测绘工程英文论文.doc

Reduction of Short-Interval GPS Data for Construction Operations AnalysisJohn Hildreth, M.ASCE1; Michael Vorster, M.ASCE2; and Julio Martinez, M.ASCE3 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 observers 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 becomes 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: 10.1061/(ASCE)0733-9364(2005)131:8(920)CE Database subject headings: Geographic information systems; Construction industry; Data analysis; Information management;Time studies.IntroductionTime studies historically have been performed to record the time required to complete various construction tasks (Oglesby et al.1989). The original time study system was the stopwatch, which has since been replaced by time-lapse video recordings.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 observer.Recently, analysts have turned to technology for new tools.Whether 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 on-board 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 field-of-view issue.However, recording data frequently enough to provide good re-sults produces a very large volume of data. With several thousand data records produced daily on each piece of instrumented equip-ment, the issue becomes 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 Table 1. A field observer identifies the key times in real time with in-stantaneous decisions of when one activity stops and the nextstarts based on enormous volumes of visual information. This work developed a methodology for making equivalent decisions based on GPS data, specifically to answer the question, How can the large volume of short-interval GPS data collected be auto-matically reduced to the small volume of key discrete points that mark the start and stop of activities? This paper briefly describes the hardware used to record thenecessary GPS data and presents procedures developed to identify the key records necessary to calculate activity durations. A case study is used to show the application of the system to an earth-moving operation. This paper also postulates how the information from the data can be used in discrete event simulations. Descrip-tions of discrete event simulation applied to construction opera-tions can be found in Martinez and Ioannou (1995), Ioannou and Martinez (1996a), Martinez (1998), Sangarayakul (1998), Cor and Martinez (1999), and Ioannou (1999). Previous Work An early use of GPS to collect productivity data was a pilot study designed to provide current information regarding earthwork operations (Ackroyd 1998). Position information was transmitted via radio and recorded by a remote PC at 2-min intervals and at the closure of proximity switches on levers of articulated haulers and motor scrapers. In the initial phase, analysis of the data focused on measuring cycle times, but no real analysis was performed of positions between events. In a second phase, strain gauges were used to estimate payload and thereby provide productivity data. This work depicts the potential of GPS for recording data from earthmoving operations but focuses on the hardware issues rather than the data reduction issues. Ackroyd concludes that the information may add value to monitoring and scheduling tasks but does not address use of the data for analyzing earthmoving operations.Field Data ReductionOglesby et al. (1989) point out that the purpose of a time study is to record the times of various activities that make up an operation.The simplest time studiesstopwatch studiesrely on the observer to decide the point in time at which activities start and stop.Oglesby et al. (1989) note that placing this responsibility on the observer limits the usefulness of the data. Observers can have differences in opinion, data recorded by a single observer may vary over time, available information is strictly limited to that recorded and is subject to the physical limitations of the observer.Additionally, the information recorded is strictly limited to the field of view of the observer.Bjornsson and Sagert (1994) presented PAVIC+ as a computer-based system to extract data from video recording of construction operations, including activity durations. Each activity instance is called a segment and defined by a start and stop time. Three tools are available for registering segments: start-stop time, cycle time, and consecutive time. The start-stop time tool operates like a stopwatch: the user indicates through keystrokes both the start and stop time. PAVIC+ reads the time of the keystroke from a time stamp placed on the audio track and records it.The cycle time tool is used when the same work sequence is repeated. The keystroke used to indicate the stop of an activity also indicates the start of the successive activity, which eliminates the need to press two keys simultaneously. The consecutive time tool is appropriate for a group of correlated activities, such as a crane serving multiple crews. Each activity is assigned a unique key that is struck when the activity ends. This keystroke indicates the stop of the associated activity and the start of the successive activity. Frank (2001) describes a very similar system used to extract information from digital video.He presents and discusses the Digital Video Analysis Tool (DVAT) and notes that PAVIC+ was the basis on which DVAT was developed. DVAT is a software package specifically developed for stripping information from digital video. The extracted information can be used to generate crew balance charts or to develop input for computer simulation models. These traditional data collection methods have relied upon manual tools limited to the field of view of the observer. Researchers have turned to technology for tools that are both automated and not restricted to field of view. Kannan (1999) describes the use of onboard instrumentation systems for recording data from earthmoving operations. He used mechanical parameters recorded by the Vital Information Management System (VIMS) and the Total Payload Management System (TPMS) produced by Caterpillar to obtain operational data. Such systems place a virtual observer in the equipment, and thus the equipment is always in the virtual field of view.Kannan (1999) states that when using onboard instrumentation, the sensors must be able to detect a change in the status of the truck. This change in status indicates the start or stop of an activity. Kannan and Vorster (2000) state that mechanical parameters can be translated to production measures through the use of surrogate measures and protocol rules. Kannan (1998) describes the use of suspension strut pressure, gear control lever position,bed raise switch position, and speed to derive the duration of truck cycle components. Truck cycle components or activities are defined in terms of the parameters. It is evident from the literature reviewed that traditional datacollection methods are limited by the ability of an observer to instantaneously decide activity start and stop times within a narrow and static field of view. Modern techniques remove this limitation by using sensor-based data-collection techniques to identify key times through changes in sensor output. A thorough understanding of the methods for reviewing, reducing, and analyzing GPS data from construction operations requires background knowledge of how and what data are collected.Data Capture An automated data capture system based on GPS technology was used to record the raw data at a user-specified time interval, including date, time, velocity, and horizontal position. The system consists of a data box, junction box, and sensor pack and was developed based on field evaluations of commercially available systems. The data acquisition and storage box is a weathertight enclosure that houses the system circuitry and the CompactFlash media on which the data is stored. The sensor pack consists of a Garmin GPS-35 LVC receiver and a Vector 2X magnetic compass manufactured by Precision Navigation, Inc. In addition to the GPS and compass, the system has been developed such that additional analog and digital sensors can be incorporated into the system for future research. The junction box serves as a central location for all electrical connections to be made. Power is made available in the box at 5 V for any additional analog sensors incorporated into the system. Data are recorded in comma delimited ASCII text format, with a separate data file for each day of collection to ensure files of a manageable size. The parameters recorded by the system include unit identification number, date, UTM time, latitude, longitude,velocity, type of GPS fix, output from the five analog and eight digital sensors, and direction of travel from the compass. Preliminary ReviewA trade-off exists between the volume of data recorded and the level of detail provided by the data. Large volumes of data are produced when recorded at a time interval sufficiently short to facilitate operations analysis. The several thousand records produced daily on each machine must be reduced to the relatively few key records necessary to calculate activity times. The large volume of data dictates that the data reduction process be automated.The format of the data, a text string repeatedly recorded at aset time interval, is conducive to analysis in a spreadsheet environment supported by capable graphics. Microsoft Excel was chosen as the data processing engine as it is a widely used and recognized program already familiar to many potential users, thereby minimizing or eliminating the learning curve. Excel is a common and robust spreadsheet application that can be automated through the Visual Basic for Applications (VBA) language and provides the means to graphically represent data.Processing the collected data for evaluation and reduction is performed in three steps:1.Import the data into Excel;2.Convert recorded horizontal position from geodetic to planar coordinates; and3.Prepare graphical representations of the data. These three steps have been automated using VBA code, and this routine is available from the system user interface, provided as Fig. 1. The process of importing, converting, and graphing is executed by selecting the Import Text option, which allows the user to point to the desired data file.Once the file is identified, the data are imported into Excel with each record in a separate row and each parameter in a separate column. The geodetic positions are recorded as latitude and longitude in units of degrees and minutes. The scale of even a very large construction earthmoving operation is sufficiently small to make geodetic coordinates less than meaningful, and therefore the recorded geodetic coordinates are converted to a planar coordinate system in units of meters. The methods to convert from geodetic to state plane coordinates are outlined in standard surveying texts (Stem 1990).In addition to importing the data and converting the coordinates, two separate graphs of the data are prepared to assist the user in reviewing and understanding the data. Mapping the recorded coordinates creates a plan view of vehicle position throughout the workday, and color is used to distinguish between positions recorded when velocity is below a user-defined value,most frequently zero, and those recorded when velocity was greater than the value. A plan view of data collected during one day of the field application is shown in Fig. 2; 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 first.Early 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 magnitudes.Such 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:1.Time spent with velocity equal to zero within a specified fixed locatione.g., an aggregate haul truck loading at a bin;2.Time spent with velocity equal to zero within a fixed distance of a moving locatione.g., a haul truck loading at a loader;and3.Time required to travel through or between fixed arease.g.,a haul truck traveling between load and dump areas. Time identification modules (TIMs) were developed to identify the GPS data records corresponding to the beginning and end of activities associated with each of the three specific conditions.These modules have been developed in the VBA language for execution in Excel. The results produced by TIMs have been compared to those produced by a PAVIC+ analysis of the videotaped operation in an effort to validate the TIMs results. The activity durations resulting from both analyses were compared and found to be in agreement,both in the duration of activities and the order in which they occurred. It was concluded that the short-interval 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 user-defined 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:1.The recorded velocity must be zero (i.e., the truck is stopped);2.The position of the truck must be within a user-defined area;and3.The previous record must be outside the area for arrival times, or the next record must be outside the area for departure times.The ADTIM evaluates each of the three criteria in the order in which they were presented. To ensure that the recorded velocity was zer