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Pages 15-27

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From page 15...
... 15 Analysis of Maintenance Staffing and Practice at Selected Agencies The NTD data analyzed provided an aggregate number of maintenance hours at each transit agency. The NTD data are not broken down by hours, type of maintenance performed, type of vehicles in the fleet, service profiles, and other signifi­ cant defining factors.
From page 16...
... 16 Maintenance Times and Requirements This was the most extensive part of the data­gathering pro­ cess. The initial questionnaire broke down actual maintenance times at each agency by type of maintenance activity and vehicle type.
From page 17...
... 17 that the researchers thought would participate did not. Other agencies did participate, but the process took much longer than anticipated.
From page 18...
... 18 Guelph Transit Guleph, ON, Canada 73 x Rockford Mass Transit District Rockford, IL 73 x First Canada Kelowna, BC, Canada 70 x Strathcona County Transit Strathcona County, AB, Canada 65 x Blacksburg Transit Blacksburg, VA 64 x MV Transit, subcontractor to Monterey-Salinas Transit Monterey, CA 62 x MV Transportation, subcontractor to SamTrans San Francisco, CA 59 x Lakeland Area Mass Transit District Lakeland, FL 59 x City of St. Albert Transit St.
From page 19...
... 19 Geographic Diversity of Participating Agencies In identifying the participating agencies, the research team made attempts to group the participants by region and get participation from multiple areas of the United States and Canada, as seen in Table 5. Efforts were made in the second round of data collection to increase research participation and validation by focusing on agencies in Texas, the Southeast, and the Mountain West.
From page 20...
... 20 Adjustments to find the number of equivalent technicians were made based on responses in both rounds of data collec­ tion about current numbers of employees in various job titles and percentages of each job titles' time assigned to mainte­ nance of the transit fleet. This matrix provided total num­ bers of employees with various job titles and the percentage of time spent by each group in various roles.
From page 21...
... 21 hour. Looking at the data gathered across both rounds of data collection, the research team found that there is indeed a correlation between fleet size and hours of maintenance per vehicle (R2 = 0.316)
From page 22...
... 22 via parts costs. It is highly likely that a different subset of these factors and other unique factors are affecting each individual agency's staffing level.
From page 23...
... 23 21.7 23.1 6.2 16.0 9.1 0.0 R² = 0.6797 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 >1000 Buses Maintained (n=6) 500 999 Buses Maintained (n=7)
From page 24...
... 24 in a fleet increases, the hours of maintenance per vehicle will tend to decrease (R2 = 0.1912) (see Figure 27)
From page 25...
... 25 was identified for these variables (R2 = 0.023 for duty cycle, R2 = 0.014 for fleet age)
From page 26...
... 26 may have included all of the maintenance work orders gen­ erated from those inspections, and yet another group may have included almost all maintenance except repairs from an in­service breakdown. Effect of Investment in Training The agencies that calculated the amount of training per technician each year reported a wide variation in training investment.
From page 27...
... 27 Supply Model versus Demand Model The research reported herein has so far been focused on actual staffing levels. The agencies also reported demanded maintenance, but this did not always correlate with reported staffing levels.

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