One-Day Long Statistical Analysis of Parking Demand by Using Single-Camera Vacancy Detection

Many researches have focused on parking demand to gain information for traffic management recommendations and decision-making where real-world car park statistics is of great importance. This paper seeks to obtain one-day long statistical analysis of a multi-purpose off-street parking space in downtown Abu Dhabi, using a single-camera vacancy detection system. The proposed methodology to collect one-day long statistics uses pattern recognition to determine occupancy states based on visual features extracted from parking spots. This vacancy detection system has two major advantages. First, it relies on only few pixels compared with other methods, being able to cover more than 150 parking spots within a single camera frame. Second, the system works well in both nighttime and daytime - robust to changing light conditions. The accuracy is 99.9% for occupied spots and 97.9% for empty spots for this period of study. This study also proposes a better indication of parking demand when the park is near its full capacity, as the utilization rate does not capture the parking demand from the motorists who fail to find parking spaces.

摘要为了给交通管理和决策制定提供依据,已有很多针对停*车需求的研究,其中对停车场实际数据的统计分析尤为重要.本研究借助单摄像机空位检测系统,获取了位于阿布扎比市区的通用路外停车场全天的停车状况. 具体检测方法为,根据停车位的视觉特征并利用图像识别来定义车位的占用或空闲状态. 本文的空位检测系统具备两个突出优点馊首先,和其他方法相比,它需要的像素点更少,单个摄像机能覆盖超过 150 个停车位;其次,无论白天还是夜间,系统的工作性能受光照条件的影响很小. 实验结果表明,在研究时段内,占用车位的检测精度为99.9%,空闲车位的检测精度为97.9%. 此外,当停车场接近饱和时,由于利用率不能包含没有获得停车位的驾员的停车需求,本文基于此问题提出了一种更加准确的描述方法.