This study analyzed trip generation data from surveys of 10 drive-through coffee outlets in Australia. Key findings included that outlets generated significantly more trips during the morning peak than afternoon, with an average of 105 trips during the morning site peak. Relationships between variables like staffing levels and service times or gross floor area and trip generation were weak. The study recommends using a baseline trip generation rate range of 70-130 trips during the morning peak for traffic assessments of drive-through coffee outlets.
1) The document discusses models for estimating car trip generation in Nairobi and Dar-es-Salaam. It estimates four types of models: with and without car ownership as an explanatory variable, two-stage models, and joint car ownership and trip generation models.
2) The results show household income, number of workers and drivers, and car ownership positively influence trip generation. However, some differences exist between the cities.
3) Models 3 and 4, which account for potential endogeneity between car ownership and trips, better explain trip generation in both cities compared to Models 1 and 2.
Three points (0,4), (2,3.25), and (5,3.0625) define a parabolic cross section of an antenna dish. A quadratic function y = 0.0625x^2 - 0.5x + 4 models this parabolic cross section based on the three given points.
A linear regression was performed to find the best fit line for race distances and times of a dog named Midnight. The linear regression allows one to predict future race times based on distances. The document outlines the steps to perform a linear regression using a graphing calculator: 1) Enter the x and y variable data into lists L1 and L2, 2) generate a scatter plot of the data, 3) use the calculator's LinReg function to calculate the best fit line equation, and 4) graph the best fit line to visualize the linear relationship between distances and times.
Determining trip generation of commercial land use of kaptaiKazi Mahfuzur Rahman
Abstract
Trip generation is the first step in the conventional transportation forecasting process. Trip generation rates can
influence the magnitude of the roadway improvements that are constructed like the amount of land that is
required to be dedicated for road’s right-of-way, and calculation of long term maintenance costs of the roadway
network. Therefore, an accurate estimate of vehicle trip generation is required to construct the necessary roadway
infrastructure without overbuilding it. Mohora to Kaptai road is an important and a busy road in Chittagong
because some important commercial buildings, power plants, industries and institutions are situated along this
road. The goal of this paper is to determine trip generation of adjacent commercial land uses of Kaptai Road. To
fulfill the goal, our objectives are to identify the number of trips generation by the adjacent commercial land uses
and to relate trip generation with respect to land use and socio-economic characteristics of Kaptai road. This
study utilizes Geographic Information System (GIS), Questionnaire Survey, Personal Interview and Multiple
Linear Regression Analysis for the trip generation analysis and calculation. Trip generation surveys have
completed at a total of 10 commercial sites, covering five different shopping centers and five different banks at
different important intersection point. The findings have clarified the existing land uses, trip generation situation
with multiple linear regression model and trip rates of commercial land uses.
The document discusses various transport modelling concepts and methods including:
1) The four step transport modelling process of trip generation, trip distribution, modal split, and trip assignment.
2) Trip generation models including linear regression models to predict trip production and attraction based on socio-economic variables.
3) Trip distribution models such as gravity models which distribute trips between zones based on production, attraction, and impedance.
4) Modal split models which predict the share of trips by different modes using logit models calibrated based on stated preference surveys.
TRIP GENERATION Survey and ORIGIN / DESTINATION STUDYGrant Johnson, PE
Origin Destination Surveys OD调查…and Trip Generation Surveys…交通出行率调查
Sample Size (100 vehicles or more) of randomly selected vehicles that turned left to go south at Interchange/Intersection 8.
In this Survey:
55% of samples went south past机场高速
45% turned right onto黄山大道
Total Left Turn Vol = 1630 vph
Result: 900 vehicles (55%) go south
730 vehicles go west on黄山大道
This document summarizes key concepts in travel demand and traffic forecasting. It discusses the need to understand travel patterns to plan transportation infrastructure investments. It also outlines the four key traveler decisions that must be modeled: temporal, destination, modal, and route. Specific models for trip generation are presented, including linear regression and Poisson regression models using household characteristics to predict trip production. An example problem demonstrates estimating the expected number of trips and probability of no trips using a Poisson regression model.
1) The document discusses models for estimating car trip generation in Nairobi and Dar-es-Salaam. It estimates four types of models: with and without car ownership as an explanatory variable, two-stage models, and joint car ownership and trip generation models.
2) The results show household income, number of workers and drivers, and car ownership positively influence trip generation. However, some differences exist between the cities.
3) Models 3 and 4, which account for potential endogeneity between car ownership and trips, better explain trip generation in both cities compared to Models 1 and 2.
Three points (0,4), (2,3.25), and (5,3.0625) define a parabolic cross section of an antenna dish. A quadratic function y = 0.0625x^2 - 0.5x + 4 models this parabolic cross section based on the three given points.
A linear regression was performed to find the best fit line for race distances and times of a dog named Midnight. The linear regression allows one to predict future race times based on distances. The document outlines the steps to perform a linear regression using a graphing calculator: 1) Enter the x and y variable data into lists L1 and L2, 2) generate a scatter plot of the data, 3) use the calculator's LinReg function to calculate the best fit line equation, and 4) graph the best fit line to visualize the linear relationship between distances and times.
Determining trip generation of commercial land use of kaptaiKazi Mahfuzur Rahman
Abstract
Trip generation is the first step in the conventional transportation forecasting process. Trip generation rates can
influence the magnitude of the roadway improvements that are constructed like the amount of land that is
required to be dedicated for road’s right-of-way, and calculation of long term maintenance costs of the roadway
network. Therefore, an accurate estimate of vehicle trip generation is required to construct the necessary roadway
infrastructure without overbuilding it. Mohora to Kaptai road is an important and a busy road in Chittagong
because some important commercial buildings, power plants, industries and institutions are situated along this
road. The goal of this paper is to determine trip generation of adjacent commercial land uses of Kaptai Road. To
fulfill the goal, our objectives are to identify the number of trips generation by the adjacent commercial land uses
and to relate trip generation with respect to land use and socio-economic characteristics of Kaptai road. This
study utilizes Geographic Information System (GIS), Questionnaire Survey, Personal Interview and Multiple
Linear Regression Analysis for the trip generation analysis and calculation. Trip generation surveys have
completed at a total of 10 commercial sites, covering five different shopping centers and five different banks at
different important intersection point. The findings have clarified the existing land uses, trip generation situation
with multiple linear regression model and trip rates of commercial land uses.
The document discusses various transport modelling concepts and methods including:
1) The four step transport modelling process of trip generation, trip distribution, modal split, and trip assignment.
2) Trip generation models including linear regression models to predict trip production and attraction based on socio-economic variables.
3) Trip distribution models such as gravity models which distribute trips between zones based on production, attraction, and impedance.
4) Modal split models which predict the share of trips by different modes using logit models calibrated based on stated preference surveys.
TRIP GENERATION Survey and ORIGIN / DESTINATION STUDYGrant Johnson, PE
Origin Destination Surveys OD调查…and Trip Generation Surveys…交通出行率调查
Sample Size (100 vehicles or more) of randomly selected vehicles that turned left to go south at Interchange/Intersection 8.
In this Survey:
55% of samples went south past机场高速
45% turned right onto黄山大道
Total Left Turn Vol = 1630 vph
Result: 900 vehicles (55%) go south
730 vehicles go west on黄山大道
This document summarizes key concepts in travel demand and traffic forecasting. It discusses the need to understand travel patterns to plan transportation infrastructure investments. It also outlines the four key traveler decisions that must be modeled: temporal, destination, modal, and route. Specific models for trip generation are presented, including linear regression and Poisson regression models using household characteristics to predict trip production. An example problem demonstrates estimating the expected number of trips and probability of no trips using a Poisson regression model.
PSU Friday Transportation Seminar 10/4/2013, featuring Michael Mauch of DKS Associates: Real-world traffic trends observed in PORTAL and INRIX traffic data are used to expand the performance measures that can be obtained from Portland Metro's travel demand model to include the number of hours of congestion that can be expected during a typical weekday and travel time reliability measures for congested freeway corridors.
Case Studies in Managing Traffic in a Developing Country with Privacy-Preserv...Biplav Srivastava
Simulation is known to be an effective technique to understand
and manage traffic in cities of developed countries. However, in developing countries, traffic management is lacking due to a wide diversity of vehicles on the road, their chaotic movement, little instrumentation to sense traffic state and limited funds to create IT and physical infrastructure to ameliorate the situation. Under these conditions, in this paper, we present our approach of using the Megaffic traffic simulator as a service to gain actionable insights for two use-cases and cities in India, a first. Our approach is general to be readily used in other use cases and cities; and our results give new insights: (a) using demographics data, traffic demand can be reduced if timings of government offices are altered in Delhi, (b) using a mobile company’s Call
Data Record (CDR) data to mine trajectories anonymously,
one can take effective traffic actions while organizing events
in Mumbai at local scale.
This document discusses how analytic reporting can identify issues with paratransit service and help solve them. It provides two case studies:
1) A system had low on-time performance (OTP) in afternoons. Shifting run start times and adjusting lengths increased afternoon capacity and improved OTP to 94.72%. Productivity increased 3.6-5.4%.
2) Analysis found runs didn't match demand. Optimizing runs reduced vehicle hours by 1.1% and driver hours by 1.3%, while maintaining service. Shifting low-demand trips to non-dedicated providers produced the most savings.
AITPM Conference Presentation Anthony JohnstoneJumpingJaq
The City of Perth conducted a cycle time study to investigate reducing cycle times at signals in the CBD to improve walkability without negatively impacting other road users. Stage 1 modeling found reducing cycle times to 120 seconds increased average private vehicle speeds by 12% and bus speeds by 13% while reducing pedestrian delay by 91 hours in AM and 86 hours in PM. Stage 2 implementation at trial sites saw cycle times reduced by up to 46% with similar travel time and pedestrian delay reductions as the model. The study demonstrated cycle time reductions can benefit pedestrians with minimal traffic impacts.
How Analytic Reporting Can Identify and Solve Paratransit Service ShortcomingsTSSParatransit
This document discusses how analytic reporting can identify opportunities to improve paratransit service quality and efficiency. It provides two case studies:
1) A system was struggling with low on-time performance in afternoons. Shifting run start times and adjusting lengths increased afternoon capacity and improved on-time rates from 84.9% to 94.7%. Productivity increased 3.6-5.4%.
2) A system's run structure did not match demand. Optimizing reduced hours/vehicles 1.1-1.3% while maintaining service. Diverting trips to non-dedicated providers during low-demand times produced the most savings.
3) Analytic tools can evaluate strategies like
3PLs are a virtually perfect competitive business model. With highly variable costs to revenue, it is challenging to make a 3PL company thrive. Here is some research we have done with Lean Transit to achieve remarkable progress towards making 3PLs more profitable.
The Linear Road paper proposes a benchmark for stream data management systems that addresses challenges from continuous and unbounded streaming data. It defines four types of input tuples representing vehicle position and query data. Queries include continuous queries that compute trip times and historical queries that analyze past expenditure and travel times. The benchmark implementation uses a traffic simulator to generate streaming input data and measures systems' ability to answer queries within a given response time and load. Experimental results showed stream data management systems outperforming relational databases by at least a factor of five for processing streaming data.
This document discusses secure benchmarking and its applications. Secure benchmarking allows companies to anonymously share and compare sensitive performance data through encryption techniques. This allows benchmarking without revealing raw confidential data. The document provides examples of secure benchmarking for transportation procurement bids and shipment costs. Regression analysis is performed on encrypted data to identify cost structures and compare performance between companies and markets over time. Insights from benchmarking can guide strategy and value creation for shippers and carriers.
Supply Chain Framework, Logistics Simulation Model for Food Delivery business.
This SCM Logistics Model has the simulator, modeller, scenario builder, database and customized reports.
The document discusses how RouteOp GPS and driver behavior monitoring solutions can increase profits, enhance customer experience, and improve resource allocation for companies. It provides examples of how RouteOp optimization software has significantly reduced drive times and mileage for customers. The document also outlines how GPS vehicle tracking, dispatching, and monitoring driver behavior can help improve safety, productivity and reduce costs. Real data examples are given showing reductions in mileage, drive times, and unsafe driving incidents.
The document summarizes the first educational meeting of the North Carolina SimCap volunteer network. It includes an overview of the North Carolina SimCap organization, its objectives of developing professionals, strengthening relationships, and advancing the traffic simulation profession. It also summarizes two presentations on the history and activities of North Carolina SimCap and the application of transportation modeling and simulation in the planning process. Finally, it discusses the results of a survey on the network's objectives, preferred meeting topics and activities, and members' experience and interest in different traffic simulation tools.
Project business Case :
PDI is package pickup and delivery company which OUTSOURCES its ground operations to a third party
Inefficiency in processes and miscommunication at PDI -> rebates and idle time costs
Gross margin needs to be improved to sustain operations and for growth in future
Problem Statement :
Identify causes behind current inefficiencies (rebate and idle cost) impacting gross margin.
The presentation discusses a project to redesign an automobile logistics system to improve efficiency and customer service. The goals are to reduce logistical costs by 30% and improve customer feedback. The method involves using logistics efficiency and allocative efficiency models to evaluate money, time, and service levels across regions and units, based on expert interviews and field study data. The project management process includes forming multi-member teams to acquire and analyze data to establish and test models, with the ultimate goals of implementing improvements and evaluating the overall project results.
There has been an explosion of data digitising our physical world – from cameras, environmental sensors and embedded devices, right down to the phones in our pockets. Which means that, now, companies have new ways to transform their businesses – both operationally, and through their products and services – by leveraging this data and applying fresh analytical techniques to make sense of it. But are they ready? The answer is “no” in most cases.
In this session, we’ll be discussing the challenges facing companies trying to embrace the Analytics of Things, and how Teradata has helped customers work through and turn those challenges to their advantage.
Christchurch (NZ) Transportation Models Update - A Moving FeastJumpingJaq
The document discusses updating the Christchurch Transport Model to reflect changes following earthquakes in 2010-2011. It describes significant shifts in land use and transportation networks due to earthquake damage and the rebuilding process. The model was validated against 2013 and 2015 data on traffic counts, travel times, and commuting patterns, and was found to generally perform well despite the challenges of modeling a city undergoing reconstruction. Ongoing monitoring of land use changes will be needed as the rebuilding continues.
ATS-16: Making Data Count, Krista NordbackBTAOregon
The document introduces the Bike-Ped Portal, a national online archive of non-motorized traffic count data. It provides an overview of the portal, including its purpose to aggregate and share bicycle and pedestrian count data. Users can upload their count data to the portal and download data. The portal currently includes over 5 million records from 5 states. The presentation demonstrates how to search for and visualize count data on the portal site.
Disruptions on Road Networks: Impact on traffic characteristicsJumpingJaq
This document summarizes a study that investigated the impact of short-term disruptions on traffic characteristics. The study analyzed travel time and traffic volume data from five pairs of parallel routes during peak periods, comparing scenarios with and without incidents. Key findings were that average travel times remained similar, but travel time volatility increased under incident conditions. Traffic volumes shifted slightly between routes when incidents occurred, indicating adaptive route choice behavior. The study highlights the need for transport models to account for disrupted traffic conditions and adaptive user equilibrium.
The document describes a project by Navstar Mapping Corporation to map 70,000 miles of local roads in Tennessee over 5 years. Key aspects of the project include using GPS and distance measuring instruments to update an existing database of 55,000 road miles across 95 counties, performing field surveys to validate data, and processing updates to deliver accurate mapping data to the Tennessee Department of Transportation. The project aims to efficiently collect and verify road data to improve the state's transportation mapping systems.
The document describes a proposed smart traffic monitoring system that uses image processing and a Raspberry Pi microcontroller to automatically adjust traffic light timing based on detected traffic density. Video is captured of intersections and processed to detect vehicles and determine traffic density on each road. The number of vehicles is then used to calculate the optimal traffic light timing, with longer green lights allocated to heavier traffic. This provides an adaptive system that is more efficient than fixed-time traffic lights that cannot adjust to changing traffic conditions.
Richard Tang - Mitcham Princes Road CrossingJumpingJaq
This document summarizes a case study regarding a request for a pedestrian crossing on Princes Road in Torrens Park. Initially, Council estimated the cost to install a zebra crossing would be $10,000-$12,000. However, during detailed design it was discovered the actual cost to meet lighting standards would be over $50,000. Staff then proposed an alternative $12,000 solution focusing on improved road lighting, signage removal, pavement marking renewal and red pavement treatment instead of the more expensive zebra crossing. Council approved this balanced solution that achieved safety improvements within the original budget.
This document summarizes a proposal for improvements to the Seaford Roundabout intersection. It includes information on vehicle and pedestrian traffic patterns, delays at the intersection during morning and afternoon peak times, and key concerns about vulnerable pedestrians, the number of crashes, and high vehicle entry speeds. The proposal engaged traffic consultants to investigate crashes, model existing traffic conditions and potential treatments, and address speeds and pedestrian safety as part of applying for "Black Spot" road safety funding to improve the intersection.
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Similar to Trip Generation Study of Drive-through Coffee Outlets
PSU Friday Transportation Seminar 10/4/2013, featuring Michael Mauch of DKS Associates: Real-world traffic trends observed in PORTAL and INRIX traffic data are used to expand the performance measures that can be obtained from Portland Metro's travel demand model to include the number of hours of congestion that can be expected during a typical weekday and travel time reliability measures for congested freeway corridors.
Case Studies in Managing Traffic in a Developing Country with Privacy-Preserv...Biplav Srivastava
Simulation is known to be an effective technique to understand
and manage traffic in cities of developed countries. However, in developing countries, traffic management is lacking due to a wide diversity of vehicles on the road, their chaotic movement, little instrumentation to sense traffic state and limited funds to create IT and physical infrastructure to ameliorate the situation. Under these conditions, in this paper, we present our approach of using the Megaffic traffic simulator as a service to gain actionable insights for two use-cases and cities in India, a first. Our approach is general to be readily used in other use cases and cities; and our results give new insights: (a) using demographics data, traffic demand can be reduced if timings of government offices are altered in Delhi, (b) using a mobile company’s Call
Data Record (CDR) data to mine trajectories anonymously,
one can take effective traffic actions while organizing events
in Mumbai at local scale.
This document discusses how analytic reporting can identify issues with paratransit service and help solve them. It provides two case studies:
1) A system had low on-time performance (OTP) in afternoons. Shifting run start times and adjusting lengths increased afternoon capacity and improved OTP to 94.72%. Productivity increased 3.6-5.4%.
2) Analysis found runs didn't match demand. Optimizing runs reduced vehicle hours by 1.1% and driver hours by 1.3%, while maintaining service. Shifting low-demand trips to non-dedicated providers produced the most savings.
AITPM Conference Presentation Anthony JohnstoneJumpingJaq
The City of Perth conducted a cycle time study to investigate reducing cycle times at signals in the CBD to improve walkability without negatively impacting other road users. Stage 1 modeling found reducing cycle times to 120 seconds increased average private vehicle speeds by 12% and bus speeds by 13% while reducing pedestrian delay by 91 hours in AM and 86 hours in PM. Stage 2 implementation at trial sites saw cycle times reduced by up to 46% with similar travel time and pedestrian delay reductions as the model. The study demonstrated cycle time reductions can benefit pedestrians with minimal traffic impacts.
How Analytic Reporting Can Identify and Solve Paratransit Service ShortcomingsTSSParatransit
This document discusses how analytic reporting can identify opportunities to improve paratransit service quality and efficiency. It provides two case studies:
1) A system was struggling with low on-time performance in afternoons. Shifting run start times and adjusting lengths increased afternoon capacity and improved on-time rates from 84.9% to 94.7%. Productivity increased 3.6-5.4%.
2) A system's run structure did not match demand. Optimizing reduced hours/vehicles 1.1-1.3% while maintaining service. Diverting trips to non-dedicated providers during low-demand times produced the most savings.
3) Analytic tools can evaluate strategies like
3PLs are a virtually perfect competitive business model. With highly variable costs to revenue, it is challenging to make a 3PL company thrive. Here is some research we have done with Lean Transit to achieve remarkable progress towards making 3PLs more profitable.
The Linear Road paper proposes a benchmark for stream data management systems that addresses challenges from continuous and unbounded streaming data. It defines four types of input tuples representing vehicle position and query data. Queries include continuous queries that compute trip times and historical queries that analyze past expenditure and travel times. The benchmark implementation uses a traffic simulator to generate streaming input data and measures systems' ability to answer queries within a given response time and load. Experimental results showed stream data management systems outperforming relational databases by at least a factor of five for processing streaming data.
This document discusses secure benchmarking and its applications. Secure benchmarking allows companies to anonymously share and compare sensitive performance data through encryption techniques. This allows benchmarking without revealing raw confidential data. The document provides examples of secure benchmarking for transportation procurement bids and shipment costs. Regression analysis is performed on encrypted data to identify cost structures and compare performance between companies and markets over time. Insights from benchmarking can guide strategy and value creation for shippers and carriers.
Supply Chain Framework, Logistics Simulation Model for Food Delivery business.
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The document discusses how RouteOp GPS and driver behavior monitoring solutions can increase profits, enhance customer experience, and improve resource allocation for companies. It provides examples of how RouteOp optimization software has significantly reduced drive times and mileage for customers. The document also outlines how GPS vehicle tracking, dispatching, and monitoring driver behavior can help improve safety, productivity and reduce costs. Real data examples are given showing reductions in mileage, drive times, and unsafe driving incidents.
The document summarizes the first educational meeting of the North Carolina SimCap volunteer network. It includes an overview of the North Carolina SimCap organization, its objectives of developing professionals, strengthening relationships, and advancing the traffic simulation profession. It also summarizes two presentations on the history and activities of North Carolina SimCap and the application of transportation modeling and simulation in the planning process. Finally, it discusses the results of a survey on the network's objectives, preferred meeting topics and activities, and members' experience and interest in different traffic simulation tools.
Project business Case :
PDI is package pickup and delivery company which OUTSOURCES its ground operations to a third party
Inefficiency in processes and miscommunication at PDI -> rebates and idle time costs
Gross margin needs to be improved to sustain operations and for growth in future
Problem Statement :
Identify causes behind current inefficiencies (rebate and idle cost) impacting gross margin.
The presentation discusses a project to redesign an automobile logistics system to improve efficiency and customer service. The goals are to reduce logistical costs by 30% and improve customer feedback. The method involves using logistics efficiency and allocative efficiency models to evaluate money, time, and service levels across regions and units, based on expert interviews and field study data. The project management process includes forming multi-member teams to acquire and analyze data to establish and test models, with the ultimate goals of implementing improvements and evaluating the overall project results.
There has been an explosion of data digitising our physical world – from cameras, environmental sensors and embedded devices, right down to the phones in our pockets. Which means that, now, companies have new ways to transform their businesses – both operationally, and through their products and services – by leveraging this data and applying fresh analytical techniques to make sense of it. But are they ready? The answer is “no” in most cases.
In this session, we’ll be discussing the challenges facing companies trying to embrace the Analytics of Things, and how Teradata has helped customers work through and turn those challenges to their advantage.
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The document discusses updating the Christchurch Transport Model to reflect changes following earthquakes in 2010-2011. It describes significant shifts in land use and transportation networks due to earthquake damage and the rebuilding process. The model was validated against 2013 and 2015 data on traffic counts, travel times, and commuting patterns, and was found to generally perform well despite the challenges of modeling a city undergoing reconstruction. Ongoing monitoring of land use changes will be needed as the rebuilding continues.
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The document describes a proposed smart traffic monitoring system that uses image processing and a Raspberry Pi microcontroller to automatically adjust traffic light timing based on detected traffic density. Video is captured of intersections and processed to detect vehicles and determine traffic density on each road. The number of vehicles is then used to calculate the optimal traffic light timing, with longer green lights allocated to heavier traffic. This provides an adaptive system that is more efficient than fixed-time traffic lights that cannot adjust to changing traffic conditions.
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This document summarizes a proposal for improvements to the Seaford Roundabout intersection. It includes information on vehicle and pedestrian traffic patterns, delays at the intersection during morning and afternoon peak times, and key concerns about vulnerable pedestrians, the number of crashes, and high vehicle entry speeds. The proposal engaged traffic consultants to investigate crashes, model existing traffic conditions and potential treatments, and address speeds and pedestrian safety as part of applying for "Black Spot" road safety funding to improve the intersection.
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Trip Generation Study of Drive-through Coffee Outlets
1. Trip Generation Study of Drive Through Coffee Outlets
Brian Schapel, Bitzios Consulting
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.
4.
5.
6.
7. Let me repeat that…...
We don’t want to get
caught napping on the job!
8. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
21. 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
22. 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)
23. 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.
24. Data Analysis – Discussion of Results (Continued)
Ziper drive-through outlet has a GFA of 7m2
25. 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.
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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?
33. 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
34. 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
35. 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%
36. 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
37. 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)
38. 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
39. 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