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Publication Number: FHWA-HRT-05-137
Date: July 2006
Evaluation of Safety, Design, and Operation of Shared-Use Paths
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CHAPTER 4. OPERATIONAL DATA COLLECTION
One of the major objectives of this research was that the LOS methodology be calibrated and validated using data collected from the United States. The challenges facing the project team included a choice of methodology to allow the efficient collection of high-quality data on the operations of shared-use paths and the selection of study paths to provide a nationwide sample of representative paths. This chapter describes how the researchers met those challenges. Chapter 5 provides the results from the operational data collection effort.
DATA COLLECTION METHOD
The model for estimating the number of meetings and passings experienced by a test bicyclist developed in depth in chapter 3 uses the volume, average speed, and standard deviation around the average speed of each mode on the path as inputs. The data collection to calibrate and validate this model must therefore involve all of these variables. Of course, trail characteristics must also be recorded at each site. To ensure later flexibility, it was also desirable that scenes on paths of interest be recorded from different perspectives so that additional data could be obtained later by viewing videotapes if needed.
The project team identified three possible methods of operational data collection, which included a one-camera method, a two-camera method, and a moving-bicycle method. The one-camera method involved a camera on a high perch that was able to record activity, including meetings and passings, on a long segment of path. The two-camera method involved two cameras set up several thousand feet apart along a path. From each camera, a sequence of users could be determined and, from those sequences, meetings and passings could be discerned. In the end, however, we concluded that the one-camera and two-camera methods would not provide adequate data, so we chose the moving-bicycle method (described below). Vantage points for the one-camera method would have been rare; tall buildings and hills with unobstructed views of qualifying shared-use paths are not common in the United States. The two-camera method would not have been able to identify the difference between actual passings and desired passings, because only path users would have known whether they wanted to pass and were unable to do so and why. For example, a bicyclist may not have been able to pass because of inadequate path width or congestion.
We chose the moving-bicycle method as our primary operational data collection method because it overcomes the flaws in the other methods. It is not restricted to places where special camera vantage points are available, and it can determine desired passings. The method works by collecting data from the perspective of the bicyclist, and is analogous to the moving-vehicle method of collecting volumes and travel times on the highway (such as described in chapter 15 of the HCM(4)). In the moving-bicycle method, a member of the project team rides a bicycle along a path segment of interest at a predetermined constant speed. The team member attempts to maintain that speed as closely as possible, passing when encountering slower same-direction users when there is sufficient room to do so safely while maintaining that constant speed. The team member wore a video camera on his or her helmet that recorded the number of meetings, the number of passings accomplished, and the number of passings delayed or not accomplished (i.e., reached the end of the segment before the opportunity to pass presented itself). At the same time, as the bicyclist was making his or her ride, a colleague was counting the number of users of each mode in each direction moving past the midway point of the segment. This provided the needed volume data.
A potential bias with the moving-bicycle method is that data collector judgments determine the difference between a desired pass, a following maneuver with no desire to pass, and a completed pass. To prevent this bias from affecting the results, we equipped the bicycle with a mini-computer (a Specialized™ Speed Zone P.Brain) that displayed the bicycle speed to the nearest 0.16 km/h (0.1 mi/h). In addition, the team recorded the time needed to ride the segment from start to finish to verify that the desired speed was maintained.
Moving-bicycle desired speeds were determined for each path based on the results of a prior study of the bicycle speed distribution on the path segment of interest. Typically, the data collection team conducted the prior study on the day before the moving-bicycle study was to commence. Therefore, the data collection process typically took 2 days per trail. On the first day, the team collected an adequate sample of speeds of the free-flowing bicycles (30 minimum) and of other path users using a stopwatch and a clearly marked distance. Then, the team calculated a mean and standard deviation for the bicycle speed sample. The moving-bicycle runs on the second day were typically made at three speed levels–high, medium, and low–that corresponded to the mean speed plus one standard deviation, the mean speed, and the mean speed minus one standard deviation from this prior sample.
On the second day, the team collected the moving-bicycle data. We used a stationary camera on the side of the path at the midpoint, next to the volume data collector, to provide backup for the volume count, to provide additional speed observations, and to provide other data that may have proved necessary later. To avoid fatigue, the two data collectors traded bicycle duty and stationary volume counting duty occasionally.
During the second day of data collection, the team set a goal to collect at least 20 runs at each of the three different speeds, or a total of 60 runs conducted at each path. Since we collected data for both directions along the trail, 60 runs actually provided 10 runs in each direction along the path at the three different speeds. Higher sample sizes would have been desirable, but were not usually possible, because user volumes on the paths of interest did not usually stay high for many hours of the day. We could only collect during daylight hours, and the bicycle riders became fatigued.
The main equipment for this data collection included a stationary camera/recorder, a bicycle, a mini video camera/recorder for the bicyclist';s helmet, and a bicycle speedometer. The bicyclist's recorder was carried in a handlebar pouch, where the display was visible to the bicyclist, so that he or she could be sure that the system was recording. A microphone taped to the bicyclist's shirt was incorporated into the mini-camera system to allow the bicyclist to record comments during a run The most helpful of these comments was whether a particular event was a delayed passing or not.
We used a hybrid bicycle, which is a combination of a mountain bicycle and a road bicycle, during our data collection. Hybrid bicycles have a smoother and wider tire than mountain bicycles in order to obtain the higher speeds and increased stability that we needed. The bicycle we used was also easy to disassemble and reassemble for travel by plane, because we attempted to use the same bicycle for all of the different data collection sites to ensure more consistency during the data collection process. In the end, mechanical problems with the bicycle meant that we used a rented bicycle during one of our data collection trips (to Saint Louis, MO).
The mobile camera and recorder system we chose are generally used for surveillance operations. The mini-camera was approximately 50 millimeters (mm) (2 inches) long and 25 mm (1 inch) in diameter. The camera had 360 lines of resolution and a 3.6-mm-wide lens. The recorder was supposedly the world's smallest VCR at the time we purchased it, with an LCD monitor that was about 190.5 mm (7.5 inches) by 114.3 mm (4.5 inches) by 88.9 mm (3.5 inches), weighing about 0.68 kilograms (kg) (1.5 pounds (lb)). The rechargeable camera battery lasted about 2 h. The research team soon developed a routine of changing all batteries and cassette tapes on all cameras and recorders every 2 h or 10 runs to ensure that we kept recording when desired. Total equipment costs for the cameras, bicycle, and accessories were about $3,000.
The project budget allowed for operational data collection for up to 20 trails in 10 cities across the United States. This was likely to provide a large enough sample to calibrate and validate the procedure in a credible manner. The project team sought operational data collection sites that met a strict set of criteria to ensure project success. These criteria included:
Based on the knowledge of the researchers and input from FHWA staff at the February 2001 briefing, the team assembled a preliminary list of possible data collection sites that may have met some or all of these criteria. The sites included:
The project team developed a questionnaire for the owners or managers of the paths listed above to determine the suitability of a particular path for data collection. The questionnaire asked:
The project team sent the questionnaire to the owners or managers of the 37 trails via regular mail and e-mail and received 26 responses. From the responses, the researchers identified a list of 10 cities and an alternate city that provided the best possible opportunities to satisfy the criteria. The list included cities in all regions of the United States and cities with many of the best-known trails in the United States. The final list of sites approved by FHWA was:
In the end, we used our alternate city, Los Angeles, and did not collect data in Denver because of travel and weather difficulties.
In the course of this study, the team collected data from 15 trails and 10 cities scattered across the United States. Some cities only had one usable trail. The data collection sites were:
The most restrictive criteria in terms of locating usable trail segments were the segment length and the need for moderate to high volumes of traffic. Trails with moderate to high volumes of traffic tend to be in areas with many intersections and trail connections; however, we wanted segments at least 0.8 km (0.5 mi) long between intersections to gather unbiased data using the moving-bicycle method. In the end, we compromised on segment length in a couple of places (a 0.40-km (0.25-mi) segment for the South Bay Trail and a 0.64-km (0.4-mi) segment for the Forest Park Trail). We settled for segments in other places that did not have very high volumes, as shown in chapter 5.
Tables 9 and 10 provides some details on the chosen study trails. The study trails were located in urban and suburban areas. The study trail environments included parks, lakes, beaches, highways, and downtown areas. There was a nice range of trail widths from 2.44 m to 6.1 m (8 to 20 ft). The study trails were sometimes marked with centerlines, and sometimes there were other adjacent treadways that accommodated some users. Few trails had significant horizontal or vertical curvature. Most trails had good sight distances, as judged qualitatively by the research team after riding them numerous times on a bicycle.
Table 9. Characteristics of operational study sites.
Table 10. Additional characteristics of operational study sites
Data Collection Execution
Data collection occurred from July 2001 to March 2002. Peak hours and peak times were identified for each trail location. The peak days were generally Saturdays and Sundays. Peak hours varied by location. There often appeared to be two peaks during the weekend days on most trails; one volume peak in the morning and a second volume peak in the afternoon. Trail users who appeared to be using the trail for fitness purposes appeared most often during the morning hours. Recreational and casual users, consisting of tourists and families, appeared more often during the afternoons. At data collection sites where there were commuters, the commuter peak hours were generally the weekday mornings. The data collection process generally lasted from early morning until dusk; consequently, a great variation in volume was typically collected at each site.
The data collection team attempted to collect 60 trials at each trail. Because of inclement weather and mechanical failures, it was not possible to obtain 60 trials at each trail location. In total, 771 runs were successfully completed. Table 11 shows the sample size by trail. Because it was such a high-quality site and there were no other candidate sites in the city, the team collected extra data at the Lakefront Trail in Chicago. The most disappointing data collection trip was to Washington, DC, during October 2001, when bad weather prevented all but a handful of runs at what should have been excellent data collection sites.
Table 11. Number of successful data collection runs by trail.
Topics: Path, trail, bicycle, shared use, level of service, width, pedestrian, skater
Keywords: Path, trail, bicycle, shared use, level of service, width, pedestrian, skater