Transportation Planning (CE 448) Project 2
Spring 2018 Dr. Mehrnaz Ghamami
Traffic Generated by Fast-Food Restaurants
INTRODUCTION The basic assignment
Each group is to develop statistical models that predict the traffic generated by fast-food restaurants: one (or more) model for “lunch-time” (11:00-2:00) and one (or more) for “dinner-time/PM peak period” (4:00-7:00). The structures of the different models will be compared to one another as will the predictions that are made by using them. The models that you develop and their predictions will also be compared to those from software promulgated by the Institute of Transportation Engineers. Each group must submit its findings and recommendations in a comprehensive written report in which the various models are evaluated and compared.
Critical dates for project
Monday, 20 February: The proposal for your data collection plan (and site) is due at the beginning of class—it is strongly suggested that you submit this earlier than the due date.
Monday, 27 March: A memo transmitting the collected data for your site is due at the beginning of class —earlier would be better.
Monday, 10 April: The final project report is due at the beginning of class.
Some background
The classic models (starting in the late 1950s) for trip generation were linear regression models based on aggregate zonal data. These types of models are still developed and used today. Moreover, such linear regression models form the basis for most software on the market, including the widely-used Institute of Transportation Engineers (ITE) trip generation software. The development and use of these models is not without some problems. They include: 1) independence of trip generation estimates from system characteristics; 2) exclusion of appropriate variables; 3) inclusion and use of counter-intuitive or illogical variables (or signs); and 4) lack of temporal consistency of models.
The most widely-used models for trip generation for specific types of land uses (and sites) are found in the ITE trip generation software. In these models, the trips generated by a site are generally a function of the size of the development and the specific land use (e.g., gross floor area [GFA] for restaurants) and/or other measures of the intensity of the use (e.g., number of seats for a restaurant, number of beds for a hospital, number of employees for offices). Such models are calibrated on data collected in a base time period (e.g., “today”) and used to predict things in the future or for a new development that is not yet built.
The ITE models are typically (although not always) univariate (simple) regression models such as:
Y = aX+b
where: Y = total number of daily trips generated
X = gross floor area (GFA)—this is only an example
a,b = coefficients determined through regression analysis
Transportation Planning (CE 448) Project 2
Spring 2018 Dr. Mehrnaz Ghamami
However, at least some of the models for fast-food restaurants (based on GFA, number of seats) have been widely criticized for giving inaccurate results. Thus, there is need for improvement of these models …and that’s what you are going to do!
SPECIFICS OF THE ASSIGNMENT
Site selection
Each group will be responsible for choosing one fast-food restaurant in the Lansing metropolitan area and collecting data on its operations and characteristics. Those data will be shared with all other groups. The restaurant that you choose must offer both drive-up and “sit-down” service and provide off-street parking. The target restaurants are the large chain-type places like McDonald’s, Burger King, and Taco Bell (and not locally unique restaurants such as Famous Taco). All groups must select a different restaurant (while, for example, more than one McDonald’s may be selected, no two groups can select the same McDonald’s). You may make your selections as soon as possible—it’s first-come, first-serve. You must prepare a written proposal that describes the restaurant you have chosen and how and when the data will be collected. However, a site may be “reserved” prior to submission of your proposal.
Data collection and presentation
A not insignificant part of this project is data collection. You must collect data on the size and other physical characteristics of the restaurant, the traffic entering and leaving the site, and the traffic on the adjacent roadway(s).
You will need to collect the following traffic data organized in 15-minute intervals:
traffic volume entering the facility (VOLenter);
traffic volume leaving the facility (VOLexit);
number of vehicles using the drive-up window(s) (drvupVOL);
maximum queue length at the drive-up window(s) (maxqueue); and
traffic volumes on the adjacent roadways: each direction, separately, for any major roadway (nearVOL, farVOL for nearest and farthest lane [or pair of lanes] from facility, respectively); and combined for minor roadways (minorVOL).
You will also need to collect the following data about the restaurant itself:
gross floor area (GFA)
number of seats (seats)
number of employees on site during traffic data collection periods (employee)
number of parking spaces (park)
number of actual drive-up windows where food is ordered (driveup)
number of registers/stations excluding the drive-up windows (register)
Each group will have to collect data on at least three separate (one-hour) occasions: one or two “lunch-time” periods and one or two “dinner-time” periods (two of one, one of the other). A total of three hours (12 15-minute periods) of data is required from each group.
Once the data are collected they must be put into a Microsoft Excel file and turned in. YOU MUST USE THE EXACT FORMAT SHOWN IN THE APPENDIX.
A memo describing the actual data collection procedure (including any problems that you had) and presenting your data must be and turned in (both a paper copy and emailed as an
Transportation Planning (CE 448) Project 2
Spring 2018 Dr. Mehrnaz Ghamami
attachment with only one file—the data) no later than the date noted above, at the beginning of class. Identify your file using your group number in some way. If you do not turn your memo/data in on time, your final project will be penalized. Identify your data file with your group number—i.e. group 5 data.
Model calibration
You will need to calibrate several simple linear regression models which relate the number of trips to and from fast-food restaurants to characteristics of the restaurant and/or traffic volume information. You will use the data that were collected by all groups to calibrate the models discussed below.
Trip estimates
Use the models you developed and also the ITE models to estimate the trips generated by fast-food restaurants.
Transportation Planning (CE 448) Project 2
Spring 2018 Dr. Mehrnaz Ghamami
MODELS
Traditional ITE-type models
The traditional ITE models relate total driveway volumes to characteristics of the restaurant. You will create ITE-type linear regression models using the dependent and independent variables listed below (a total of 24 simple univariate models).
Dependent variables
TLUNCH: total hourly driveway volume during lunch period
TPMPEAK: total hourly driveway volume during PM peak period
INLUNCH: inbound hourly driveway volume during lunch period
INPMPEAK: inbound hourly driveway volume during PM peak period
WLUNCH: total hourly service window volume during lunch period
WPMPEAK: total hourly service window volume during PM peak period
ITE-type independent variables
GFA: gross floor area
SEATS: number of seats
EMPLY: number of employees on site during traffic data collection period
PARK: number of parking spaces
For each of the dependent variables listed above, it is necessary that you select the model which is “best” (i.e., which regression model is best for predicting each dependent variable).
New regression models
Develop/examine models utilizing independent variables mentioned earlier. For the new regression models, the dependent variables will be exactly the same as those listed for the ITE-type models. However, the independent variables might also include variables such as:
directional (or total) traffic volumes on adjacent major roadways; and
2-way volumes on adjacent minor roadways.
and possibly be used in combination with one or more ITE-type independent variables. That is, the independent variables might be different and the models might be more complex:
Y = b + a1X1 +a2X2
where: Y = total driveway volume for lunch period
X1 = restaurant characteristics (e.g., gross floor area)
X2 = traffic volume measure for adjacent street/road
a1,a2,b = coefficients determined through regression analysis
There will be six new regression models, one for each dependent variable. These will be the “best” that can be produced using the data available.
NOTE: The models will be calibrated using data based on 15-minute intervals while the desired estimates are for one-hour periods. Thus, the predictions that the models provide will have to be modified to get hourly volume estimates.
Transportation Planning (CE 448) Project 2
Spring 2018 Dr. Mehrnaz Ghamami
Comparisons of Trip Estimates
Once the ITE-type and new regression models are calibrated, they will be used to make various predictions (estimates) of trips generated by your site. These predictions will be compared to those obtained using the ITE software and the actual field observations that you made.
Thus, in addition to making estimates using the models that you developed, it will also be necessary to use the ITE trip generation software (available on transportation laboratory computers and/or to each group) to predict the various driveway volumes.
As noted above, once the models are calibrated, you will use them to make predictions for all dependent variables for your group’s site. You will need to complete a table similar to the one shown below and thoroughly discuss the differences in the predictions between the various models and the values observed in the field (note that it is possible that not all cells can be filled).
Dependent variables
Observed
trips/volume
Estimated Trips
Traditional ITE software/ models
New regression models
Transportation Planning (CE 448) Project 2
Spring 2018 Dr. Mehrnaz Ghamami
PREPARATION OF FINAL REPORT
A final report must be prepared which includes: a description of what you did; the results of your analysis including the models that you developed; discussion of the quality of your models and the differences between the predictions of the models for your site and the observed values; and overall conclusions regarding using the various methods and models to predict the traffic related to Bus Park and Ride facilities.
Questions/comments about the project
I will be glad to answer questions about the project in class, over e-mail, or during office hours.
You will need to use a statistical package (e.g., Microsoft Excel, Mini-Tab, R studio, the Statistical Package for the Social Sciences (SPSS)) to do the actual modeling.
Comment on proposal for data collection
For this project, the first submission is a formal business letter from your group to me proposing your data collection plan. This should include the following points:
1. Identification of your project site—note that approval of the site should be obtained prior to the submission of the outline (prior approval is encouraged as sites are assigned on a first-come, first-serve basis). A drawing of the site is required.
2. A time schedule for collection of the required data (no weekends, weekdays only).
3. A description of how the data will be collected. This should be based on a field visit that includes collecting some preliminary data so that it is clear, for example, where data collectors will have to be situated so that they can, in fact, collect the required data. This includes a copy of any/all forms to be used during data collection. It is your responsibility to obtain any permission necessary to collect data at your site.
4. A description of how data will be transferred to a file and delivered (e.g., software used).
Comments on and some requirements for the final project report
1. The report must “stand alone” and contain a presentation and discussion of the assignment, the analysis, the results, and the conclusions—as if you were consultants reporting to a city agency. See also the “instructor’s grading rubric”.
2. The report does not have to contain a review of regression procedures, but those aspects which are germane to the analysis must be included. For example, it would be appropriate to discuss some of the assumptions that are inherent in the methods used; the effects of violating these assumptions; and the effects of making the assumptions in the first place.
3. Please note that an important part of this project is how well it is presented. That is, a technically perfect project that is sloppily prepared and presented could result in a grade much lower than you might expect. To this end, I encourage you to make use of the university writing center in preparing your final report.
Some additional comments on the overall assignment
You may wonder what do I “really” want for this assignment. This is a fair question. I want a product that is: technically correct; well developed; thoroughly explained; well organized; and clearly presented. If assumptions are made, state them; if decisions are made, explain and defend them; if references are used, cite them.
Transportation Planning (CE 448) Project 2
Spring 2018 Dr. Mehrnaz Ghamami
Some hints on the analysis that is expected (this list is not necessarily exhaustive):
1. The data should be inspected both statistically and graphically for correlations and the general relationships between key variables.
2. Alternative forms of the variables should be examined (maybe they work better)—for example, what is the impact of using variable X and, alternatively, log(X) or X2 on the predictive capability of a model.
3. A summary of appropriate statistical measures should be provided for your models as well as discussion of how these statistics were considered in making decisions about the validity and utility of various models.
4. Your group must decide (and support the decision) whether the “best” model includes all possible explanatory variables or only some specified subset of them.
5. The model must also be evaluated from a pragmatic standpoint—does it make sense, is it realistic? You are supposed to be modeling causality—does your model reflect this?
6. While not every model examined or every plot of the data that is made should be shown in the report, representative examples should be shown and discussed. The final models must be presented in the body of the report. This project is meant to be done independently by group—that is, each group should work separately. It is, however, permissible to talk about general concepts, problems with the analysis package, and other general topics among your non-group peers.
For a design project there are generally not “right” and “wrong” answers per se. There are, however, degrees of achievement. While attaching absolute grades to your submissions is difficult, it will be done. If there are significant differences in the levels of achievement by the different groups, there will be similar differences in the grades received.
Transportation Planning (CE 448) Project 2
Spring 2018 Dr. Mehrnaz Ghamami
APPENDIX- DATA FORMAT
General
This handout defines the format for data you collect for project 2—you MUST use this format.
Constructing the data file
The data you collect in the precise format specified below.
1. The data are to be contained in one file.
2. The file must be a Microsoft Excel file.
3. The data in the file must be organized precisely as indicated below.
4. Email the .xlsx file as an attachment. Use your group number in the filename (i.e. group3.xlsx).
5. Each line of data in the file represents one 15-minute period.
6. Each file will have 12 lines of data.
The data must be organized “flush left” in the file. The following list shows the name of each variable and its location in the file—i.e., “group” (group number) is the first column, then there is “GFA” (gross floor area) in column 2, and so on:
group, GFA, seats, park, driveup, register, employee, Day, Time, VOLenter, VOLexit, drvupVOL, maxqueue, nearVOL, farVOL, and minorVOL
The definitions of the values taken for the variables “Day” and “Time” are as follow:
ID
Day
1
Monday
2
Tuesday
3
Wednesday
4
Thursday
5
Friday
If you have any questions, please ask. It is very important that the collected data be correctly organized in the file.
If the data are not in the correct format, if the recording medium is “contaminated” with a virus, or if other files are present, your grade will be lowered. The magnitude of such errors (and how much time it takes me to fix them) is directly correlated with how much the grade is lowered!
ID
Time Window
ID
Time Window
ID
Time Window
ID
Time Window
1
11:00 AM
–
11:15 AM
8
12:45 PM
–
1:00 PM
15
2:30 PM
–
2:45 PM
22
4:15 PM
–
4:30 PM
2
11:15 AM
–
11:30 AM
9
1:00 PM
–
1:15 PM
16
2:45 PM
–
3:00 PM
23
4:30 PM
–
4:45 PM
3
11:30 AM
–
11:45 AM
10
1:15 PM
–
1:30 PM
17
3:00 PM
–
3:15 PM
24
4:45 PM
–
5:00 PM
4
11:45 AM
–
12:00 PM
11
1:30 PM
–
1:45 PM
18
3:15 PM
–
3:30 PM
25
5:00 PM
–
5:15 PM
5
12:00 PM
–
12:15 PM
12
1:45 PM
–
2:00 PM
19
3:30 PM
–
3:45 PM
26
5:15 PM
–
5:30 PM
6
12:15 PM
–
12:30 PM
13
2:00 PM
–
2:15 PM
20
3:45 PM
–
4:00 PM
27
5:30 PM
–
5:45 PM
7
12:30 PM
–
12:45 PM
14
2:15 PM
–
2:30 PM
21
4:00 PM
–
4:15 PM
28
5:45 PM
–
6:00 PM
