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.
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
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.