Spot-level parking availability information (the availability of each spot in a parking lot) is in great demand, as it can help reduce time and energy waste while searching for a parking spot. In this article, we propose a crowdsensing system called SpotE that can provide spot-level availability in a parking lot using drivers’ smartphone sensors. SpotE only requires the sensor data from drivers’ smartphones, which avoids the high cost of installing additional sensors and enables large-scale outdoor deployment. We propose a new model that can use the parking search trajectory and final destination (e.g., an exit of the parking lot) of a single driver in a parking lot to generate the probability profile that contains the probability of each spot being occupied in a parking lot. To deal with conflicting estimation results generated from different drivers, due to the variance in different drivers’ parking behaviors, a novel aggregation approach SpotE-TD is proposed. The proposed aggregation method is based on truth discovery techniques and can handle the variety in Quality of Information of different vehicles. We evaluate our proposed method through a real-life deployment study. Results show that SpotE-TD can efficiently provide spot-level parking availability information with a 20% higher accuracy than the state-of-the-art.

中文翻译：

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使用真相发现的驾驶员行为感知停车可用性人群感应系统

停车位级别的可用信息（停车场中每个停车位的可用情况）的需求量很大，因为它可以帮助减少寻找停车位时的时间和能源浪费。在本文中，我们提出了一个名为 SpotE 的人群感应系统，该系统可以使用驾驶员的智能手机传感器在停车场提供现场级别的可用性。SpotE 只需要驾驶员智能手机中的传感器数据，避免了安装额外传感器的高昂成本，并实现了大规模的户外部署。我们提出了一种新模型，该模型可以使用停车场中单个驾驶员的停车搜索轨迹和最终目的地（例如，停车场的出口）来生成包含每个停车位被占用的概率的概率分布图很多。针对不同驾驶员产生的估计结果相互矛盾，由于不同驾驶员停车行为的差异，提出了一种新的聚合方法SpotE-TD。所提出的聚合方法基于真相发现技术，可以处理不同车辆信息质量的变化。我们通过实际部署研究评估我们提出的方法。结果表明，SpotE-TD 可以有效地提供现场级别的停车可用性信息，其准确度比最先进的技术高 20%。我们通过实际部署研究评估我们提出的方法。结果表明，SpotE-TD 可以有效地提供现场级别的停车可用性信息，其准确度比最先进的技术高 20%。我们通过实际部署研究评估我们提出的方法。结果表明，SpotE-TD 可以有效地提供现场级别的停车可用性信息，其准确度比最先进的技术高 20%。