Traffic congestion continues to be a growing problem for cities of all sizes in the United States. Transportation agencies in urban areas are facing the difficult challenges of providing an efficient and reliable transportation system for residents and businesses despite ever-diminishing resources. Agencies in these areas need the capability of determining the future benefits of transportation investments so they can communicate this information to the public. This capability is difficult for many agencies, especially some of the smaller ones, who may not have the resources to make these analyses without turning to expensive long-range models.
This research uses readily available socio-economic, land use, and traffic congestion data from many of the Texas urban areas to create prediction models to estimate future traffic congestion levels. Many of the transportation agencies that could utilize this tool do not have the resources to deal with large complex databases. Thus, basic information such as income, employment, single family residences, or commercial properties, to name a few, is used to create the predictions models.
Results from this research show that traffic congestion prediction models can be created from socio-economic and land use data. These models were created for eighteen individual Texas urban areas and several combinations of areas. Transportation agencies could use the results of this research to estimate future congestion in their respective areas.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/1245 |
| Date | 15 November 2004 |
| Creators | Schrank, David Lynn |
| Contributors | Pugh, David L. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 1038604 bytes, 714549 bytes, electronic, application/pdf, text/plain, born digital |
Page generated in 0.001 seconds