Impact of Sampling Rate of GPS-Enabled Cell Phones on Mode Detection and GIS Map Matching Performance
Impact of Sampling Rate of GPS-Enabled Cell Phones on Mode Detection and GIS Map Matching Performance
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发表时间：
2007
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通讯作者：
Young-Ji Byon;B. Abdulhai;A. Shalaby
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作者：
Young-Ji Byon;B. Abdulhai;A. Shalaby
新兴的GPS（全球定位系统）使手机提供了新的机会，数据收集在大量的成本相对较低的专用探测车辆。在加拿大，商业手机服务提供商开始提供支持GPS的手机，从而实现各种基于位置的服务（LBS）。不管应用，每个蜂窝电话位置查询或“ping”被收取一定的费用，因此最小化ping频率符合用户的利益。交通监控应用程序首先需要确定启用GPS的手机是否实际上在汽车中，其次，它需要将当前GPS设备位置与GIS（地理信息系统）地图上的相应链接进行匹配。本文提出了一种方法来确定手机ping采样率和模式检测和地图匹配过程的准确性之间的关系。据发现，2 ping的AGPS手机每3分钟的结果在80%的准确率在自动模式检测率。实验还发现，每间隔的ping次数越多，数据跟踪间隔越长，准确率越高，自动模式识别率高达98%。采样频率对地图匹配算法的影响是路段长度、车辆当前速度和一天中的时间段的函数。所开发的算法实现在以前开发的应用程序框架命名为GISTT。
Emerging GPS (Global Positioning System) enabled cell phones offer new opportunities of data collection in massive volumes at relatively cheaper cost than the dedicated probe vehicles. In Canada, commercial cell phone service providers are beginning to offer GPS-enabled phones and hence enabling a variety of Location Based Services (LBS). Regardless of the application, each cell phone location query or "ping" is charged with a certain cost and therefore it is in the user's interest to minimize the pinging frequency. Traffic monitoring applications first need to determine whether the GPS-enabled cell phone is actually in an automobile and secondly, it needs to match the current GPS device location to a corresponding link on a GIS (Geographic Information Systems) map. This paper develops a methodology to determine the relationship between cell phone pinging sampling rate and the accuracy of mode detection and map matching processes. It is found that 2 pings of an AGPS cell phone per every 3 minutes results in 80% accuracy in auto mode detection rate. It is also found that the higher the number of pings per interval and the longer the data trace interval, the better the accuracy, achieving as high as 98% auto mode identification rate. The impact of a sampling frequency on map matching algorithm is found to be a function of link length, current speed of a vehicle and period of the day. The developed algorithms are implemented in a previously developed application framework named GISTT.