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Trip Generation Study of Drive-through Coffee Outlets


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Brian Schapel

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Trip Generation Study of Drive-through Coffee Outlets

  1. 1. Trip Generation Study of Drive Through Coffee Outlets Brian Schapel, Bitzios Consulting
  2. 2. The need for this study There has been a dramatic increase in the number of drive-through coffee outlets in recent years WHY? Are we working too hard? Staying up late at night? We don’t want to get caught napping on the job!
  3. 3. Let me repeat that…... We don’t want to get caught napping on the job!
  4. 4. The need for this study  The RMS Guide to Traffic Generating Developments (Guide) does not yet include drive-through coffee outlets  Unique operational characteristics compared to other drive-through facilities: − Mostly limited to coffee, minimal food sales − No seating for most outlets and limited parking − Better and consistent planning outcomes – reliable trip generation and parking demand data
  5. 5. Study scope  Determine the sample number of outlets required to provide meaningful results  Identify suitable outlet survey sites  Obtain agreements from outlets to conduct surveys  Gather site operational data  Conduct on-site surveys to collect all road traffic trip generation data  Tabulate, analyse and graphically present the collected data to identify key statistical dependency relationships  Recommend traffic generation rates to adopt in the Guide
  6. 6. Site selection  Wide variations in the location, type and operation of outlets  Outlets were sought in metropolitan, sub-metropolitan and regional areas of New South Wales, Queensland and Victoria  22 outlets were identified as potentially suitable sites  10 outlets provided agreement for surveys  Challenges in getting agreements − Relatively small businesses compared to large drive-through fast food outlets − Many very unwilling to cooperate, concerned with business viability, previous complaints and/or commercial confidentiality − Lengthy process, in some cases up to two months
  7. 7. Survey procedure and schedule  Sites were surveyed between 12th May 2015 and 23rd June 2015  2 outlets were surveyed for 6 days − One of the six-day surveys conducted over 12 hours (6:00AM to 6:00PM) − The other six-day survey conducted over 4 hours (6:00AM to 10:00AM)  8 outlets were surveyed for 1 day on a Tuesday or Wednesday  Morning survey 6:30AM – 9:00AM (2 ½ hours)  Afternoon survey times varied due to differing PM business opening times (2 hours)  Almost all outlets are closed on Sundays
  8. 8. Data Collection – Site Information Outlet’s physical structure and operation  Building area  Opening times  Number of employees on a typical shift  Product range  Years of operation  Surrounding land use  Relevant local issues
  9. 9. Data Collection – On-site Surveys  Number of site entry and exit points  Frontage roads’ AM and PM peaks  Drive-through lane capacity (length available for queuing)  On-site parking availability (including for bicycles)  Number of waiting bays  Seating provision - internal and external  Number and type of ordering booths or terminals and collection points  Record of the time that a vehicle enters the site  Record of the time that the same vehicle exits the site
  10. 10. Data Collection – On-site Surveys (Continued)  Number of entering and exiting vehicles (cars/HVs) (15 minute blocks)  Number of vehicle occupants (15 minute blocks)  Number of pedestrians and cyclists (15 min blocks)  Number of queued vehicles (every 5 minutes)  Number of on-site parked vehicles relevant to the site (every 15 minutes)  Significant amount of data collection presented challenges for site surveyors as site layout restricted visibility in many cases
  11. 11. Data Collection – Passing trade Selected customers were asked three brief questions:  Was the trip just for coffee or had they had dropped in on the way somewhere else  What they were ordering  Their postcode These questions were aimed at:  Determining trip origin to assist with determining direction of travel in AM  Percentage of passing trade  Establishing a relationship between order size and service time
  12. 12. Preliminary Analysis  Initial data analysis indicated AM period significantly more trips than PM and unnecessary to undertake further detailed analysis for the PM period  Comparison of daily totals for six-day surveys showed no clear indicator of which weekday is the busiest  Saturday is less busy than the week days.  Only three outlets had any internal or external seating, therefore parking analysis unreliable. Limited available parking and maximum was 8 parked vehicles.  Survey data and key derived statistics were cross-checked for expected consistencies and variations against: − RMS Guide to Traffic Generation Developments; − Land Use Traffic Generation – Data and Analysis 22: Drive-Through Restaurants (1993) − Land Use Traffic Generation – Data and Analysis 5: Fast Food (1980), and − ITE Trip Generation Rates – 8th Edition
  13. 13. Preliminary Analysis (Continued)  Trip rates contained in the RMS Guide for KFC and McDonalds and Institute of Traffic Engineers (ITE): Survey RMS ITE AM Site Peak AM Site Peak AM Network Peak AM Network Peak DCO’s KFC McD KFC McD Coffee W/- Drive-through 105 150 260 100 180 102
  14. 14. Data Analysis - Methodology  Relationships between variable independent and dependent data tested to determine statistically relevant linkages between various parameters and the drive- through trip generation  Initial analysis of survey data showed no significant association between variables  Simple linear regression analysis was conducted to derive R2  R2 represents the percentage of variation in the dependent variable  Values less than 0.80 (80%) not considered accurate enough to indicate a significant relationship between the dependent and independent variable
  15. 15. Data Analysis - Results Key relationships tested for R2 to establish key influences on trip generation and queue lengths (dependent variables) as a priority R2 results of the linear regression testsIndependent Variable Dependent Variable Reference R2 Frontage Road Network AM Peak Hour Trip Generation Sec. 5.2.1 0.14 Frontage Road Site AM Peak Hour in CBD Direction Trip Generation Table 2 0.12 Frontage Road Site AM Peak Hour Queue Length Table 3 0.26 Frontage Road Two-Way Network AM Peak Hour Trip Generation Sec. 5.2.1 0.12 Gross Floor Area (GFA) Trip Generation Table 4 0.01 Site AM Peak Trip Generation Queue Length Table 5 0.67 Number of Staff Service Time Table 6 0.64 Number of Staff Trip Generation Table 7 0.31 Service Time Queue Length Sec. 5.2 0.07 Service Time Trip Generation Sec. 5.2 0.07 Number of Service Booths Service Time Sec. 5.2 0.06 Number of Service Booths Trip Generation Table 8 0.61 CBD In/ Outbound Site AM Peak Frontage Road Traffic Percentage Passing Trade Sec. 5.3 N/A CBD In/ Outbound Site AM Peak Frontage Road Traffic Trip Generation Sec. 5.3 N/A
  16. 16. Intermission
  17. 17. Data Analysis – Discussion of Results  Very low R2 results for influence of: − Service time on queue length − Service time on trip generation − Number of service booths on service times − GFA on trip generation
  18. 18. Data Analysis – Discussion of Results (Continued) Frontage Road Site AM Peak Hour in CBD Direction Vs Trip Generation No clear correlation or relationship can be formed. Similar results and conclusions drawn for trip generation and CBD bound or two-way frontage road traffic 1 2 3 4 5 67 8 9 10 y = 0.0166x + 85.717 R² = 0.1186 0 50 100 150 200 250 0 500 1000 1500 2000 2500 3000 3500 4000 DCOGeneratedTrips CBD-Bound Traffic Volumes - Site Peak AM Trip Generation vs CBD-Bound Traffic (Site Peak)
  19. 19. Data Analysis – Discussion of Results (Continued) Outlet Gross Floor Area (GFA) relationship to Trip Generation No correlation between generated trips and GFA of the DCO’s.
  20. 20. Data Analysis – Discussion of Results (Continued) Ziper drive-through outlet has a GFA of 7m2
  21. 21. Data Analysis – Discussion of Results (Continued) Frontage Road Site AM Peak Hour in CBD Direction and Queue Lengths View with caution as there are other influencing factors such as accessibility of traffic from both directions of the road, service times and the number of vehicles served.
  22. 22. Data Analysis – Discussion of Results (Continued) Trip Generation Relationship to DCO Queue Lengths Shows a relationship between queue lengths and trip generation, however other contributing factors that influence trip generation as a dependent variable 12 3 4 5 6 7 8 9 10 y = 0.0402x + 2.4677 R² = 0.6679 0 2 4 6 8 10 12 14 0 50 100 150 200 250 QueueLength(Veh) Site AM Peak Trip Generation Queue Length Relationship to Trips
  23. 23. Data Analysis – Discussion of Results (Continued) Staff Number Impact on Service Times Suggests that a higher number of staff results in an increased service time. Intuitively not logical. More staff to handle the peak, but service times increase as business increases. Nature of the relationship rather than dependence. 1, 2, 3 4 5, 6 7 8 9 10 y = 0.8746x + 1.2898 R² = 0.6436 0 1 2 3 4 5 6 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 AverageServiceTime(min) Number of Staff Number of Staff to Service Time
  24. 24. Data Analysis – Discussion of Results (Continued) Staff Number Impact on Trip Generation Results probably indicate correlation rather than dependency. 1 2 3 4 5 6 7 8 9 10 y = 39.943x - 12.431 R² = 0.3139 0 50 100 150 200 250 0 1 2 3 4 5 TripGenerationAM(SitePeak) Number of Staff Number of Staff to Trip Generation
  25. 25. Data Analysis – Discussion of Results (Continued) Service Booth Numbers Impact on Trip Generation Higher number of service points are operated by outlets to cater for the business’s generated trips. Therefore, the relationship is probably more correlation than dependency 1 2 3 4 5 67 8 9 10 y = 37.517x - 14.655 R² = 0.6149 0 50 100 150 200 250 0 1 2 3 4 5 6 7 TripGenerationAM(SitePeak) Number of Service Booths (Ordering + Payment + Collection) Number of Service Booths (Total) to Trip Generation
  26. 26. Data Analysis – Discussion of Results (Continued) DCO Location Relationship with CBD Inbound Vs Outbound Traffic  Determine possible relationships between the accessibility of each DCO location to capture customers from CBD inbound and CBD outbound traffic  Reasonable expectation that the location of DCO’s that were best suited to capture the AM CBD inbound traffic would attract higher trip generation rates  Analysis however, showed no distinct differences in the average DCO’s trip generation or passing trips based on location
  27. 27. Conclusions  Significantly more trips generated in the AM peak than PM peak  Based on six-day surveys, very low number of customers on Saturday and most outlets closed on Sunday  Based customer interviews there is a high proportion of passing trips throughout the day (average 83%) also verified by postcode data  Inter-relationships identified in Table 1, whilst indicative of some dependence, can be explained by reasoning of normal operations of a business such as DCOs  Some correlation between road frontage traffic volumes and trip generation, however the R2 relationship is not statistically significant  Does not appear to be a correlation of GFA to trip generation  Appears to be some correlation between trip generation and queue lengths
  28. 28. Conclusions ( Continued)  Outlet management confirm that the number of staff serving is increased during site peak times to reduce service times, also designed to manage queue lengths  Service times across all outlets generally consistent, with a range of 2:41(min:sec) to 5:29 and average of 3:53. A “levelling out” of customers an outlet can serve based on the coffee making equipment they have?  Maximum queue lengths: − Ranged from 2 to 11 − One maximum queue of 2, two maximum queue of 11 − Remaining seven maximum queue was between 5 and 7 − Overall average maximum for all outlets of 6.7 vehicles − Queuing capacity of all sites sufficient to avoid queued vehicles onto roadway − Customers’ limited tolerance to waiting times?
  29. 29. Conclusions – Other influencing factors  Visible exposure to passing traffic  Ease of access to the site  Ease of site egress  Quality and visibility of signage and advertising  Reputation, quality of coffee, food and service  Type of coffee machines used and capacity to produce a maximum rate of coffees
  30. 30. Recommendations With the exception of a small number of outlets surveyed, due to local circumstances and excluded as “outliers”, a range of trip generation rates could be reasonably adopted between 70 and 130 AM peak hour trips
  31. 31. Recommendations (Continued)  Range of values between 70 and 130 trips in the AM peak hour be adopted as a baseline estimate  The average trip generation for the AM site peak calculated for all DCOs of 105 falls within this range  When assessing proposed DCO developments, selection of an appropriate traffic generation rate should consider the range of variable influencing factors  Recommended that the average passing trip percentage of 83%
  32. 32. What rates to use for Traffic Impact Assessments?  Baseline range 70 to 130 trips  Whilst R2 not significant there are still evident relationships: − Frontage road traffic − Visible exposure to passing traffic − Ease of access to the site − Potential customer catchment  Other factors may be unknown at Development Application stage, such as: − Quality and visibility of signage and advertising − Reputation and quality of coffee, food and service − Number of service booths, staff and coffee making capacity − Seating
  33. 33. What rates to use for Traffic Impact Assessments? (Cont)  Be careful about road frontage traffic and trip generation assumption  This outlet captures a large industrial access restricted area  AM Peak traffic 68 vehicles generating 88 trips (44 vehicles)
  34. 34. What rates to use for Traffic Impact Assessments? (Cont) Summary of key traffic impact considerations  Baseline trip generation rate of 70 – 130 peak AM trips  Exposure to frontage road traffic  Consider capture of CBD bound traffic in AM  Passing trade – 83 %  Likely maximum queue lengths – Average maximum approximately 7, maximum 11  Visible exposure to passing traffic  Ease of access to the site  Ease of site egress  For proposed sites with seating use parking rates for cafe  Any other known influences such as proposed number of service booths
  35. 35. Acknowledgements  Bitzios Consulting would like to acknowledge − Vince Taranto, RMS Leader Road Network Analysis for management, support and assistance throughout this study; − Traffic Data and Control for the extensive traffic and outlet survey work; and − Drive-through coffee outlets for their cooperation and assistance: Fastlane Coffee 1, Dubbo NSW Coffee Club, Tingalpa, QLD Fastlane Coffee 2, Dubbo NSW Di Bella, Bowen Hills, QLD Starbucks, Mt Druitt, NSW Espresso Lane, Labrador, QLD Ziper, Concord, NSW The Brew, Bathurst, NSW Johnny Bean Good, Bathurst, NSW Tico’s Drive Thru, Brooklyn, VIC