论文标题
驾驶员侧和基于交通的街道停车解决方案的评估模型
Driver Side and Traffic Based Evaluation Model for On-Street Parking Solutions
论文作者
论文摘要
对于城市司机来说，停车一直是一个痛苦的问题。随着越来越多的人倾向于在全球城市化的背景下生活在城市中，停车疼痛加剧了。因此，要求找到一种减轻河流停车头的解决方案。许多解决方案试图通过预测停车位的占用来解决停车问题。他们的重点是理论方面的准确性，但缺乏标准化模型来评估这些建议在实践中。本文开发了一个基于驾驶员的评估模型（DSTBM），该模型为不同的停车解决方案提供了一般评估方案。使用DSTBM分析了两种常见的停车检测方法，即固定感测和移动感测。结果首先表明，DSTBM从驾驶员的角度研究了不同的解决方案，并且与其他评估方案没有冲突。其次，DSTBM证实，就预测准确性而言，固定传感的性能要比移动感测得更好。
Parking has been a painful problem for urban drivers. The parking pain exacerbates as more people tend to live in cities in the context of global urbanization. Thus, it is demanding to find a solution to mitigate d rivers' parking headaches. Many solutions tried to resolve the parking issue by predicting parking occupancy. Their focuses were on the accuracy of the theoretical side but lacked a standardized model to evaluate these proposals in practice. This paper develops a Driver Side and Traffic Based Evaluation Model (DSTBM), which provides a general evaluation scheme for different parking solutions. Two common parking detection methods, fixed sensing and mobile sensing are analyzed using DSTBM. The results indicate first, DSTBM examines different solutions from the driver's perspective and has no conflicts with other evaluation schemes; second, DSTBM confirms that fixed sensing performs better than mobile sensing in terms of prediction accuracy.
