As engineers, it is our job to make the roads safe and efficient for all users.Here is the economic logic behind increased efforts to promote bicycle use:Cars enjoy huge direct subsidies in the form of road construction and public parking spaces, as well as indirect subsidies to the oil industry that provides their fuel. These subsidies far exceed the tax revenue generated by car use (as this excellent discussion of the technical issues at stake in these calculations makes clear.)Yet cars impose major social costs: their use contributes to global warming, traffic congestion, accident fatalities and sedentary lifestyles.
Monday, July 4, 2011
Economic Justification of Alternative Transport
Nancy Folbre, a professor of economics at the University of Massachusetts, had a nice post on the NYTime's Economix blog over the weekend. From the piece:
Tuesday, April 19, 2011
ITE elections on Thursday
I hope you had a nice weekend and that this email reaches you well. First off, I would like to thank everyone that helped out with the cookout. It was a big success and I couldn’t have done it without your help. This Thursday at 11:00 am in SEB 316 Marsha Anderson Bomar will be speaking. As usual, lunch will be provided. We will also hold officer elections for the president, vice president of outreach, and vice president of finance positions. I have received the following nominations so far.
President: Donny Katz
VP Outreach: Greg Macfarlane
VP Finance: Chris Toth
If you are interested in running for one of these positions or you would like to nominate someone, please send me an email. Thursday is your last chance to purchase tickets ($10) for next Tuesday’s GT vs. UGA baseball game at Turner field. It will also be your last chance to order an ITE at GT polo shirt. They are $22.
Second in District!
Georgia Tech ITE was named the runner-up in the annual Southern District student chapter of the year competition. This award is validation of the success ITE@GT has seen in recruiting members, planning activities, and providing community service.
Obesity and the Built Environment
Planners have asserted for years that the built environment affects health, claiming statistical correlation between mixed land uses and lower obesity rates. This finding is important, because it casts the blame for American obesity on the suburban landscape. Planners now have another tool in their arsenal arguing for better land use mix (a cause that I support). As the thinking goes, people who are able to walk to activities will do so, and thereby have lower obesity rates than the general population.
I just finished reading a paper (by my advisor and her former doctoral student in Transportation) that critiques the methodology in at least one of these studies, a 2004 study by Frank, Andersen, and Schmid (American Journal of Preventative Medicine) relating obesity to travel patterns in Atlanta. The authors constructed a linear regression model based on a household travel survey of Atlanta residents. They showed statistically significant positive correlation with age (older people are more obese), in-vehicle travel time (obese people have longer commutes), and being black. Statistically significant negative correlation was seen in education (people with higher incomes are less likely to be obese), income, daily walking distance, uniform land use patterns, and being a woman.
My advisor's analysis examined the AJPM model and improved on it by introducing non-linear relationships. For example, people tend to gain weight until they reach about 60, and then lose it again. So using age as a strictly linear variable misses the impact that late age weight loss may have on obesity. This and other adjustments increase the ability of the model to predict obesity far more than improving the prediction of the land use field. So while people who live in mixed-use areas are obese less frequently than the general population, land use by itself is a poor predictor of obesity.
What are needed are studies that carefully measure both travel behavior and Body Mass Index. But the studies have not indicated that health records should be a major focus of travel survey collection. Indeed, theTransportation paper indicates that shifting the focus of a travel survey to health information can be a very risky proposition, because you may end up under-sampling more important variables, like actual travel behavior.
I just finished reading a paper (by my advisor and her former doctoral student in Transportation) that critiques the methodology in at least one of these studies, a 2004 study by Frank, Andersen, and Schmid (American Journal of Preventative Medicine) relating obesity to travel patterns in Atlanta. The authors constructed a linear regression model based on a household travel survey of Atlanta residents. They showed statistically significant positive correlation with age (older people are more obese), in-vehicle travel time (obese people have longer commutes), and being black. Statistically significant negative correlation was seen in education (people with higher incomes are less likely to be obese), income, daily walking distance, uniform land use patterns, and being a woman.
My advisor's analysis examined the AJPM model and improved on it by introducing non-linear relationships. For example, people tend to gain weight until they reach about 60, and then lose it again. So using age as a strictly linear variable misses the impact that late age weight loss may have on obesity. This and other adjustments increase the ability of the model to predict obesity far more than improving the prediction of the land use field. So while people who live in mixed-use areas are obese less frequently than the general population, land use by itself is a poor predictor of obesity.
What are needed are studies that carefully measure both travel behavior and Body Mass Index. But the studies have not indicated that health records should be a major focus of travel survey collection. Indeed, theTransportation paper indicates that shifting the focus of a travel survey to health information can be a very risky proposition, because you may end up under-sampling more important variables, like actual travel behavior.
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