Chinese to English: Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues
General field: Tech/Engineering
Detailed field: Engineering (general) |
Source text - Chinese
With the availability of Global Positioning System (GPS) receivers to capture vehicle location, it is now feasible to easily collect multiple days of travel data automatically. However, GPS-collected data are not ready for direct use in trip rate or route choice research until trip ends are identified within large GPS data streams. One common parameter used to divide trips is dwell time, the time a vehicle is stationary. Identifying trips is particularly challenging when there is trip chaining with brief stops, such as picking up and dropping off passengers. It is hard to distinguish these stops from those caused by traffic controls or congestion. Although the dwell time method is effective in many cases, it is not foolproof and recent research indicates use of additional logic improves trip dividing. While some studies incorporating more than dwell time to identify trip ends having been conducted, research including actual trip ends to evaluate the success of trip dividing methods used have been limited. In this research, 12 ten-day real-world GPS travel datasets were used to develop, calibrate and compare three methods to identify trip start points in the data stream. The true start and end points of each trip were identified in advance in the GPS data stream using a supplemental trip log completed by the participants so that the accuracy of each automated trip division method could be measured and compared. A heuristic model, which combines heading change, dwell time and distance between the GPS points and the road network, performs best, correctly identifying 94% of trip ends. | Translation - English
有了可靠的全球定位系统接收器获取车辆的位置，目前自动收集多日旅行数据容易可行。但是，除非大型GPS数据流中的出行起止点能被自动识别，GPS收集到的数据并不能直接被运用到出行率和路径选择的研究中。一个常用的划分出行起止点的参数是停留时间，也就是车辆保持静止的时间。在中间有短暂停留的出行链发生的时候，辨识出行起止点尤其困难，例如送人和接人的时候：很难把这些短暂的停顿时间跟由于交通控制装置或者拥挤造成的停顿区分开来。虽然使用停留时间方法在很多时候有效，这个办法并不是绝对正确的。最近的研究显示，使用多层次的逻辑方法能提高出行起止点辨别的准确率。虽然目前已有研究引进了包括停留时间在内的多种方式识别出行起止点，但是很少有研究使用真实的出行起止点来检验起止点辨识方法的有效性。在本篇论文中，12段GPS收集的10日实时旅行数据流被用来建模。三个从连续GPS连续数据流中辨识出行起止点的模型被检校并跟真实的出行起止点进行对比以验证其准确性。真实的出行起止点事先经由司机手工记载的补充出行日志跟GPS数据进行比对已经在GPS数据流中标志出。一个结合了停留时间，GPS点到路网距离，以及车辆转向多种参数在内的试探算法模型能够得到最佳结果：94%的出行起止点能被成功识别。 |
The translation workplace 
Portfolio