Techniques for Measuring Over-All Speeds in Urban Areas - Research Proposal Example

PART Introduction Three situations have been used for travel time data collection since the late 1920s. Traditionally, this technique has involved the use of a data collection vehicle within which an observer records cumulative travel time at predefined checkpoints along a travel route. This information is then converted to travel time, speed, and delay for each segment along the survey route. There are several different methods for performing this type of data collection, depending upon the instrumentation used in the vehicle and the driving instructions given to the driver. Since these vehicles are instrumented and then sent into the field for travel time data collection, they are sometimes referred to as "active" test vehicles. Conversely, "passive" ITS probe vehicles are vehicles that are already in the traffic stream for purposes other than data collection. Three situations are as follows Manual - manually recording elapsed time at predefined checkpoints using a passenger in the test vehicle; Historically, the manual method has been the most commonly used travel time data collection technique. This method requires a driver and a passenger to be in the test vehicle. The driver operates the test vehicle while the passenger records time information at predefined checkpoints - Distance Measuring Instrument (DMI) - determining travel time along a corridor based upon speed and distance information provided by an electronic DMI connected to the transmission of the test vehicle; Technology has automated the manual method with the use of an electronic DMI. The DMI is connected to a portable computer in the test vehicle and receives pulses at given intervals from the transmission of the vehicle. Distance and speed information are then determined from these pulses - Global Positioning System (GPS) - determines test vehicle position and speed by using signals from the Department of Defense (DOD) system of earth-orbiting satellites. GPS has become the most recent technology to be used for travel time data collection. A GPS receiver is connected to a portable computer and collects the latitude and longitude information that enables tracking of the test vehicle. Each of these test vehicle techniques is described in detail in the following sections of this chapter. The following elements are included for each technique: overview, advantages and disadvantages, cost and equipment requirements, data collection instructions, data reduction and quality control, and previous experiences. Since the driver of the test vehicle is a member of the data collection team, driving styles and behavior can be controlled to match desired driving behavior. The following are three common test vehicle driving styles (1): - Average car - test vehicle travels according to the driver's judgement of the average speed of the traffic stream; - Floating car - driver "floats" with the traffic by attempting to safely pass as many vehicles as pass the test vehicle; and - Maximum car - test vehicle is driven at the posted speed limit unless impeded by actual traffic conditions or safety considerations. The floating car driving style is the most commonly referenced. In practice, however, drivers will likely adopt a hybrid of the floating car and average car because of the inherent difficulties of keeping track of passed and passing vehicles in high traffic volume conditions Advantages and Disadvantages The manual method (pen and paper) has the following advantages: - No special equipment needs; - Low skill level (no special hardware training); and - Minimal equipment costs. The manual method (pen and paper) has the following disadvantages: - High labor requirements (driver and observer); - Low level of detail (average speeds for 0.4 to 0.8 km, or 0.25 to 0.5 mi). Average speed and delay are reasonable while queue length and speed profiles are difficult; - Greater potential for human error (potential for marking wrong checkpoints or inaccurate times); - Potential data entry errors (e.g., recording travel time errors in the field and transcription errors from field sheet to electronic format); - Cost and time constraints prohibit large sample sizes; and - Little automation potential and only estimates of emission, fuel consumption, and other performance measure limitations due to the averaged speeds over 0.4 to 0.8 km (0.25 to 0.5 mi). Distance Measuring Instrument The electronic distance measuring instrument (DMI) is used for a variety of applications, such as route numbering, emergency 911 addressing, acreage and volume calculations, as well as general linear distance measuring for pavement markings. These instruments are very accurate once calibrated (plus or minus one foot per mile, or 0.19 meter per kilometer). Travel time data collection with manual DMIs was conducted in the early 1970s. Original DMI units used an adding machine tape or printer to record the distance and speed from the unit. A circular graph known as a tachograph was used to continually record distance and speed. These manual DMI units used a magnetic wheel sensor to measure revolutions. Calibration was provided by knowing the number of revolutions over a fixed distance. When properly calibrated, these devices provided accurate results. However, there were some problems with this technology. Wheel sensors would fall off or not read properly and sometimes unbalance the wheel. Data media was paper format, either circular graphs or adding machine tape, which were difficult to read and required large amounts of data entry. The advent of the electronic DMI solved these problems. Figure 3-4 illustrates the equipment typically used in electronic DMI data collection. The electronic DMI calculates distance and speed using pulses from a sensor attached to the vehicle's transmission. These pulses are sent from the transmission to the sensor based on the vehicle's speed. The DMI converts the pulses to units of measure and calculates a speed from an internal clock. The DMI unit is able to send the data to a portable computer for storage. Specialized software can be used to record the electronic information, eliminating the data entry and errors associated with the older models. Notes can also be added to the end of the file to describe incidents or other relevant information about the travel time run. A consistent data format allows for automation of reduction and analysis of travel time information. Commercial and proprietary software can be used to interact with the DMI or read the pulses directly from the transmission sensor. The DMI is essentially a specialized piece of hardware/software that interprets the pulses from the transmission sensor and converts them into a distance. Most software packages provide a data collection module (field data collection) as well as reduction/analysis software. These software packages allow collection for multiple runs and data reduction including tabular summaries and speed profiles. Some DMI manufacturers have proprietary collection and analysis software, while others provide example computer code to read the data from the DMI. This allows users to develop and customize the data collection and analysis software. File format, sample rate, and report format are among the most relevant issues for researchers and practitioners to customize in the data collection and analysis software. Appendix A contains additional information about computer software available for test vehicle techniques. Advantages and Disadvantages Test vehicle data collection with an electronic DMI has the following advantages (as compared to the other test vehicle methods): - Reduction in staff requirements compared to the manual method. There is no passenger recording information. No data to enter or errors associated with data entry (e.g., transposition, format); - Reduction in human error including missed checkpoints or incorrectly recording information. However, the starting point or first checkpoint must be accurately marked; - Offers some redundancy of checkpoint locations as long as the first checkpoint is marked properly; - Commercially available software provides a variety of collection and analysis features; - Field notes, incidents, and anomalies electronically recordable at the location the incident occurred available in most software packages; - Increased amount and variety of data available for applications including determining queue lengths, stopped delay, average speed, link speeds, detailed speed profiles, input to models for planning, emissions, or fuel consumption, and performance evaluation computation; - Relatively cost-effective and accurate; - Provides data in a consistent format to aid in the automation of data reduction and analysis automation; and - Proven technology. Test vehicle data collection with an electronic DMI has the following disadvantages: - Storage requirements for the vast amount of data collected; - Must be calibrated to obtain accurate results; - Requires accurate marking of first checkpoint; Travel Time Data Collection Handbook Global Positioning System The global positioning system (GPS) was originally developed by the Department of Defense (DOD) for the tracking of military ships, aircraft, and ground vehicles. Signals are sent from the 24 satellites orbiting the earth at 20,120 km (12,500 mi) (see Figure 3-5). These signals can be utilized to monitor location, direction, and speed anywhere in the world. A consumer market has quickly developed for many civil, commercial, and research applications of GPS technology including recreational (e.g., backpacking, boating), maritime shipping, international air traffic management, and vehicle navigation. The vehicle location and navigation advantages of GPS have found many uses in the transportation profession (12). Due to the level of accuracy that GPS technology provides, the DOD has altered the accuracy of the signal for civilian use. This is called selective availability (SA), and when it is activated precision can be degraded to about 91 meters (300 feet). In the absence of selective availability activation, accuracy can be within 18 meters (60 feet) (13). However, with the use of the differential global positioning system (DGPS), accuracy can be improved. DGPS utilizes a receiver placed at a known location to determine and correct the signal that is being provided when SA is activated. A commercial market has developed to provide differential correction hardware as well. Many recent developments will affect the future use of GPS in civil applications. Currently, the U.S. Department of Transportation is considering the expansion of the Coast Guard marine DGPS beacon system. This includes the existing beacons utilized for DGPS along coastal areas and in the major inland waterways. However, such an expansion would provide a much broader system that would include interior areas throughout the nation (14). In addition, the Clinton administration has approved the release of the SA restrictions within the next ten years. This will provide much more accurate information for civil and commercial use (12,14). There is also a significant market increase for in-vehicle GPS units. Japan currently holds the largest market for in-vehicle navigation systems. In 1995, the country had 60 million vehicles and there were 500,000 in-vehicle systems sold. This was up 150,000 from the previous year. In the U.S., one study showed that one-half of U.S. consumers are familiar with in-vehicle navigation systems while almost one-fifth expressed an interest in owning such a system for their vehicle (13). It is estimated that GPS in-vehicle navigation systems will not be viewed as a luxury item in the next five years (15). Current efforts to provide a world standard for in-vehicle navigation mapping will also provide compatibility between the many manufacturers in the market (16). PART 2 Louisiana State University Research performed at the Remote Sensing and Image Processing Laboratory at Louisiana State University (LSU) developed a methodology to use GPS in collecting, reducing, and reporting travel time data for congestion management systems (17,21). The data collection methodology began with the development of a base map at the interchange of I-10 and I-12 that was being studied. Since adequate base maps of the site did not exist, the base map was developed in the GIS software with the use of the data collected from the GPS units themselves. The study routes in this corridor were driven in both directions with the use of GPS to collect data every second. All entrance and exit ramps at the interchange were also driven to ensure all portions of the interchange were included. In addition, ramps, lane drops, and signalized intersections were included (21). The next step in the methodology was the determination of checkpoints along the route since travel time and average speed studies generally average these measures over a specific link length. Two rules were used in the establishment of the checkpoint locations. The first was to establish a checkpoint at all physical discontinuities (e.g., signalized intersections, significant unsignalized intersections, lane drops, exit ramps, entrance ramps, other geometric discontinuities). The second guideline used in the determination of checkpoints was a nominal spacing of five checkpoints every mile. This resulted in 2,397 segments with an average segment length of 0.21 km (0.13 mi) (17,21). After the determination of the checkpoint locations, it was important to link each of the segments to a relational database. The use of a unique identifier for each segment allowed for associating the number of lanes and posted speed limit to each section. In addition, analyses performed over different dates and times could be associated with specific segments (21). Travel time data were collected in the morning and afternoon peak hours as well as during off-peak periods. To aid in the data reduction effort, a data reduction software macro was developed. The macro aids in transforming the GPS point-by-point data into travel times and average speeds over the segment. When the user clicks on a specific segment along the corridor, the data reduction application recognizes the segment and determines entrance and exit times and updates the user interface (21). CASE 2 Central Transportation Planning Staff (CTPS), Boston, Massachusetts Travel time was identified as a performance measure for the Boston area's congestion management system (CMS). Several data collection techniques for travel time data were considered, but GPS was selected for use. GPS was the selected method since it provided the high potential for more accurate data at a reduced cost. Further, the Central Transportation Planning Staff in Boston selected the GPS technology since it allows for the collection of an increased amount of data (i.e., collected every second) that may be utilized for analyses of queue lengths, stopped delay, and speed profiles for the CMS (20). Several months went into the development of an interface in the GIS software to allow for standardizing the editing process of the data collected with the GPS receivers. The menu within the GIS software allows for the calculation of key performance measures such as travel time and average speed along predetermined segments. A file is then produced that contains characteristics of the segment including the route, date, segment name, segment start and end times, segment length, and average speed for every segment in which GPS data were collected (20). The study also compared data collection utilizing the manual method with the GPS method. These techniques were compared on one segment. The traditional manual method that was used employed a passenger in the test vehicle who wrote down the time as they reached predetermined checkpoints. The information was then entered into a spreadsheet to calculate travel times and speeds. Data was collected with GPS units in one-second increments and included longitude, latitude, time, altitude, and other information. A passenger/recorder was not necessary when collecting data in this manner. The study found that there was general agreement between the traditional manual method and the GPS technology. The differences in distance between segments was less than 0.16 km (0.1 mi) and all the speeds were within 8 km/h (5 mph) (20). It is interesting to note that differential correction was not used for the GPS travel time data collection in this study. References 1. Manual of Transportation Engineering Studies. Robertson, H.D., editor, Institute of Transportation Engineers, Washington, DC, 1994. 2. May, A.D. Traffic Flow Fundamentals. Prentice Hall, Inc., Englewood Cliffs, New Jersey, 1990. 3. Lomax, T., S. Turner, G. Shunk, H.S. Levinson, R.H. Pratt, P.N. Bay, and G.B. Douglas. Quantifying Congestion: User's Guide. NCHRP Report 398, Volume II. Transportation Research Board, Washington, DC, 1997. 4. Lomax, T., S. Turner, G. Shunk, H.S. Levinson, R.H. Pratt, P.N. Bay, and G.B. Douglas. Quantifying Congestion: Final Report. NCHRP Report 398, Volume I. Transportation Research Board, Washington, DC, 1997. 5. Berry, D.S. and F.H. Green. "Techniques for Measuring Over-All Speeds in Urban Areas." In Proceedings. Highway Research Board, National Research Council, Volume 28, 1949, pp. 311-318. 6. Berry, D.S. "Evaluation of Techniques for Determining Over-All Travel Time." In Proceedings. Highway Research Board, National Research Council, Volume 31, 1952, pp. 429-439. Read More