Estimating Through Trip Travel without External Surveys

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 often 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. Previous research 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. The first model estimates the portion of all trips at an external station that are through trips. The second model estimates the external stations where the trips originated. The models produce separate results for commercial and non-commercial vehicles, and these results can be used to develop through trip tables. For predictor variables, the models use results from a simple gravity model; the average daily traffic (ADT) at each external station as a proportion of the total ADT at all external stations; the number of turns on the routes between external station pairs; and whether the route 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, indicating that the models can be useful for practical applications. Talbot, Burris and Farnsworth 2 INTRODUCTION Through trips are an important part of travel demand modeling. To develop and calibrate the through-trip component of travel demand models, Metropolitan Planning Organizations (MPOs) use data from external surveys, which gather information from travelers entering or leaving the study area. In the past, transportation planners generally conducted external surveys using the roadside interview technique at locations where traffic enters and exits the study area (external stations). Although the roadside interview method is an effective way to collect information on through trip travel patterns, it also has potential drawbacks. First, the roadside interview method may create an unsafe situation for drivers because they may not expect to encounter stopped or slowed traffic at the location where the survey is conducted. Second, roadside interviews cause delays for drivers who are surveyed, and may also cause delays for drivers who are not surveyed. Third, some drivers consider being stopped for a survey as an invasion of their privacy. Fourth, the roadside interview method is expensive, with the cost for a complete set of external surveys for a study area usually exceeding $100,000 (Texas Transportation Institute, unpublished internal reports, 2001-2006). For some or all of these reasons, in early 2008 the Texas Department of Transportation suspended external surveys throughout the state. Texas MPOs now need a way to estimate through trips without recent external survey data. Previous research efforts developed models for estimating through trip patterns without external survey data. However most of these models focused on study areas with less than 50,000 people, while MPO study areas have more than 50,000 people. In addition, most of the previous research used linear regression, which may not be the best approach because the variable of interest is a proportion, rather than a continuous number. To improve on these previous models, this research developed a system of two models to estimate through trips without the benefit of external survey data. Each model was developed using logistic regression, which is appropriate for data where the responses are proportions. The first model is a binary logit model which estimates the proportion of trips exiting the study area at an external station that are through trips. The second model is a multinomial logit model which distributes these through trips between all the external stations where a through trip could have entered the study area. These models were developed for study areas with 50,000 to 6 million people. Model evaluation shows that these models perform well and will be useful for estimating through travel. LITERATURE REVIEW Modlin developed a set of multiple linear regression equations to estimate through trips for small study areas (study areas with less than 50,000 people). He used the roadway functional classification, average daily traffic, percent trucks, and percent pickups and panel vans at each external station, and the population of the study area as explanatory variables. Modlin's model has two stages. The first stage estimates the percent of all trips at each external station that are through trips. The second stage distributes the through trips between external stations (1). A few years later, Pigman published a set of similar equations for small study areas (2), and Modlin followed up with a new set of regression equations. This second set of equations was similar to his first, but also included route continuity, which is a binary variable signifying whether or not two external stations are on the same highway route (3). Talbot, Burris and Farnsworth 3 The Modlin and Pigman models considered the study area in isolation from the rest of the world. More recent research has worked to develop better models by incorporating the geographic and economic context of the study area into the model. Anderson reported on the results of an evaluation of the effectiveness of simple gravity models for estimating through trips. Anderson tested the three models using a city in Iowa with four external stations and a population of 8,500 (4). Anderson later applied one of the models to three small cities in Alabama (5). Horowitz developed a model which assigns a “catchment” area from the region outside of the study area to each external station, and calculates a weight factor for trips between two external stations by calculating the probability that a line connecting two points within their catchment areas passes through the study area, or crosses a barrier to travel between catchment areas. These weight factors are then used to estimate through trips using the procedure outlined in the first Quick Response Freight Manual (6,7). Han combined the work of Modlin, Anderson and Horowitz. Like the Modlin model, Han's model is a set of regression equations, and uses information about the traffic and roadway at the external station, and about the study area, as explanatory variables. However, the Han model also includes a simple gravity model and Horowitz weights as explanatory variables. Han's model was developed for small and medium sized study areas with up to 200,000 people (8,9). Anderson also developed Modlin-like regression equations, and included a variable to signify the presence of a near-by major city (10). Martchouk and Fricker recently proposed modeling through trips using logistic regression rather than linear regression, as all previous regression-based models had done. They developed two alternative multinomial logit models which each predict the percent through trips and the distribution of through trips between external stations. The authors also tested a nested logit model, but found the nested structure unnecessary. These models were developed using results from regional model subarea analyses for 15 small urban areas in Indiana. At least one of the models was tested using results from license plate origin-destination surveys in two urban areas in Indiana. The model performed better than Modlin’s model, Anderson’s model, and subarea analysis for one urban area, but performed worse than Modlin’s and Anderson’s models for the second urban area. The models use the average daily traffic at each external station and whether the route between two external stations is continuous as predictor variables. These models provided the basis for the modeling approach for this research (11). These efforts yielded insight into the variables, characteristics and methods that may prove useful in estimating through trips in larger urban areas. Therefore, many of the variables discussed above were investigated for their potential use in the models developed here. DATA COLLECTION Model development required a significant amount of data, including external survey data, traffic data, roadway data, demographic data, interaction score data, and measures of external station separation. The external survey data was used as the source for observed through trip data, and it came from external surveys performed in 13 study areas in Texas between 2001 and 2006, listed in Table 1. Average values of key variables at these external stations are included in Table 2. Other data were used as sources for the through trip predictor variables that were considered for inclusion in the models. These predictor variable data needed to be available without the use of an external survey. Talbot, Burris and Farnsworth 4 Traffic data consisted of information on the average daily traffic and the proportion of large vehicles at each external station. This data was obtained from automated vehicle classification counts at each of the external stations. This data was used as a potential predictor, but is also necessary for expanding the model results to represent measured volumes. Roadway data included the number of lanes at each external station, whether the road at the external station was divided, and whether the road at the external station was a limited-access facility. The roadway data came from examining satellite imagery. The demographic data came from the U.S. Census Bureau and the U.S. Bureau of Labor Statistics, and included the population, employment, average income and surface area of the study area. The interaction score quantifies the volume of traffic between urban areas, then assigns that estimated quantity to the appropriate study area external stations. The data for calculating the interaction score came from the U.S. Census Bureau, which publishes population estimates for each Census urban area and urban cluster, as well as provides a GIS file with polygons for all urban areas and clusters throughout the United States (12). Least time routes between pairs of urban area centroids and between centroid-external station pairs were extracted from a web routing service and analyzed programmatically to determine which of the study area external stations, if any, the route passes through. The interaction score is defined by  ij INT                       } , { 2 6 10 w v U v U w vwij vw w v f D P P (1)