Predicting the effectiveness of commute reduction plans using neural networks

Commute trip reduction plans are being implemented at an increasing number of worksites. In order to be able to structure the most effective plan for a specific worksite, it is necessary to understand the factors that determine commuter response to company incentives to change commuting habits. This study compares the predictive ability of neural networks compared to linear regression models in calculating the change in vehicle trip rate (VTR) at a given worksite over a year long travel plan period. Using a Los Angeles area dataset (n = 3439), linear regression and neural network models were constructed and optimized using input variables including worksite incentives, business type and number of years of incentives at the worksite. Significant differences(p=0.006, 0.007) in program effectiveness were discovered between the results of local authority worksites ([delta]VTR = -1.495) and both businesses ([delta]VTR = -0.986) and hospitals ([delta]VTR = -0.728). It was determined that the neural network (R² = 0.229) performed better than the linear regression models (R² = 0.038) when evaluated by R², representing an improvement of 0.014 on previous models.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Rush, Monica R

Advisor dc:contributor.advisor

• David Wallace.

• Mechanical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/32934

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/32934