespread parametric and semi-parametric survival models, as well as random survival forests and deep learning survival models. The available dataset consists of more than 48,000 driving trips conducted in the Region of Attica, Greece, and is enriched with exogenous variables, such as population density and land use in each trips’ destination area. Findings reveal that the time of day in which the trip was performed, as well as trip duration and length, significantly affect parking searching duration. In addition, the land use of the destination area appears to be a significant factor for predicting parking searching duration. Although all survival models share similar results in terms of the significance of the parameters, deep survival neural networks noticeably improve the survival time predictions.

寻找停车位是造成城市道路拥堵的重要原因，这导致驾驶员因花费更多的旅行时间和燃料消耗而产生额外的费用。本工作尝试使用广泛的参数和半参数生存模型，以及随机生存森林和深度学习生存模型，对通过智能手机感应监控的停车位搜索的持续时间进行建模。可用的数据集包括在希腊阿提卡地区进行的48,000多次驾车旅行，并且丰富了外在变量，例如每次旅行的目的地区域的人口密度和土地使用。研究结果表明，一天中旅行的时间以及旅行的持续时间和时长会显着影响停车搜索的持续时间。此外，目的区域的土地使用似乎是预测停车搜索持续时间的重要因素。尽管就参数的重要性而言，所有生存模型都具有相似的结果，但是深度生存神经网络显着改善了生存时间的预测。