【正文】 position of the truck must be within a userdefined area。 Data analysis。 and Julio Martinez, Abstract: The systems that historically have been used to collect data for time studies of construction operations are manual in nature and limited to the observer39。 points in gray indicate velocity was zero, and those in black indicate velocities greater than zero.Time is of paramount interest from the data, and yet the most straightforward graphic, the plan view, does not contain time information. Therefore, a graph of position when velocity was zero versus time is also prepared to aid in understanding the data. The distance to a fixed point is used to represent the 2D position by a single value. The point can be arbitrarily chosen, but for load and haul operations, choosing the approximate center of the loading area will provide additional meaning to the graph by allowing the user to quickly identify cycles. Such a graph is shown in Fig. 3.The value of this graphic is that a steady pattern depicts a steady cyclic operation, and changes in pattern indicate changes in the cyclic operation. Three separate operations are identified in Fig. 3, and the number of peaks associated with each operation indicates the number of cycles performed.Preparations are also made during the Import Text process for data reduction. Three additional sheets are placed in the workbook and populated with data to be used by the data reduction modules developed as part of this work and described in the following section.Data ReductionMany approaches were explored in attempts to reduce the datafile to a manageable size and identify the records that mark critical aspects of truck cycles. Initially, attempts were made to review the collected data in its raw form. The enormous data volume made review in a timely manner infeasible and led the user into a state of information overload. It was recognized that user review was necessary, but that data reduction was required investigations into the data were focused on analyzing the velocity data. Plan view plots, not unlike Fig. 3, were generated with point color used to distinguish between velocity plots allowed the user to identify locations of interest but provided no information as to when events of interest occurred.This shifted focus to analyzing position data. Attempts to identify the key records were not entirely successful based solely on position data. It was found that both position and velocity criteria were necessary to successfully identify the key records. The process led to the understanding that the use of mobile vehicles can be better understood if data are obtained for the following 3 conditions: spent with velocity equal to zero within a specified fixed location—., an aggregate haul truck loading at a bin。 those produced by queuing can be identified as those preceding another result at approximate same time.EXTIM identified 150 records that marked the start and stop of the load activities. Further review and reduction by the user of the identified records resulted in 84 key records. This level of reduction from 18,850 to 84 records is very similar to that of the ADTIM and represents a greater than % reduction.FBTIM was used to identify the records key to the haul and return activities. The duration of the haul and return activities can be characterized as the time required for the truck to travel through or between FBTIM areas. For purposes of illustration, the FBTIM areas will be applied in both manners and are presented in Fig. 6. The truck traveled through boundary number 1 during thefirst operation and between boundaries 2 and 3 during the second operation.Further manual reduction of the data resulting from the TIMs may be necessary to produce a set of critical records. Overlapping areas can be defined for the FBTIM and produce results that are both accurate and useful. It is important to understand that the results produced may also overlap, and the appropriate overlapping results should be neglected.Data reduction by FBTIM reduced the number of records from 18,850 to 202. Further review and reduction by the user resulted in the identification of 164 key records that mark the start and stop of activities. These 164 records represent less than 1% of the total number of data records collected。 recorded horizontal position from geodetic to planar coordinates。Reduction of ShortInterval GPS Data for Construction Operations AnalysisJohn Hildreth, 。Time studies.IntroductionTime studies historically have been performed to record the time required to plete various construction tasks (Oglesby et ). The original time study system was the stopwatch, which has since been replaced by timelapse video recordings. These systems are manual in nature and limited to the field of view of the observer, or what is within the line of sight of the , analysts have turned to technology for new an observer performs the study in the field with a stopwatch or the operation is filmed for preliminary review and data reduction in the office, the information is limited to the field of view of the observer or camera. Analysts have looked to onboard instrumentation as a data collection tool useful beyond the field of view.Global Positioning System (GPS) technology incorporated into an onboard instrumentation system can provide position and velocity data as a potential solution to the fieldofview , recording data frequently enough to provide good results produces a very large volume of data. With several thousand data records produced daily on each piece of instrumented equipment, the issue bees managing the data and identifying the relatively few key records that mark the beginning and end of the activities being studied. Regardless of the tool implemented, the process is performed in four phases: data capture, preliminary review, data reduction, and data analysis. The tools used in each phase for various systems can be seen in Tabl