Daniel A. Badoe

Transportation–land-use interaction: empirical findings in North America, and their implications for modeling

Abstract The last two decades have witnessed the implementation of various policies based on land-use to address problems stemming from automobile ownership and use. There are, however, questions with respect to the efficacy of such policies. This paper therefore reviews the literature on empirical studies of the transportation–land-use interaction with the objective of identifying the current state of knowledge concerning the interactions, particularly with respect to the impact land-use policies are likely to have on the system. The focus is on studies conducted in North America. The results are mixed; some studies conclude that urban densities, traditional neighborhood design schemes, and land-use mix have an impact on auto ownership and use. Other studies find the impact of such variables to be at best marginal. Gaps in our understanding of the interaction are identified. These are found to be primarily the result of data limitations and methodological weaknesses. A detailed discussion of the implications of the findings for the development and application of integrated transportation–land-use models is provided, with the recommendation that without such an integrated approach to analyzing the transportation–land-use interaction, any study of impacts of urban form on travel behavior is likely to yield erroneous results.

Forecasting Travel Demand with Alternatively Structured Models of Trip Frequency

Abstract This paper develops alternatively structured trip frequency/generation models, and investigates their forecast performance. The first model presented is the simple linear model with a discussion of its theoretical shortcomings. Models that address, in a progressive fashion, the underlying shortcomings of the linear model are then presented. These models are namely the truncated normal model, the Poisson model, the negative binomial model, and an ordered logit model. The modeling unit employed in the study is the individual. The models are assessed by how closely they are able to replicate trips produced by each individual in the dataset, and by each traffic zone. This assessment of performance in prediction is conducted on an estimation dataset collected in the Toronto Region in 1986, and on an independent dataset collected in the same geographic region, 10 years later, in 1996. The results show that, notwithstanding the simplicity of the simple linear model and its lack of an explicit underlying travel behavioral theory, it predicts travel in the base and forecast years with less error compared to any of the more complex models.

Review of Policies on Access to Transportation Planning Data and Models: Implications for Transportation Planning Agencies

Transportation planning agencies receive requests from both public and private entities to share their transportation planning data, models, and model output. In addition, some agencies receive requests from these entities to undertake special model runs on their behalf. Planning agencies have to respond to these requests with consideration, first, of state laws governing access to public information and, second, of the professional staff time required and the significant monetary expenses incurred in the process. Given the adverse state of public finances, planning agencies may face resource challenges in responding to such requests. This paper, first, presents the results of a literature review on data sharing/release policies for the transportation sector. Second, results of a nationwide survey of transportation planning agencies, conducted to identify primary concerns related to data release and cost recovery, are presented. Finally, the paper discusses the implications of these findings for public agencies seeking to develop policies. The literature review points to the need to develop data release and sharing arrangements so that limited public resources can be maximized and to ensure data that is released is well documented so that misuse does not occur. The survey findings indicate that the area of greatest concern for most planning agencies in sharing information is the potential for misinterpretation or misuse of data or models because they have found that often, requesters do not understand the limitations or appropriate uses of the data or models. To address these concerns, this paper makes a strong case for agencies to develop formal data and model release policies and to define a specific cost recovery structure if the agency intends to charge for services.

Receipt of Travel Survey Advance Letter and Its Impact on Reported Trips and Number of Phone Calls for Survey Completion in Telephone Surveys

AbstractResearch is conducted into the effect the receipt of a travel survey advance letter, sent as part of a household travel survey, has on the person trips reported by a household’s survey respondent for each household member and on the number of phone calls made to a household to successfully complete the survey using two data sets collected in household travel surveys conducted in the Greater Toronto Area in 2001 and 2006. The results of the statistical analysis of the data led to the conclusion that not receiving the survey advance letter resulted in survey respondents significantly underreporting the trips made by their respective household members. The underreporting varied with trip purpose, with home-based mandatory trips not being underreported while home-based discretionary and non-home-based trips were significantly underreported. Additionally, households not receiving the survey advance letter required significantly more phone calls to complete the survey compared to households that receive...

Estimating Household Trip Rates for Cross-Classification Cells with No Data: Alternative Methods and Their Performance in Prediction of Travel

This paper investigates a number of alternative methods for addressing the empty-cell problem of traditional cross-classification analysis. Data used in the study were collected in the Toronto region in 1986, 1996, 2001, and 2006. Alternative models, developed on each year’s data, were assessed for how well they predicted travel at the disaggregate household level and at the aggregate traffic analysis zone level in the respective years. In addition, the alternative models estimated on the 1986 data set were assessed for their ability to replicate travel in 1996 and 2006. The results show that a method proposed by Mandel and a model developed in this research, which estimates the household trip rate for an empty cell through a linear combination of the predictions yielded by row and column models, overall give the best forecast performance of travel. They perform better than multiple classification analysis, which is the current industry standard for addressing this shortcoming of traditional cross-classification analysis. The combined categories model also performed very well, particularly in predicting travel at the aggregate level of planning interest.