National Academies Press: OpenBook

Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services (2006)

Chapter: Chapter 5 Results and Performance Evaluation

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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
×
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Suggested Citation:"Chapter 5 Results and Performance Evaluation." National Academies of Sciences, Engineering, and Medicine. 2006. Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services. Washington, DC: The National Academies Press. doi: 10.17226/13955.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

18 This chapter synthesizes the findings of the preliminary and detailed case studies to identify applicable traits that transit agencies can consider in establishing suburban transit services. To show how the applicable traits may relate to one another, the chapter presents several analyses. However, because the findings of this study were insuffi- cient to establish concrete guidelines for all transit agencies, each of the analyses in the chapter uses only a few case studies. Therefore, the findings of the analyses in this chapter cannot be extended to all transit agencies. Nonetheless, the traits identified in this chapter can help transit agencies think about the issues involved in sub- urban transit services. Analysis of Land Use versus Transit Service and Operating Performance An analysis was performed on routes in Albany, Detroit, Minneapolis, and Portland to determine the relationship of land use (i.e., service area characteristics) to transit serv- ice characteristics and operating performance measures. Figure 5-1 shows this research objective, and Figure 5-2 shows the types of suburban transit services available. Fig- ure 5-3 shows the routes that were analyzed for this portion of the report. Figures 5-4, 5-5, and 5-6 show the “spatial adaptation” (i.e., flexibility of location), “temporal adapta- tion” (i.e., flexibility of time), and demand level, respec- tively, of all the routes. Although no clearly defined characteristics were isolated, a series of findings were made: • Most services are in areas with fewer than 20,000 trip ends per square mile (see Figure 5-7). • The best performing services (with performance measured in passengers per hour) are among the least flexible (see Figure 5-8). • Ridership is not a simple function of density. Local policy decisions often appear to accept lower productivity (as measured in passengers per hour) as a trade-off for increased coverage (see Figure 5-9). • The best performing routes (with performance measured in passengers per hour) are among those serving the most balanced mix of land uses (see Figure 5-10). • Services that target specific groups, such as seniors and stu- dents, seem to be among the most productive (with pro- ductivity measured in passengers per hour and weekday revenue-hours) (see Figure 5-11). C H A P T E R 5 Results and Performance Evaluation Service Area Characteristics Density Diversity Design Deterrents to driving Transit Service Characteristics Service Format Service Design Parameters Productivity Cost Operating Performance Measures Figure 5-1. Research objective: To determine the relationship of land use (i.e., service area characteristics) to transit service charac- teristics and operating performance measures.

19 su bu rba n t ran sit ser vic es ur ban tra ns it se rv ice s ru ra l tra ns it se rv ice s targeted m arkets flexible routes subscription feeders deviated routes dial-a-ride feeders vanpool area circulators commuter express employer shuttles door-to-door demand response traditional fixed routes Spatial Adaptation low high Te m po ra l A da pt at io n high low Figure 5-2. Spatial and temporal flexibility, or “adaptation,” of different types of suburban transit services. Figure 5-3. Case study routes that were analyzed for this portion of the report.

20 Note: Numbers on the y-axis represent the number of routes analyzed for this portion of the report. Bars without x-axis labels represent services that incorporate characteristics of both the service to the right and the service to the left of the bar. Note: Numbers on the y-axis represent the number of routes analyzed for this portion of the report. Figure 5-4. Spatial adaptation of the routes analyzed for this portion of the report. Figure 5-5. Temporal adaptation of the routes analyzed for this portion of the report.

Finding: The best performing services are among the least flexible (with performance measured in passengers per hour). Note: See Figure 5-3 for a “key” to the colors and abbreviations listed here. Figure 5-6. Demand level of the routes analyzed for this portion of the report. Figure 5-7. Density versus transit service. Figure 5-8. Route flexibility or time flexibility versus productivity. Note: Numbers on the y-axis represent the number of routes analyzed for this portion of the report. Note: See Figure 5-3 for a “key” to the colors and abbreviations listed here. Finding: Most services are in areas with fewer than 20,000 trip ends per square mile.

22 Figure 5-9. Trip density versus productivity. Figure 5-10. Land-use mix versus productivity. Figure 5-11. Service level versus productivity. Note: See Figure 5-3 for a “key” to the colors and abbreviations listed here. Finding: Ridership is not a simple function of density. Local policy decisions often appear to accept lower productivity as a trade-off for increased coverage (with productivity measured in passengers per hour). Note: See Figure 5-3 for a “key” to the colors and abbreviations listed here. Finding: The best performing routes (with performance measured in passengers per hour) are among those serving the most balanced mix of land uses. Note: See Figure 5-3 for a “key” to the colors and abbreviations listed here. Finding: Services that target specific groups seem to be among the most productive (with productivity measured in passengers per hour and weekday revenue-hours).

23 Analysis of Performance Measurement versus Demographics, Service Delivery, and Pedestrian Network Following the land-use analysis, a more traditional transit performance measurement analysis was performed, with demographics, service delivery, and pedestrian network eval- uated for the case study routes. The routes were characterized in two ways: (1) by the trip type served (the home end of a trip versus the work end of a trip) and (2) by the type of service (local fixed route, flexible route, and commuter). Each route’s service area was defined as follows: • Fixed route—all areas within one-quarter mile air distance of any branch of the route. • Dial-a-ride—the dial-a-ride service area. • Deviated route—the combination of the route deviation area and all other areas within one-quarter mile of the fixed-route portion of the route. • Commuter—the areas within one-quarter mile of the local service portion of the route, where customers would mainly be boarding in the morning. The destination ends of the routes (transit centers) were not included. Table 5-1 shows the routes that were evaluated and their characteristics. Demographics The smallest geographic unit available—either Census block group or Census traffic analysis zone (TAZ)—was used to estimate the viability of transit service in a given area: • Population density—the number of persons per square mile within the service area. • Job density—the number of employees per square mile within the service area. • Percentage of population 0-17 years old. • Percentage of population 65 or more years old. • Percentage of households with no vehicles available. • Percentage of employees with no vehicles available at home. • Average median income—the median income was known for each census block or TAZ; a weighted average of these median incomes was determined for the service area as a whole. Service Delivery The following service delivery variables were evaluated: • Adult peak fare—the lowest (e.g., one-zone) adult fare during peak periods. • Service area—calculated in square miles,using GIS software. • Weekday TLOS indicator—the Florida Transit Level of Ser- vice (TLOS) indicator1 measures a combination of service frequency and span. In this application, it measures the per- centage of a weekday that locations within the service area have access to transit. Pedestrian Network The following factors relating to street network connectiv- ity were evaluated: • Network connectivity factor—the number of links (i.e., street segments between intersections) within the service area, divided by the number of nodes (i.e., intersections). • Average minimum circularity ratio—the minimum cir- cularity ratio was determined for all blocks falling within a given one-half mile grid square, and the average of the minimum circularity ratios was calculated based on all grid squares intersecting a route’s service area. • Average block size factor—the ratio of a block’s area (in square miles) to one-fiftieth square mile. An average value of 1.0 or less suggests a relatively dense, walkable street net- work. The average block size factor was calculated based on all blocks intersecting a route’s service area. Findings Figures 5-12 through 5-15 highlight the most promising rela- tionships between the evaluated factors and route productivity. The six flexible-route services showed a strong correlation between population density and productivity (see Figure 5-12), which contrasts with the more limited correlation between trip density and productivity shown previously in Figure 5-9. The remaining local fixed-route services showed a fairly weak cor- relation between population density and productivity. There was some correlation between the productivity of the employer-oriented services and the percentage of employees who had no vehicle at home (see Figure 5-13). There was some correlation between productivity and the amount of service provided, as measured by the Florida TLOS indicator, which includes both the span and frequency of service (see Figure 5-14). There was relatively good correlation between productivity and the service area size, with the result that the larger the service area, the less productive the service (see Figure 5-15). Factors that showed no apparent correlation with route productivity included fares, percentage of population under 18, and walkability. 1 Ryus, Paul, Jon Ausman, Daniel Teaf, Marc Cooper, and Mark Knoblauch, “Development of Florida’s Transit Level of Service Indicator,” Transportation Research Record 1731, Transportation Research Board, National Research Council, Washington, DC (2000).

24 Figure 5-12. Population density versus productivity. R2 = 0.9514 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 Productivity (passengers/revenue hour) Po pu la tio n De ns ity (p ers o n s/ sq ua re m ile ) Fixed Route Flexible Route Commuter Linear (Flexible Route) MVRTA 224 Margate Portland/Wilsonville TriMet 41 Table 5-1. Routes evaluated for the effects of demographics, service delivery, and pedestrian network on performance measurement. Route Agency Type Trip End Margate A BCT Fixed Route Home Margate B BCT Fixed Route Home Margate C BCT Fixed Route Home Margate D BCT Fixed Route Home Cedar Mill Shuttle TriMet Dial-a-Ride Home 155 Sunnyside TriMet Fixed Route Home 156 Mather Rd TriMet Fixed Route Home 157 Happy Valley TriMet Fixed Route Home 204 Wilsonville Rd SMART (Wilsonville) Fixed Route Home 903 Federal Way King County Metro Deviated Route Home 914 Kent King County Metro Deviated Route Home 927 Issaquah-Sammamish King County Metro Deviated Route Home 421 Burnsville-Savage MVRTA Deviated Route Home 152 Milwaukie TriMet Fixed Route Work 41 Hawthorn Farm TriMet Fixed Route Work 50 Cornell Oaks TriMet Fixed Route Work 201 Barbur SMART (Wilsonville) Commuter Work 1X Salem SMART (Wilsonville) Commuter Work 291 Redmond King County Metro Deviated Route Work 224 Shoreview-Roseville MVRTA Fixed Route Work

25 R2 = 0.3318 R2 = 0.3855 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 Productivity (passengers/revenue hour) TL O S In di ca to r V al ue (% of D ay w ith S er vic e) Cedar Mill Shuttle excluded Figure 5-14. Florida Transit Level of Service (TLOS) indicator versus productivity. Figure 5-13. Productivity of employer-oriented services versus the percentage of employees who had no car at home. R2 = 0.3854 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 Productivity (passengers/revenue hour) Pe rc en t J ob s w /N o Ca r Home Work Linear (Work) TriMet Cedar Mill Shuttle

26 R2 = 0.4086 R2 = 0.4945 0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.000 10.000 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 Productivity (passengers/revenue hour) Se rv ic e Ar ea (s qu are m ile s) Figure 5-15. Service area size versus productivity. The demand-responsive services, when looked at as a group, tended to show better correlation for several factors than the correlation shown for these factors by other serv- ices. Two possible explanations for this are that (1) the demand-responsive services tended to serve larger areas than the fixed-route and commuter services and (2) none of the demand-responsive services overlapped with each other. To sum up, many of the fixed-route services that were stud- ied had service areas that significantly overlapped with other fixed-route services. Because the overlapping services covered areas with relatively similar population densities, any differ- ences in productivity would be the result of other factors. In contrast, all of the demand-responsive services that were studied served unique areas that were not part of the service areas of other studied routes. Thus, the variety of services that were included in this analysis from various parts of the coun- try did not provide many significant findings, with the fol- lowing exception: Population density, not trip density, proved to have a good correlation to productivity, especially for demand-responsive services. Relating the Land-Use Analysis to the Transit Performance Measurement Analysis Matrixes can be developed to help transit agencies deter- mine the most appropriate form of transit and to measure performance over time. Table 5-2 provides an example matrix comparing performance measures with service types for the services discussed thus far in this chapter. The left group of performance measures represents traditional measures that most transit agencies already use. The right group of per- formance measures represents nontraditional measures sug- gested in this report based on land use (i.e., service area characteristics) and transit service characteristics. The center group of performance measures represents the application of both groups of measures to the routes discussed thus far in this chapter. The services have been structured to isolate performance measurement ranges that transit planners can use in estab- lishing their own services and measures for evaluation. Activity Surface Example As indicated in a previous section, where sufficient demo- graphic and land-use data are available in GIS format, an activity surface can be created to depict the land-use and travel patterns. Figures 5-16 through 5-18 and Table 5-3 show an example of how this activity surface can be tied to sub- urban transit service planning. The agency featured in this example is MetCouncil in Minnesota. Route 224 serves an area with more density and destina- tions than Route 421. This is depicted in Figure 5-16 by the

Performance Measures Traditional measures* Application of traditional and nontraditional measures to case study routes Nontraditional measures suggested in this report* Service Types A nn ua l R id er sh ip A nn ua l R ev en u e- H ou rs Pa ss en ge rs p er H ou r A nn ua l R id er sh ip A nn ua l R ev en u e- H ou rs Pa ss en ge rs p er H ou r W ee kd ay T LO S In di ca to r Se rv ic e A re a N et w or k C on ne ct iv ity In de x Pr od uc tiv ity W ee kd ay T LO S In di ca to r Se rv ic e A re a N et w or k C on ne ct iv ity In de x Pr od uc tiv ity Fixed Route 11,599 1,770 6.6 11,599 1,770 6.6 0.056 4.387 1.57 6.6 0.056–0.125 1.408– 5.164 1.410– 1.680 6.600– 32.300 13,970 1,778 7.9 13,970 1,778 7.9 0.292 2.660 1.37 7.9 0.292 2.66 1.37 7.9 Flexible 4,064– 29,464 953– 16,929 1.0-8.8 4,352 953 4.6 0.028 7.506 1.59 4.6 0.028–0.111 3.924– 8.947 1.47–1.59 4.6–15.5 Commuter 83,800 5,080 16.5 83,800 5,080 16.5 0.080 2.091 1.84 21.3 0.056– 0.080 2.091– 2.919 1.77–1.84 21.3–25.9 Weekday TLOS Indicator = % of day service provided to bus stops along route, with each fixed-route-only bus providing 10 minutes worth of transit access. Service Area = route service area (square miles). Network Connectivity Index = (# of intersections/# of links) in area served by route (<1.30 = cul-de-sac pattern, 1.30-1.55 = hybrid pattern, >1.55 = grid pattern). Productivity = boardings per revenue-hour. *Values in these columns represent typical ranges or averages that transit agencies may find for their services. Demand Responsive Table 5-2. Service types versus performance measures. Performance Measure Route 224 Route 421 Annual passengers 11,599 4,352 Revenue-hours 1,770 953 Passengers per hour 6.6 4.6 Cost per passenger $6.48 $18.73 Cost per hour $42.45 $85.54 Source: MetCouncil Route Profiles Table 5-3. Operational performance of MetCouncil Routes 224 and 421. Figure 5-16. MetCouncil (Minnesota) activity surface.

28 Figure 5-17. Route 224 map. LAKE JOHANNA LAKE JOSEPHINE SNAIL LAKE enil ma H not gni xeL gnill en S S ne lli ng Av e Lydia weivri aF enil ma H palnu D Red Fox Rd Grey Fox Rd Old Hw y 10 not gni xeL Jose hp ine V ic to ria P P 51 Hwy 96 Hwy 36 Hwy 35W 694 Arden Hills Roseville Shoreview C Co Rd D Co Rd E Co Rd F Co Rd C2 Co Rd B2 Co Rd B Co Rd Twin Cities Arsenal Ramsey County Library Arden Hills Business Park Bethel College CLC Atrium Apartments Arden View Townhomes Shoreview Mall Midwest Special Services SuperTarget 62 1 2 3 Rosedale Transit Center 260 223 224 225 226 227 52R 801 65 To St. Paul: To Roseville: To Minneapolis: 32 8483 87 224 226 227 62 To St. Paul: To Roseville: Shoreview Community Ctr 227 darker shading and numerous ridges. Thus, the planning solution for Route 224 was to add a few route deviations, but maintain a general fixed-route orientation (see Figure 5-17). Conversely, Route 421 was designed to serve a broader area with flexible-route service, which has more demand-responsive characteristics than fixed-route char- acteristics (see Figure 5-18). The differences from serving diverse markets and densities are reflected in the opera- tional information, which shows higher ridership, more service hours, and lower costs for Route 224 than for Route 421 (see Table 5-3). Thus, the efficiency and effectiveness, measured in passengers per hour and cost per passenger, was better for the route-deviation service (Route 224), although more service area coverage was provided under the flexible-route service (Route 421). Note: Although MetCouncil and some other areas that were examined in this study had the necessary demographic and land-use information available to display in GIS format, most areas either did not have data readily available in this format or had the data housed in multiple agencies, which resulted in numerous difficulties in collection, display, and comparison with transit data. There are indications that more areas are looking to connect the land-use and transit data in GIS format, which in the long term will assist in developing more land-use and transit relationships for consideration in suburban service planning.

Analysis of Passengers per Revenue-Hour versus Transit Use Factors An analysis was used to assess performance of services in a single region. The community of Margate in Broward County, Florida, demonstrates how a network of suburban services can be developed and what performance can be expected as a result of an areawide analysis. The relationships between passengers per revenue-hour and such measures as population density, income, the elderly seg- ment of the population, the student-age segment of the popu- lation, the number of owner-occupied units, the number of renter-occupied units, and number of car owners were tested at the route level with data derived from the census blocks. The data permitted a Pearson correlation analysis to be conducted to measure the magnitude and sign of these relationships.2 Find- ings are summarized in Table 5-4 and include the following: • The correlation between passengers per revenue-hour and income shows clearly that as the level of income declines, the number of passengers per revenue-hour rises. This noticeable inverse relationship confirms standard transit use theory, which says that lower income, particularly in areas of higher population density, increases transit use. • The elderly and student-age segments are both positively correlated to passengers per revenue-hour. This finding also confirms transit use theory, though in this sample set, the relationship is minimal to nonsignificant. However, 29 Table 5-4. Passengers per revenue-hour (pass. rev. hr.) versus transit use factors in Broward County, Florida. Figure 5-18. Route 421 map. BROOKVIEW DR. HIGHLAND DR. JU D IC IA L RD. T H O M A S A V E . S T E V E N R D . R O S E M O U N T D R . A V E . .TS G L E N H U R S T W A S H B U R N A VE HWY 13 FRONTAGE RD HWY 13 FRONTAGE RD A V E . C H O W E N A VE . P R IN C E T O N Q U E N T IN A V E . McCOLL DR. 133rd .RD NAGE D A K O T A A V E . S . ST. U T IC A A V E . S O U T H C R O S S DR. A V E . A V E . 137t h PK W Y. WIL LIAM S DR. V E R N O N L Y N N J O P P A A V E . W. 531 A V E . N A T C H E Z A V E . .TS ht041 .TS ht041 S ht421 .T ht521 135th 140th ST. th A V E . JO PP A ST . G L E N D A L E RD . YELLAV NEDDIH .W ht931 .W TS B U R N S V IL L E H U N T IN G T O N A V E . D A K O T A A V E . P K W Y . BUR NS VILL E DR. HTUOS DR . PARK P O R T L A N D A V E . RD . D R . R ID G E P L E A S A N T A V E . P IL L S B U R Y ST. H A R R IE T A V E . N IC O L L E T WO O D H I L L IR VIN G G IR A R D SLATER LA. A L D R IC H A V E . LA. ST. ST. .TS A V E . A L D R IC H A V E . IR V IN G A V E . ST. B U C K H IL L R D . D R . DR. ST. TR. DR. B U R N H A V E N D R .RD. BL VD . A V E . P K W Y . CENTER 134th 140 th dr341 ht051 ht621 W .TS ht631 . ht 631 A V E . A VE . ht031 F A IR V IE W CIVIC Q U E N T I N O 'C O N N E L L R D O T T A W A 138th ST. U P T O N A V E . M A N O R LACOTA E. TR AV ELE RS CLIFF P O R T L A N D NICOLLET IN G LE W O O D N IC O LL E T AV E. McA NDRE W S PO R TL A N D B U R N H A V E N D R . KE BL VD SOUTHCROS SOUTHCR OS S JUDICIAL R D EAG L E BURN SAVAGE Lake Earley Sunset Pond 13 13 5 5 38 4242 42 31 27 16 35W 35W P P Nicollet JHS Cub Foods Burnsville North Lynn Court Housing Savage Park & Ride H P Eagle Ridge JHS Savage Library M.W. Savage Elem. Sch. Hidden Valley Elem. School Harriet Bishop Elem. Sch. City Hall Burnsville Center Camelot Acres Rambush Estates DUFFERIN DR. DUFF ER IN D R. RIVER CROS SIN G 27 NOTE: Buses serve office only at Camelot Acres and Rambush Estates NORTH Burnsville Transit Station Point Of Interest Time Point Regular Route Flag Stop Deviation Area Park & RideP FLEX Route 421 The colored area on the map shows where FLEX Route 421 service is available. FLEX vehicles will stop at all of the flag stops and time points. f you cannot get to one of the stops, or your destination is not close to a FLEX stop, call (952) 882-6000 to make a reservation. Please plan to arrive at your stop or be ready for your scheduled pickup 5-10 minutes ahead of schedule as the bus cannot wait if you are not ready. 2 Hyperstat Online Contents,“Pearson’s Correlation,”http://davidmlane.com/hyper- stat/A62891.html: Pearson’s correlation reflects the degree of linear relationship between two variables. It ranges from 1 to 1. A correlation of 1 means that there is a perfect positive linear relationship between variables. It is a positive relationship because high scores on the X-axis are associated with high scores on the Y-axis. A correlation of 1 means that there is a perfect negative linear relationship between variables. It is a nega- tive relationship because high scores on the X-axis are associated with low scores on the Y-axis. A correlation of 0 means there is no linear relationship between the two variables. Transit Use Factor Pearson’s Correlation Pass. Rev. Hr./Income -0.57648 Pass. Rev. Hr./Elderly Segment 0.061163 Pass. Rev. Hr./Student Segment 0.090209 Pass. Rev. Hr./Population Density 0.83333 Pass. Rev. Hr./Owner-Occupied -0.39667 Pass. Rev. Hr/Renter-Occupied 0.036481 Pass. Rev. Hr./Owner-Occupied, No Car 0.694742 Pass. Rev. Hr./Owner-Occupied, 1 Car 0.380401 Pass. Rev. Hr./Renter-Occupied, No Car 0.520486 Pass. Rev. Hr./Renter-Occupied, 1 Car -0.12368

what goes against standard transit use theory is that the correlation between student population and transit rider- ship is stronger than the correlation between elderly pop- ulation and transit ridership. The youth were more likely to use transit than the elderly. • Population density is highly positively correlated to pas- sengers per revenue-hour in the routes analyzed, so stan- dard transit use theory holds firmly in this local circulator setting as well. Higher population density results in higher transit usage. • The segment of owner-occupied units with no car was strongly correlated to passengers per revenue-hour. This finding is consistent with standard transit use theory, which says that lack of auto ownership increases transit use. • The segment of owner-occupied units with one car is also positively correlated with passengers per revenue-hour. This might be because the owner-occupied households with only one car have more people in the household with mobility needs that are not being met with a single car. This finding is again consistent with standard transit use theory, which says that lack of auto ownership increases transit use. • The segment of renter-occupied units with no car is posi- tively correlated with passengers per revenue-hour, once again consistent with the notion that the absence of personal transportation, especially in the case of persons renting units, implies transit use for many trip purposes. • The segment of renter-occupied units with one car is slightly negatively correlated with passengers per revenue- hour. Thus, as renters obtain personal vehicles, ridership on the shuttle system declines. This finding might reflect that renter-occupied units have fewer people and less travel demand. It might seem obvious that certain demographic charac- teristics contribute to better transit ridership, but with such limited experience in the provision of local circulators in pri- marily suburban settings, it was of value to confirm if normal indicators of transit use potential apply to local circulators as they do to regular fixed-route transit service in a more regional setting. As noted above, there is a very strong positive relationship between transit use and population density for the local circulators that were studied. In short, the higher the density, the higher the transit ridership per hour was for the local circulators. There was also a high positive correlation between lack of car ownership and transit use. Perhaps a little surprising was that the relationship was even stronger for owner-occupied dwellings without cars (0.69) than for renter-occupied dwellings without cars (0.52). As expected, there was also a strong negative correlation (0.58) between income and tran- sit ridership per hour. In other words, the higher the income, the lower transit ridership per hour was in the local circulator systems. Although this study focused on data from only one com- munity—Margate in Broward County, Florida—the results are consistent with typical transit analyses of data from many areas, with the exception of the finding that youth were more likely to use transit than the elderly. This general consistency of findings indicates that measuring similar services within a given geographic area would likely lead to more specific findings. Establishing Performance Measurement Programs Evaluating suburban services is an important component of the successful implementation of suburban services. Not only is it important to ensure that the form of transit is appropriate for the market, but equally important is ensuring that expectations in a community are commensurate with performance.No other form of public transit engenders more local characteristics than suburban transit. Suburban transit is at the local level where the balance between resource expenditures and the need for enhanced service coverage must be determined. Following is an example of how one agency, in the imple- mentation of a broad family of services, manages perform- ance and expectations for service performance with its stakeholders and the broader community. Denver RTD has established guidelines in its service stan- dards that the least productive 10% of routes, based on either subsidy per boarding or boardings per hour, need to be evaluated for marketing, revision, or elimination. The same evaluation is applied to routes when both subsidy per board- ing and boardings per hour fall within the least productive 25%. The calculation of the 10% and 25% standards is made from the annual unweighted data, assuming that the data have a normal distribution and using the appropriate formu- las for standard deviation and confidence intervals. However, the standard deviation is applied to the weighted average. Table 5-5 gives the weighted averages and standards by class of service. RTD’s general approach is as follows. Develop a family of services suited to a variety of markets. Connect all the serv- ices together to accommodate the region’s dispersed travel patterns. Match the level of service with demand, thereby improving performance and sustainability. At RTD,“performance”is a term often used interchangeably with “effectiveness” and “efficiency.” “Effectiveness” measures attainment of the objective—maximize ridership within the budget—and is presented on the vertical axis of Figure 5-19 as subsidy per vehicle. “Efficiency”—productivity or output/ input—is presented on the horizontal axis as boardings per hour. 30

RTD service standards are depicted in Figure 5-19 to help decision makers make judgments about performance. Each shaded rectangle represents the domain for routes that meet or exceed the 10% minimum performance requirements for that service type.“10% minimum” is defined as meeting or exceed- ing 10% of the performance for all routes in each category. RTD’s graph makes it easy to single out poorly performing routes for further evaluation. Other transit agencies can use this graph as a model to create similar graphs relevant to their own areas. By evaluating the suburban services, transit agen- cies can ensure that expectations in a community are com- mensurate with performance. 31 Table 5-5. Denver RTD subsidy per boarding and boardings per hour. ($35) ($30) ($25) ($20) ($15) ($10) ($5) $0 0 5 10 15 20 25 30 35 Boardings Per Vehicle Hour Su bs id y Pe r V eh ic le Minimum productivity for 15 min frequency Shaded domains contain all routes meeting the 10% minimum standards 75 403 470Ltd 7759Ltd 60 426 325 402Ltd 327 324 121 169Ltd66 169 Lone Tree Longmont Gateway Note: triangles represent fixed routes, and diamonds represent call-and-ride routes. Figure 5-19. Denver RTD service performance for suburban local routes and call-n-ride services. Subsidy per Boarding Boardings per Hour Class of Service Average 10% Max. 25% Max. Average 10% Min. 25% Min. CBD Local $ 2.72 $ 6.52 $ 4.71 33.3 18.5 25.6 Urban Local 3.51 11.20 7.53 26.2 14.3 20.0 Suburban Local 7.95 18.48 13.46 14.4 6.6 10.3 Call-n-Ride 14.76 24.38 19.79 4.1 0.7 2.3 Express 6.22 13.86 10.22 28.5 8.8 18.2 Regional 6.82 14.46 10.81 18.2 10.7 14.3 SkyRide 4.26 6.37 5.37 18.3 13.0 15.5 Vanpool 1.19 N/A N/A 5.2 N/A N/A CBD = central business district.

Next: Chapter 6 Lessons and Conclusions »
Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services Get This Book
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TRB’s Transit Cooperative Research Program (TCRP) Report 116: Guidebook for Evaluating, Selecting, and Implementing Suburban Transit Services examines the current status of suburban transit services and land-use environments and the relationship between the two. Types of suburban transit services include commuter, route deviation, demand response, circulators, shuttles, and vanpools. Also, the guidebook describes the emerging trends that significantly influence the availability and operation of suburban transit services.

TCRP Web-Only Document 34, is the companion document to the guidebook. TCRP Web-Only Document 34 includes eight case studies that describe the types of suburban transit services offered; the types of operational issues; the funding arrangements; the marketing program; the performance-measurement program; and the successes, challenges, and lessons learned from introducing suburban transit services. The companion report also includes quantitative and qualitative decision matrixes.

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