Logit Models for Estimating Urban Area Through Travel

• Main Author: Talbot, Eric
• Other Authors: Burris, Mark
• Format: Others
• Language: en_US
• Published: 2011
• Subjects: travel demand modeling; logit models; through trips
• Online Access: http://hdl.handle.net/1969.1/ETD-TAMU-2010-08-8410

Since through trips can be a significant portion of travel in a study area, estimating them is an important part of travel demand modeling. In the past, through trips have been estimated using external surveys. Recently, external surveys were suspended in Texas, so Texas transportation planners need a way to estimate through trips without using external surveys. Other research in the area has focused on study areas with a population of less than 200,000, but many Texas study areas have a population of more than 200,000. This research developed a set of two logit models to estimate through trips for a wide range of study area sizes, including larger study areas. The first model estimates the portion of all trips at an external station that are through trips. The second model distributes those through trips at one external station to the other external stations. The models produce separate results for commercial and noncommercial vehicles, and these results can be used to develop through trip tables. For predictor variables, the models use results from a very simple gravity model; the average daily traffic (ADT) at each external station as a proportion of the total ADT at all available external stations; the number of turns on the routes between external station pairs; and whether the route is valid, where a valid route is one that passes through the study area and does not pass through any other external stations. Evaluations of the performance of the models showed that the predictions fit the observations reasonably well; at least 68 percent of the absolute prediction errors for each model and for the models combined were less than 10 percent. These results indicate that the models can be useful for practical applications.