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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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Suggested Citation:"Chapter 6 - Tenure and Performance." National Academies of Sciences, Engineering, and Medicine. 2010. Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations. Washington, DC: The National Academies Press. doi: 10.17226/14415.
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65 It is also commonly felt that if vehicle operators can be retained and are satisfied in their jobs, improved performance should be the end result. The added experience on the job should allow operators to be more efficient. “The right” oper- ators, working in a supportive environment and satisfied with their jobs, should also help to ensure that higher quality ser- vice is provided. While the above seems obvious, little actual research could be found to document that improved performance does in fact result from lowering turnover and retaining vehicle oper- ators. To begin to document the extent to which performance is influenced by tenure, two ADA paratransit systems were selected and studied in detail. One system was the DART pro- gram in Dallas, Texas. The second was the LYNX program in Orlando, Florida. These two systems were selected for the fol- lowing reasons: • They were large enough to provide a pool of vehicle oper- ators with an adequate number of operators in various tenure groups; • They had moderate to high turnover, which was assurance that there would be enough new operators to make an effective comparison; and • They utilized state-of-the-art paratransit software that allowed for the creation of special queries on operator and trip data. At the time of the study, the DART system in Dallas employed a total of 339 ADA paratransit vehicle operators and reported an annual post-training turnover rate of 48%. The LYNX program in Orlando employed 247 ADA para- transit vehicle operators and reported an annual post-training turnover rate of 70%. These systems were also selected to provide some contrast in operating designs. The DART service is centrally scheduled with contracted vehicle operations. Reservations, scheduling and dispatching are performed in-house by DART with its own public employees. Vehicle operators are hired and supervised by a contracted private carrier. The LYNX service is a single-contractor, turnkey operation. A private contrac- tor performs all aspects of service delivery including reserva- tions, scheduling, dispatching, and vehicle operations. With the cooperation of these systems, productivity was compared for similar runs performed by operators with dif- ferent levels of tenure. On-time performance was also com- pared based on operator tenure. And the rates of complaints per operator per month were calculated for different tenure groups. Following are descriptions of the methodologies used and the outcomes obtained. Impacts of Tenure on Productivity Methodology At each system, a week of reconciled trip data was obtained. The data was sorted by day and then by run number. For each run, the number of trips provided and the number of vehicle- revenue-hours was extracted so that productivity (trips per revenue-hour) could be calculated. The employee number of the vehicle operator who performed the run was also obtained. To control for inherent differences that might exist between certain types of runs, the run structure was also reviewed with the lead scheduler at each system. Each lead scheduler was asked to identify runs that might be expected to have different productivities—for example, split runs that operate only dur- ing peak hours versus straight runs that continue through off- peak periods, versus late evening or weekend runs. A code was assigned to each run to indicate its characteristics. Next, a list of vehicle operators was obtained showing the dates of hire. Using the dates of hire, the number of months of tenure of each operator was calculated. This list was then combined with the run/trip data so that it was possible to determine the productivity achieved by each operator and the tenure of the operator that performed each run. C H A P T E R 6 Tenure and Performance

Runs were then sorted based on type and operator tenure groups. The run descriptions were unique to each system. A similar grouping of operators was also done. The tenure groupings chosen for the analysis at both systems were the following: • Less than 3 months of tenure, • 3 to 5 months of tenure, • 6 to 12 months of tenure, • 13 to 24 months of tenure, • 25 to 60 months of tenure, and • 61+ months of tenure. The average productivity achieved on all runs performed by operators in each tenure group was then calculated for each grouping of run type. Productivity Results—DART, Dallas, TX During the week of March 1–7, 2009, in Dallas, TX, a total of 1,614 individual runs were performed. Vehicle operator information was successfully linked to all but one of these runs. The review of the run structure for the DART system iden- tified 22 different types of runs that could have different pro- ductivity characteristics. These included weekday versus week- end runs, morning, midday and afternoon runs, split and straight runs, and back-up runs which DART termed “pro- tects.” There were also special runs that had been created to provide dedicated service to individuals going to and from a major medical facility, termed the Parkland Shuttle runs. Table 6-1 shows the average productivity and total number of runs in the sample week for each run grouping. It also shows the number of runs performed by operators in each tenure group. For many run types, there were not enough runs for the week to provide an adequate sample of operators in that tenure group. For example, even though there were 40 weekday, mid- day straight runs, there was only one run performed by an operator with less than 3 months experience, and only two runs performed by operators in the 3- to 5- and 6- to 12-month tenure groups. Several run groupings did, however, have enough opera- tors in each tenure group to allow for a reasonable compari- son of average productivities achieved. These run groupings included the following: • Weekday AM Splits (266 runs); • Weekday AM Straights (457 runs); • Weekday PM Splits (330 runs); and • Weekday PM Straights (264 runs). Figures 6-1 to 6-4, and the associated data tables, show the productivities by tenure group for each of these types of runs. For the two “straight run” groups, higher productivities were generally achieved by operators with more than 2 years 66 Table 6-1. Number of runs by type and operator tenure group, DART, Dallas, TX, March 1–7, 2009. <3 3-5 5-12 13-24 25-60 61+ Weekday AM Splits 1.80 26 19 31 33 83 74 266 Weekday AM Straights 1.73 29 30 16 34 33 315 457 Weekday Midday Splits 1.81 1 0 10 Weekday Midday Straights 1.66 1 31 40 Weekday PM Splits 1.90 34 32 40 45 86 0 2 3 4 2 2 0 4 93 330 Weekday PM Straights 1.67 26 37 27 38 55 81 264 1 3 10 0 6 25 3 8 8 9 14 45 3 3 0 0 1 10 6 5 5 3 8 33 1 1 0 1 0 3 3 1 3 1 1 16 3 2 0 1 1 9 0 3 1 0 1 6 6 1 2 0 5 17 0 2 0 0 1 4 1 0 0 1 0 6 8 3 4 4 4 32 1 0 0 0 1 4 8 0 1 0 2 14 0 1 0 1 3 6 0 5 1 3 7 17 Weekday Evening 1.18 5 Weekday "Protects" 1.12 3 Saturday AM Splits 1.34 3 Saturday AM Straights 1.40 6 Saturday Midday Splits 1.70 0 Saturday Midday Straights 1.67 7 Saturday PM Splits 1.52 2 Saturday PM Straights 1.64 1 Saturday Evening 1.40 3 Saturday "Protects" 1.03 1 Sunday AM Splits 1.52 4 Sunday AM Straights 1.48 9 Sunday PM Splits 1.58 2 Sunday PM Straights 1.36 3 Sunday "Protects" 1.28 1 Parkland Shuttle 1.50 1 Totals 1.69 1681 164 156 188 289 6491 1614 # of Runs by Tenure Group (in Months)Average Productivity Total RunsRun Description

67 Figure 6-1. Productivity by vehicle operator tenure group, weekday AM split runs, DART ADA paratransit service, March 1–7, 2009. Figure 6-2. Productivity by vehicle operator tenure group, weekday AM straight runs, DART ADA paratransit service, March 1–7, 2009. Figure 6-3. Productivity by vehicle operator tenure group, weekday PM split runs, DART ADA paratransit service, March 1–7, 2009. 0.00 0.50 1.00 1.50 2.00 2.50 <3 3-5 6-12 13-24 25-60 61+ Pr od uc tiv ity Tenure in Months Productivity by Tenure # Runs Tenure Productivity 26 <3 1.65 19 3-5 1.77 31 6-12 1.98 33 13-24 1.66 83 25-60 1.90 74 61+ 1.76 Total Runs: 266 1.62 1.64 1.66 1.68 1.70 1.72 1.74 1.76 <3 3-5 6-12 13-24 25-60 61+ Pr od uc tiv ity Tenure in Months Productivity by Tenure # Runs Tenure Productivity 29 <3 1.66 30 3-5 1.73 16 6-12 1.66 34 13-24 1.72 33 25-60 1.71 315 61+ 1.74 Total Runs: 457 1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.05 2.00 <3 3-5 6-12 13-24 25-60 61+ Pr od uc tiv ity Tenure in Months Productivity by Tenure # Runs Tenure Productivity 34 <3 1.89 32 3-5 1.83 40 6-12 2.02 45 13-24 2.00 86 25-60 2.01 93 61+ 1.80 Total Runs: 330 1.35 1.40 1.30 1.45 1.50 1.55 1.60 1.80 1.65 1.75 1.70 <3 3-5 6-12 13-24 25-60 61+ Pr od uc tiv ity Tenure in Months Productivity by Tenure # Runs Tenure Productivity 26 <3 1.57 37 3-5 1.74 27 6-12 1.46 38 13-24 1.69 55 25-60 1.73 81 61+ 1.70 Total Runs: 264 Figure 6-4. Productivity by vehicle operator tenure group, weekday PM straight runs, DART ADA paratransit service, March 1–7, 2009.

of experience. The group of operators with 3 to 5 months of experience also performed well on these straight runs. Opera- tors with less than 3 months of experience and those with 6 to 12 months of experience performed at lower productivities. The largest group of long-term operators (315) who worked the AM straight runs had the highest productivity in that group. Longer-term operators working PM straight runs also had among the highest productivity in that group. The longer term operators working split runs, however, performed at only a moderate productivity on morning runs and at a low productivity on the afternoon runs. Combining the results for the two groups of split runs (AM and PM) indicates that operators with 6 months to 5 years of experience were about 8% to 9% more productive than oper- ators with less than 6 months experience. For the two groups of straight runs, the pivot point appears to be at about one year of experience. In these two groups, oper- ators with one to 5 years of experience were about 5% more productive than those with less than one year of experience. The fact that it took longer for operators to be as productive on straight runs could be due to the fact that these runs, which go through the midday, often have fewer subscription trips than morning and afternoon split runs that operate more in the peak. Vehicle operators would be making more non- subscription pick-ups throughout the service area and it would take longer to learn the entire area. Another possibility is that the operators with the longest tenure had an idea of which runs had more moderate workloads (e.g., smaller subscription trip groups), and may have chosen those in the run picks. Complete results for all types of runs at DART, including those presented in this section, are provided in the table in Appendix B. Productivity Results—LYNX, Orlando, FL A similar analysis was performed with data from the LYNX ADA paratransit service in Orlando, FL. Data from the week of April 19–25, 2009, was used. During this week, a total of 786 runs were performed and vehicle operator tenure infor- mation was successfully linked to all runs. A standard run structure using staggered straight runs was used. The only real distinction in terms of run type was whether runs were scheduled for weekdays versus Saturdays or Sundays. Therefore, instead of categorizing runs by types, the lead scheduler was asked to give each run an expected produc- tivity rating based on the number and type of subscription trips and “program” trips assigned as well as his general knowledge of the runs. The categorization also depended on whether runs were typically assigned to the urban area or more rural parts of the service area. Using a list of weekday runs, the lead sched- uler categorized each run based on expected productivity using the following general categories: • Low productivity, • Medium-low productivity, • Medium productivity, • Medium-high productivity, and • High productivity. Saturday and Sunday runs were then also considered sep- arate groups. Table 6-2 shows the breakdown of the 786 runs for the sam- ple week by expected weekday and weekend, and weekday productivity category. It also shows the average actual produc- tivity for each run category. As shown in Table 6-2, the lead scheduler’s opinion about the expected productivities of each run was quite accurate. The average actual productivities do in fact increase from the “Weekday Low” category to the “Week- day High” category. Saturday runs had an average productiv- ity that was almost the same as the “Weekday Medium” runs. Sunday runs had an average productivity that was between the “Weekday Low” and “Weekday Medium-Low” levels. As shown in Table 6-2, the only two categories that had a suf- ficient distribution over all operator tenure groups were the runs with expected productivities of “Weekday Medium” and “Weekday Medium-High.” Figures 6-5 and 6-6 show the results of the productivity analysis for each of these categories of runs. For each run group, the results show significantly improved productivity for operators with greater tenure. In the “Week- day Medium” group, operators with 6 months to 5 years of 68 <3 3-5 6-12 13-24 25-60 61+ Weekday Low 0.95 1 15 12 41 Weekday Medium-Low 1.17 0 0 0 11 15 7 4 6 0 30 Weekday Medium 1.22 75 13 54 71 109 74 396 Weekday Medium-High 1.36 17 8 20 48 58 18 169 Weekday High 1.51 2 0 11 27 14 8 62 Saturday 1.23 10 6 14 18 5 7 60 Sunday 1.07 6 2 9 6 3 2 28 Totals 1.25 111 29 134 197 200 115 786 Run Description Average Productivity # of Runs by Tenure Group (in Months) Total Runs Table 6-2. Number of runs by anticipated productivity and operator tenure group, LYNX, Orlando, FL, April 19–25, 2009.

tenure performed at an average productivity or 1.29, compared to an average productivity of only 1.02 for operators with less than 6 months of experience. This represents a 34% increase in productivity. Average productivity for operators with more than 5 years of experience dropped to 1.21—still 17% higher than operators with less than 6 months of experience but 12% less than operators in the 6 month to 5 year tenure category. For the “Weekday Medium-High” group, operators with 6 months to 5 years of tenure performed at an average produc- tivity or 1.40, compared to an average productivity of only 1.18 for operators with less than 6 months of experience. This represents a 19% increase in productivity. Average productiv- ity for operators with more than 5 years of experience dropped only slightly to 1.37—still 16% higher than operators with less than 6 months of experience, and only 2% less than operators in the 6 month to 5 year tenure category. Figure 6-7 shows all weekday runs for the Orlando service and the productivities achieved by operators in each tenure group. Operators with 6 months to 5 years of tenure performed at an average productivity of 1.39, compared to an average pro- ductivity of only 1.02 for operators with less than 6 months of experience. This represents a 24% increase in productivity. Average productivity for operators with more than 5 years experience dropped only slightly to 1.26—still 19% higher than operators with less than 6 months of experience and only 4% less than operators in the 6 month to 5 year tenure category. Complete results for all types of runs at LYNX, including those presented in this section, are provided in the table in Appendix C. Impacts of Tenure On On-Time Performance Methodology The levels of on-time performance achieved by operators in each tenure group were also analyzed during both case study sites. On-time performance was calculated for each run for the sample week. This was done by identifying the num- ber of trips with pick-ups that were performed late compared to the total number of pick-ups on each run. The operators who performed each run were then identified. Using their dates of hire, their tenure in months was then calculated. This tenure information was then attached to each run. On-time 69 Figure 6-5. Productivity by vehicle operator tenure group, weekday medium runs, LYNX ADA paratransit service, April 19–25, 2009. Figure 6-6. Productivity by vehicle operator tenure group, weekday medium-high runs, LYNX ADA paratransit service, April 19–25, 2009. 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 <3 3-5 6-12 13-24 25-60 61+ Pr od uc tiv ity Tenure in Months Productivity by Tenure # Runs Tenure Productivity 75 <3 1.04 13 3-5 0.99 54 6-12 1.25 71 13-24 1.32 109 25-60 1.30 74 61+ 1.21 Total Runs: 396 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.60 1.40 <3 3-5 6-12 13-24 25-60 61+ Pr od uc tiv ity Tenure in Months Productivity by Tenure # Runs Tenure Productivity 17 <3 1.15 8 3-5 1.25 20 6-12 1.37 48 13-24 1.38 58 25-60 1.42 18 61+ 1.37 Total Runs: 166

performance for various tenure groups was then developed. As was done in the productivity analysis, the following tenure groups were used: • Less than 3 months of tenure, • 3 to 5 months of tenure, • 6 to 12 months of tenure, • 13 to 24 months of tenure, • 25 to 60 months of tenure, and • 61+ months of tenure. On-Time Performance Results— DART, Dallas, TX Figure 6-8 shows the results of this analysis for the DART ADA paratransit service. As shown, on-time performance improved significantly for operators with 3 or more months of experience. Operators with less than 3 months experi- ence performed 83.99% of all trips on-time. Operators with between 3 months and 5 years of experience performed between 88.67% and 89.64% of trips on-time. On-time per- formance decreased somewhat for operators with more than 5 years of experience, falling to 86.84%. On-Time Performance Results— LYNX, Orlando, FL The same analysis was performed for the LYNX ADA para- transit service in Orlando. Figure 6-9 shows on-time perform- ance by operator tenure group for all weekday runs. Figure 6-10 then shows this information for the largest group of common runs—those categorized as “Weekday Medium” (or average productivity runs). For all weekday runs, operators with less than 3 months experience were on-time for only 74.48% of all trips. Per- formance improved to 83.60% and 82.62% for operators with 3 to 5 months of experience and 6 to 12 months of expe- rience. On-time performance continued to improve with experience—85.98% for operators with 13 to 24 months of experience, 86.20% for those with 25 to 60 months of experience, and 87.40% for those with more than 5 years of experience. 70 0.20 0.40 0.00 0.60 0.80 1.00 1.20 1.60 1.40 <3 3-5 6-12 13-24 25-60 61+ Pr od uc tiv ity Tenure in Months Productivity by Tenure # Runs Tenure Productivity 95 <3 1.05 21 3-5 1.11 111 6-12 1.26 173 13-24 1.34 192 25-60 1.32 106 61+ 1.26 Total Runs: 698 Figure 6-7. Productivity by vehicle operator tenure group, all weekday runs, LYNX ADA paratransit service, April 19–25, 2009. Figure 6-8. On-time performance by operator tenure in months, all runs DART ADA paratransit service, March 1–7, 2009. Tenure On-Time Performance <3 83.99 3-5 89.64 6-12 88.67 13-24 89.11 25-60 89.25 61+ 86.84 Total Runs: 1560 81.00 82.00 83.00 84.00 85.00 86.00 87.00 88.00 89.00 90.00 91.00 <3 3-5 6-12 13-24 25-60 61+ O n- Ti m e Pe rfo rm an ce Tenure in Months On-Time Performance By Tenure

For the “Weekday Medium” runs, which were the largest number of similar type runs, the difference in performance was even more pronounced. Operators with less than 3 months experience were on-time for only 77.36% of all trips. Operators with 3–5 months of experience performed slightly better with 80.15% of all trips on-time. With 6 to 12 months of experience, operators achieved 84.31% on-time performance. This then capped out at between 86.02% and 86.24% for operators with more than 1 year of experience. Impacts of Tenure on Complaints Methodology A second measure of service quality that was analyzed was operator-related complaints. At each system, several months of complaint data was reviewed. Complaints that were “operator-related” were selected. This included complaints such as improper assistance provided, rude conduct, unsafe driving, etc. For each complaint, the operator was identified. The oper- ator’s tenure at the time of the complaint was then calculated by comparing the date of hire to the date of the complaint. Complaint rates were then calculated by comparing the num- ber of complaints in each tenure grouping to the number of operators in each group. The ratios of complaints per opera- tor per month were then compared for each tenure group. Complaint Rate Results—DART, Dallas, TX Six months of complaint data, from September 2008 through February 2009 was used for the analysis in Dallas, TX. A total of 459 operator-related complaints were analyzed. Figure 6-11 and Table 6-3 show the results of this analysis. As shown in Table 6-3, the rate of complaints increased with operator tenure. Only 7% of the operators with less than 3 months of experience had complaints, and the rate for this group was only 0.03 complaints per operator per month. Operators with 3–5 months experience had a complaint rate between 0.12 and 0.17 complaints per operator per month. Operators with 1 to 5 years of experience had a complaint rate between 0.19 and 0.27 complaints per month. And operators with more than 5 years of experience had complaint rates of 0.21 complaints per operator per month. 71 Figure 6-9. On-time performance by operator tenure in months, all weekday runs LYNX ADA paratransit service, April 19–25, 2009. # Runs Tenure On-Time Performance 95 74.48% 21 83.60% 111 82.62% 173 85.98% 192 86.20% 106 87.40% Total Runs: 698 90.00% 85.00% 80.00% 75.00% 70.00% 65.00% <3 3-5 6-12 13-24 25-60 61+ O n- Ti m e Pe rfo rm an ce Tenure in Months On-Time Performance by Tenure <3 3-5 6-12 13-24 25-60 61+ Figure 6-10. On-time performance by operator tenure in months, weekday medium runs, LYNX ADA paratransit service, April 19–25, 2009. 72.00% 74.00% 76.00% 78.00% 80.00% 82.00% 84.00% 86.00% 88.00% # Runs Tenure On-Time Performance 75 77.36% 13 80.15% 54 84.31% 71 86.02% 109 86.65% 74 86.24% Total Runs: 396 <3 3-5 6-12 13-24 25-60 61+ O n- Ti m e Pe rfo rm an ce Tenure in Months On-Time Performance by Tenure <3 3-5 6-12 13-24 25-60 61+

Complaint Rate Results—LYNX, Orlando, FL Eleven months of complaint data was analyzed in Orlando. A total of 277 operator-related complaints were identified. Fig- ure 6-12 and Table 6-4 show the results of the analysis. The results in this second case study site were very different. As shown, the rate of complaints decreased dramatically as operators gained more experience. Operators with less than 3 months experience had a complaint rate of 0.08 complaints per operator per month. This decreased steadily to 0.02 to 0.03 for operators with 5 to 7 months of experience. Operators with more than 8 months of experience had a complaint rate of only 0.01 to 0.02 complaints per operator per month. Given these dramatically different results for the two case study sites, further research in this area is clearly needed. Summary of Findings This analysis suggests that greater job experience does translate into increased productivity. In Dallas, operators per- forming split shifts who had at least 6 months of experience were 8% to 9% more productive than operators working sim- ilar shifts who had less than 6 months of experience. Opera- tors working straight shifts and who had from 1 to 5 years of experience were 5% more productive than those working sim- ilar sifts who had 1 year or less of experience. In Orlando, the difference was much greater. Operators with at least 6 months of experience were 24% more produc- tive than those with less than 6 months of experience. In both systems studied, productivity continued to increase with experience, but a slight drop-off in productivity was documented in both systems for operators with more than 5 years of experience. Operators with more than 5 years of experience were still, however, more productive than those with less than 6 months experience. This drop-off in longer- term productivity could point to the need for ongoing train- ing and incentives. Information from both systems studied also indicated marked improvements in on-time performance with increased 72 Table 6-3. Complaints by operator tenure, DART ADA paratransit service, September 2008 through February 2009. Figure 6-11. Complaint rates by operator tenure (in months), DART ADA paratransit service, September 2008 through February 2009. 0 0.05 0.1 0.15 0.2 0.25 0.3 603624181265431 Co m pl ai nt s pe r O pe ra to r p er M on th Tenure Tenure in Months Complaints per Driver Months of Data Complaints per Driver per Month Percent of Drivers with Complaints 61+ 1.26 6 0.21 55% 36-60 1.61 6 0.27 50% 24-35 1.17 6 0.19 42% 18-23 1.25 6 0.21 58% 12-17 1.15 6 0.19 58% 6-11 1.50 6 0.25 60% 5 1.00 6 0.17 36% 4 0.71 5 0.14 43% 3 0.47 4 0.12 29% <3 0.07 2 0.03 7%

experience. In Dallas, on-time performance was almost 5 per- centage points higher for operators with more than three months of experience compared to operators with less than 3 months of experience. On-time performance did not vary much after operators had at least 3 months of experience. In Orlando, on-time performance improved by between 3 and 8 percentage points once operators had at least 3 months of experience. On-time performance continued to increase with experience, eventually operators with the most experi- ence were between 9 and 13 percentage points higher com- pared to those who were new to the job. The review of complaint rates produced mixed results. In Orlando, operators with more experience provided service with fewer incidents of complaints. In Dallas, complaint rates increased with job tenure. These mixed results indicate that more research is needed in this area. 73 Figure 6-12. Complaint rates by operator tenure (in months), LYNX ADA paratransit service, July 2008 through May 2009. 0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080 1 92 3 4 5 6 7 8 10 11 12 18 24 36 48 60 Co m pl ai nt s pe r O pe ra to r p er M on th Tenure Table 6-4. Complaints by operator tenure, LYNX ADA paratransit service, July 2008 through May 2009. Tenure (Months) # of Complaints # of Drivers # of Drivers with Complaints Complaints per Driver Months of Data Complaints per Driver per Month % of Drivers with Complaints 1 105 7 7 0.08 1 0.08 6.67% 2 86 6 0.07 2 0.03 6.98% 3 11 76 10 0.14 3 0.05 13.16% 4 76 4 0.05 4 0.01 5.26% 5 13 75 11 0.17 5 0.03 14.67% 6 12 71 10 0.17 6 0.03 14.08% 7 17 72 13 0.24 7 0.03 18.06% 8 62 8 0.15 8 0.02 12.90% 9 71 9 0.13 9 0.01 12.68% 10 12 77 11 0.16 10 0.02 14.29% 11 9 9 9 6 4 8 76 0.12 11 0.01 9.21% 12 53 349 42 0.15 11 0.01 12.03% 18 18 219 18 0.08 11 0.01 8.22% 24 47 234 40 0.20 11 0.02 17.09% 36 13 150 12 0.09 11 0.01 8.00% 48 13 129 12 0.10 11 0.01 9.30% 60 23 318 19 0.07 11 0.01 5.97% TOTAL: 277 2246 239

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TRB’s Transit Cooperative Research Program (TCRP) Report 142: Vehicle Operator Recruitment, Retention, and Performance in ADA Complementary Paratransit Operations provides guidance for understanding the relationships that influence and enhance operator recruitment, retention, and performance in Americans with Disabilities Act (ADA) complementary paratransit services.

Appendixes to TCRP Report 142 were published electronically as TCRP Web-Only Document 50: Survey Instrument, Productivity Charts, and Interview Protocol for Case Studies for TCRP Report 142.

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