National Academies Press: OpenBook

Trip Generation Rates for Transportation Impact Analyses of Infill Developments (2013)

Chapter: Chapter 6 - Conclusions and Recommendations

« Previous: Chapter 5 - Confirming the Proposed Approach for Estimating Infill Trip Generation
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Suggested Citation:"Chapter 6 - Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2013. Trip Generation Rates for Transportation Impact Analyses of Infill Developments. Washington, DC: The National Academies Press. doi: 10.17226/22458.
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Suggested Citation:"Chapter 6 - Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2013. Trip Generation Rates for Transportation Impact Analyses of Infill Developments. Washington, DC: The National Academies Press. doi: 10.17226/22458.
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Suggested Citation:"Chapter 6 - Conclusions and Recommendations." National Academies of Sciences, Engineering, and Medicine. 2013. Trip Generation Rates for Transportation Impact Analyses of Infill Developments. Washington, DC: The National Academies Press. doi: 10.17226/22458.
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37 The research team concludes that the fundamental approach of adjusting baseline ITE trip generation data with factors derived from empirical data, or with factors extracted from HTSs, is logical and intuitive to users and can be a useful tool for estimating trip generation in traffic impact analyses of urban infill development. This conclusion is despite the fact that there were insufficient study sites to validate the proposed methodology or draw definitive conclusions on the accuracy of the method’s estimates of infill trip generation. 6.1 Principal Conclusion Based on the review of the data and analyses summarized in Chapters 4 and 5, the research team draws the following conclusion: Basing the approach on the collection of empirical data, as well as an alternate method to extract data from travel surveys, the proposed methodology meets the research objective. In Chapter 1, the research objective was stated as: Develop an easily applied methodology to estimate automobile trip generation and mode share of non-vehicular trips that can be used in the preparation of site-specific transportation impact analyses of infill development projects located within existing higher-density built-up areas. The research team believes this objective has been met based on the following four reasons: (1) The method has compatibility with existing traffic impact analysis methods (i.e., ability to estimate peak hour, directional-dependent variables). The method recommended in this report directly modi- fies the data most commonly used (and frequently required by agencies) in preparing traffic impact studies—ITE trip generation rates. As an adjustment factor to one variable in the process, derived using common techniques familiar to the transportation professional, the method does not materially alter the established procedures for preparing impact studies. (2) The method applies to the land uses in the ITE Trip Generation Manual and has few, if any, restrictions on land use categories and geography. The proxy method uses an adjustment factor applied to baseline ITE trip generation data and, therefore, may be used with any of the land use categories in the ITE Trip Genera- tion Manual as long as the user has or collects the required data from a site or sites with the same land use and other similar characteristics. The use of the household travel survey method for deriving adjustment factors, however, is restricted to metropolitan areas that have current travel surveys and limits land use categories to general common categories such as residential, school, office, retail, and restaurant. (3) Input data needed to apply the method are readily available, or the ease and cost of collecting and apply- ing the data are reasonable. Practitioners who regularly prepare traffic impact studies are familiar with collecting the type of data required under the minimum data collection and comprehensive data collection variants. Once the data have been acquired, application of the data uses a simple and transparent process. The household travel survey method requires having or learning specialized skills in database manipulation, and because the structure of travel surveys varies widely from region to region, extracting data from a survey in a specific universal procedure cannot be prescribed. (4) The method would likely be accepted by members of the transportation planning and traffic engineering profes- sion who prepare and review site traffic impact analyses. C H A P T E R 6 Conclusions and Recommendations

38 The research team is confident the recommended method will be accepted by the profession due to the following reasons: • The overall approach and methodological variations for obtaining data are compatible with the current practice of preparing impact studies and do not require a significant shift in paradigm to use. • The method is simple in its structure, transparent in its computations, and intuitive to the user familiar with pre- paring impact studies. • The method is easy to document and justify in a traffic impact study, and is simple to describe to laypeople and decision makers. 6.2 Additional Conclusions The sample of case studies is too small to be conclusive. The ITE Trip Generation Manual’s User’s Guide and Hand- book recommends collecting data from at least five sites for each LUC to test the validity of local trip generation rates. A sample size of five sites per LUC per context would require 60 or more to perform a complete verification and valida- tion analysis (i.e., 5 validation sites X 4 land use categories X 3 context categories). In this research study, 14 sites were studied for four LUCs. Additional data collection may have achieved the mini- mum sample size recommended by ITE if the case study sites had been located in more consistent contexts. However, because of contextual inconsistencies, none of the four LUCs have a sample size sufficient for the investigators to conclude that the proposed estimation method is either valid or invalid. The analysis of case study sites lacked sufficient empiri- cal vehicle occupancy data. The testing and verification of the method assumed values for baseline vehicle occupancies in the computation to convert ITE trip rates to person-trip rates. For some infill sites, vehicle occupancy data were either not collected or were collected informally. The results, there- fore, lacked sufficient empirical data on vehicle occupancy representing the ITE baseline data due to limited resources to conduct comprehensive surveys, so that Step 4, converting infill person-vehicle trips to infill vehicle trips (18), had to rely on assumed vehicle occupancies (from the ITE Trip Gen- eration Manual and other sources) for most of the sites. The use of ITE published vehicle occupancies, while acceptable in a validated estimation model, adds uncertainty to the results of these initial tests. Small sample size cannot show the distribution of data or meaningful calculation of statistical measures. The data in the ITE Trip Generation Manual typically show a scatter of data points above and below the actual trip generation value. Larger sample sizes allow for the calculation of the mean and standard deviation of the samples, as well as for checking for anomalies and abnormal distribution patterns. The data col- lected for the 14 case study sites are anticipated to show simi- lar scatter from a known mean rate, but not with the limited data points that exist. With a robust database of case study sites, the method can be compared with other known data patterns, and more definitive conclusions can be drawn. Determining consistency in context and land use char- acteristics for selected case study sites is a critical task in validating the method. Case study sites need to be carefully selected so as to be as consistent as possible in context and be similar in some other development characteristics to sub- urban examples representative of ITE data. Inconsistency in context or the specific characteristics of an individual LUC can greatly affect the outcome of the validation tests. This is largely a qualitative assessment that is difficult to conduct remotely and should be conducted by an experienced trans- portation professional in the field. 6.3 Future Research The following are the research team’s primary recommen- dations for future research, as well as several secondary recom- mendations, developed during the verification process. The primary recommendation of the research team is to focus future research on validating one LUC in one urban context in one metropolitan area. This will produce a more definitive conclusion on whether the method can be validated for a given use, while minimizing the required resources. Future research efforts could include validation of both the proxy site and the household travel survey methods concur- rently, and then compare the results. Based on the quantity and quality of data collected to date, the research team recom- mends starting with the residential LUC, in a GU/UC context, within the Washington, D.C., metropolitan area. Validation and proxy site data collection can add to the data already col- lected in this study. In validating the household travel survey method, future researchers may choose to use the mode share and average vehicle occupancy data that were extracted and summarized in this study, or could begin anew and extract the data independent of this study (with consistent use of crite- ria defining context) allowing for a comparison of extraction methods and resulting adjustment factors. Future validation of single land use categories with single contexts could include a minimum of five sites (and pref- erably up to 30) for the selected LUC and the selected con- text. The minimum travel and site-related data suggested for future validation of the methodology are: • Full-cordon person-trip counts by mode entering site building(s), or at least a cordon count of persons using automobile and persons using non-automobile modes of travel.

39 • Vehicle occupancy of at least 25% to 50% of the automo- biles accessing the site during the survey period (collection of enough vehicle occupancy data to ensure adequate data are available for each hour of the survey period). • Collection of mode share and vehicle occupancy data for sites representing baseline ITE trip data within the same metropolitan region as the infill data collection described previously. • The percentage of occupied development units (e.g., floor area, dwelling units) at the time of the surveys for both the infill and baseline ITE validation sites. The following secondary research recommendations are related to further developing the data and analysis included within this study: • Expand upon the work performed in this research study to develop mode share and vehicle occupancy adjustment factors for suburban contexts of the Washington, D.C., and San Francisco Bay Area HTSs to be used as initial default factors for converting baseline ITE trip generation esti- mates to person-trip estimates. • Compare the Washington, D.C., residential and office mode-share data to 2010 census journey-to-work data for the census blocks in which the study sites are located. Assess whether the census data can also be used (with sufficient accuracy) for other work-related site trip generation esti- mates and which ITE land use classifications could be cov- ered. With knowledge of the proportion of commute trips within the peak hours, the comparison could be normalized and could provide a quantitative check on the household travel survey method’s predictive capability. • Validation of the household travel survey method may be done using the same validation sites to compare the effect of adjustment factors derived with and without the use of the HTS expansion and weighting factors. This requires applying the expansion and weighting factors to the subset of data extracted for a specific context (before any further extraction of data for land use, time of day, mode of travel, etc.) and deriving mode share and vehicle occupancy from the expanded and weighted data as well as the non-expanded and non-weighted data. Using the same validation sites, the predicted versus actual results between the adjustment factors from the expanded/weighted data and the non- expanded/non-weighted data are compared. If shown to affect the results of the validation significantly in either direc- tion, a validation process comparing the effects of weighting and expansion may be implemented in parallel until enough sites have been analyzed to determine if expanding and weighting the travel survey data have a significant effect on the method’s accuracy, either positively or negatively. • Review and compare data on person trips per household in various contexts from Washington, D.C., and other HTS data from metropolitan areas throughout the United States to determine a relationship between person trips and con- text. A preliminary statistical assessment using the San Francisco 2000 BATS data may be used as an example of one method of determining this relationship (19).

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TRB’s National Cooperative Highway Research Program (NCHRP) Report 758: Trip Generation Rates for Transportation Impact Analyses of Infill Developments details a procedure for analyzing potential vehicular trip generation impacts in urban and urbanizing locales.

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