【正文】 The activity durations resulting from both analyses were pared and found to be in agreement,both in the duration of activities and the order in which they occurred. It was concluded that the shortinterval GPS data can be reduced to the key records using the TIMs.Arrive and Depart Time Identification ModuleThe duration of certain construction activities that repeatedly occur in a fixed location can be characterized as the time elapsed while the vehicle was stopped within a given distance from a fixed point. A truck loaded from a hot mix asphalt bin or a truck dumping into the bin of a rock crusher are examples of such activities. The Arrive and Depart Time Identification Module (ADTIM) was developed for this condition and to identify the first (arrive) and last (depart) times at which the truck was stopped within a given distance of a userdefined point. The time difference between arriving and departing represents the activity duration.Three criteria must be satisfied for a record to be considered as an arrival or departure time: recorded velocity must be zero (., the truck is stopped)。 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。 recorded horizontal position from geodetic to planar coordinates。 Information management。 Construction industry。 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。Reduction of ShortInterval GPS Data for Construction Operations AnalysisJohn Hildreth, 。 Michael Vorster, 。s field of view. Global Positioning System (GPS) technology incorporated into an onboard instrumentation system can be used to autonomously collect position and velocity data without the field of view limitation. Data must be collected at a short time interval to provide the level of detail necessary for operations analysis. Thus the issue bees managing the data and identifying the relatively key records that mark the start and stop of activities. A field observer identifies the key times in real time withinstantaneous decisions of when one activity stops and the next starts based on enormous volumes of visual information. This work developed a methodology for making equivalent decisions based on GPS data and presents the procedures developed to identify the key records necessary to calculate activity durations. A case study is used to illustrate application of the system to an earthmoving operation. Also, it is postulated how the information can be used in discrete event simulation. DOI: (ASCE)07339364(2005)131:8(920)CE Database subject headings: Geographic information systems。 Data analysis。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