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• 1. [Article] Improving Vehicle Trip Generation Estimations for Urban Contexts: A Method Using Household Travel Surveys to Adjust ITE Trip Generation Rates

  The purpose of this research is to develop and test a widely available, ready-to-use method for adjusting the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip generation ...

  Citation × Citation Citation Abstract Copy Title: Improving Vehicle Trip Generation Estimations for Urban Contexts: A Method Using Household Travel Surveys to Adjust ITE Trip Generation Rates Author: Currans, Kristina Marie Year: 2013 The purpose of this research is to develop and test a widely available, ready-to-use method for adjusting the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip generation estimates for urban context using regional household travel survey data. The ITE Handbook has become the predominant method for estimating vehicle trips generated by different land uses or establishment, providing a method for data collection and vehicle trip estimation based on the size of the development (e.g. gross square footage, number of employees, number of dwelling units). These estimates are used in traffic impact analysis to assess the amount of impact the development will have on nearby transportation facilities and, the corresponding charges for mitigating the development's negative impacts, with roadway expansions, added turning bays, additional parking or traffic signalization, for example. The Handbook is often criticized, however, for its inability to account for variations in travel modes across urban contexts. For more than fifty years, ITE has collected suburban, vehicle-oriented data on trip generation for automobiles only. Despite the provision of warnings against application in urban areas, local governments continue to require the use of the ITE Handbook across all area-types. By over predicting vehicle traffic to developments in urban developments, developments may be overcharged to mitigate these developments locating in urban environments despite the lower automobile mode shares, discouraging infill development or densification. When ITE's Trip Generation Handbook overestimates the vehicle impact of a development, facilities are also overbuilt for the automobile traffic and diminishing the use of alternative modes. When ITE's TGH underestimates this impact, adjacent facilities may become oversaturated with traffic, pushing cars onto smaller facilities nearby. Currently, there is momentum amongst practitioners to improve these estimation techniques in urban contexts to help support smart growth and better plan for multiple modes. This research developed and tested a method to adjust ITE's Handbook vehicle trip generation estimates for changes in transportation mode shares in more urban contexts using information from household travel surveys. Mode share adjustments provide direct reductions to ITE's Handbook vehicle trip estimations. Household travel survey (HTS) data from three regions were collected: Portland, Oregon; Seattle, Washington; and Baltimore, Maryland. These data were used to estimate the automobile mode share rates across urban context using three different adjustment methodologies: (A) a descriptive table of mode shares across activity density ranges, (B) a binary logistic regression that includes a built environment description of urban context with the best predictive power, and (C) a binary logistic regression that includes a built environment description of urban context with high predictive power and land use policy-sensitivity. Each of these three methods for estimating the automobile mode share across urban context were estimated for each of nine land use categories, resulting in nine descriptive tables (Adjustment A) and eighteen regressions (Adjustments B and C). Additionally, a linear regression was estimated to predict vehicle occupancy rates across urban contexts for each of nine land use categories. 195 independently collected establishment-level vehicle trip generation data were collected in accordance with the ITE Handbook to validate and compare the performance of the three adjustment methods and estimations from the Handbook. Six land use categories (out of the nine estimated) were able to be tested. Out of all of the land uses tested and verified, ITE's Trip Generation Handbook appeared to have more accurate estimations for land uses that included residential condominiums/townhouses (LUC 230), supermarkets (LUC 850) and quality (sit-down) restaurants (LUC 931). Moderate or small improvements were observed when applying urban context adjustments to mid-rise apartments (LUC 223), high-turnover (sit-down) restaurants (LUC 932). The most substantial improvements occurred at high-rise apartments (LUC 222) and condominiums/townhouses (LUC 232), shopping centers (LUC 820), or coffee/donut (LUC 936) or bread/donut/bagel shops (LUC 939) without drive-through windows. The three methods proposed to estimate automobile mode share provides improvements to the Handbook rates for most infill developments in urban environments. For the land uses analyzed, it appeared a descriptive table of mode shares across activity density provided results with comparable improvements to the results from the more sophisticated binary logistic model estimations. Additional independently collected establishment-level data collections representing more land uses, time periods and time of days are necessary to determine how ITE's Handbook performs in other circumstances, including assessing the transferability of the vehicle trip end rates or mode share reductions across regions. Title: Improving Vehicle Trip Generation Estimations for Urban Contexts: A Method Using Household Travel Surveys to Adjust ITE Trip Generation Rates Author: Currans, Kristina Marie Year: 2013 The purpose of this research is to develop and test a widely available, ready-to-use method for adjusting the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip generation estimates for urban context using regional household travel survey data. The ITE Handbook has become the predominant method for estimating vehicle trips generated by different land uses or establishment, providing a method for data collection and vehicle trip estimation based on the size of the development (e.g. gross square footage, number of employees, number of dwelling units). These estimates are used in traffic impact analysis to assess the amount of impact the development will have on nearby transportation facilities and, the corresponding charges for mitigating the development's negative impacts, with roadway expansions, added turning bays, additional parking or traffic signalization, for example. The Handbook is often criticized, however, for its inability to account for variations in travel modes across urban contexts. For more than fifty years, ITE has collected suburban, vehicle-oriented data on trip generation for automobiles only. Despite the provision of warnings against application in urban areas, local governments continue to require the use of the ITE Handbook across all area-types. By over predicting vehicle traffic to developments in urban developments, developments may be overcharged to mitigate these developments locating in urban environments despite the lower automobile mode shares, discouraging infill development or densification. When ITE's Trip Generation Handbook overestimates the vehicle impact of a development, facilities are also overbuilt for the automobile traffic and diminishing the use of alternative modes. When ITE's TGH underestimates this impact, adjacent facilities may become oversaturated with traffic, pushing cars onto smaller facilities nearby. Currently, there is momentum amongst practitioners to improve these estimation techniques in urban contexts to help support smart growth and better plan for multiple modes. This research developed and tested a method to adjust ITE's Handbook vehicle trip generation estimates for changes in transportation mode shares in more urban contexts using information from household travel surveys. Mode share adjustments provide direct reductions to ITE's Handbook vehicle trip estimations. Household travel survey (HTS) data from three regions were collected: Portland, Oregon; Seattle, Washington; and Baltimore, Maryland. These data were used to estimate the automobile mode share rates across urban context using three different adjustment methodologies: (A) a descriptive table of mode shares across activity density ranges, (B) a binary logistic regression that includes a built environment description of urban context with the best predictive power, and (C) a binary logistic regression that includes a built environment description of urban context with high predictive power and land use policy-sensitivity. Each of these three methods for estimating the automobile mode share across urban context were estimated for each of nine land use categories, resulting in nine descriptive tables (Adjustment A) and eighteen regressions (Adjustments B and C). Additionally, a linear regression was estimated to predict vehicle occupancy rates across urban contexts for each of nine land use categories. 195 independently collected establishment-level vehicle trip generation data were collected in accordance with the ITE Handbook to validate and compare the performance of the three adjustment methods and estimations from the Handbook. Six land use categories (out of the nine estimated) were able to be tested. Out of all of the land uses tested and verified, ITE's Trip Generation Handbook appeared to have more accurate estimations for land uses that included residential condominiums/townhouses (LUC 230), supermarkets (LUC 850) and quality (sit-down) restaurants (LUC 931). Moderate or small improvements were observed when applying urban context adjustments to mid-rise apartments (LUC 223), high-turnover (sit-down) restaurants (LUC 932). The most substantial improvements occurred at high-rise apartments (LUC 222) and condominiums/townhouses (LUC 232), shopping centers (LUC 820), or coffee/donut (LUC 936) or bread/donut/bagel shops (LUC 939) without drive-through windows. The three methods proposed to estimate automobile mode share provides improvements to the Handbook rates for most infill developments in urban environments. For the land uses analyzed, it appeared a descriptive table of mode shares across activity density provided results with comparable improvements to the results from the more sophisticated binary logistic model estimations. Additional independently collected establishment-level data collections representing more land uses, time periods and time of days are necessary to determine how ITE's Handbook performs in other circumstances, including assessing the transferability of the vehicle trip end rates or mode share reductions across regions. Close

• 2. [Article] Integrating Freight into Livable Communities

  Where livability is a goal of the planning process, freight runs the risk of not being considered except as an afterthought or as something to be excluded. Yet, freight is an integral part of local economic ...

  Citation × Citation Citation Abstract Copy Title: Integrating Freight into Livable Communities Author: Williams, Kristine M., Carroll, Alexandria Year: 2015 Where livability is a goal of the planning process, freight runs the risk of not being considered except as an afterthought or as something to be excluded. Yet, freight is an integral part of local economic development. Because economic prosperity is a key characteristic of livable communities, freight must be incorporated into the planning process. This study explores the relationship between freight and livability through a comprehensive literature review and case study research. The final report includes a menu of strategies and case study perspectives that highlight the importance of transportation and land use integration, interagency coordination, and context-sensitivity in freight and livability planning. Title: Integrating Freight into Livable Communities Author: Williams, Kristine M., Carroll, Alexandria Year: 2015 Where livability is a goal of the planning process, freight runs the risk of not being considered except as an afterthought or as something to be excluded. Yet, freight is an integral part of local economic development. Because economic prosperity is a key characteristic of livable communities, freight must be incorporated into the planning process. This study explores the relationship between freight and livability through a comprehensive literature review and case study research. The final report includes a menu of strategies and case study perspectives that highlight the importance of transportation and land use integration, interagency coordination, and context-sensitivity in freight and livability planning. Close

• 3. [Article] Getting to Know the Data: Understanding Assumptions, Sensitivities, Uncertainty, and Being "Conservative" While Using ITE's Trip Generation Data in the Land Development Process

  Many agencies rely on trip generation estimates to evaluate the transportation impacts of land development in urban and suburban areas alike. Over the past decade, substantial attention has been paid to ...

  Citation × Citation Citation Abstract Copy Title: Getting to Know the Data: Understanding Assumptions, Sensitivities, Uncertainty, and Being "Conservative" While Using ITE's Trip Generation Data in the Land Development Process Author: Currans, Kristina Marie Year: 2017 Many agencies rely on trip generation estimates to evaluate the transportation impacts of land development in urban and suburban areas alike. Over the past decade, substantial attention has been paid to one national set of guidelines—the Institute of Transportation Engineers (ITE) Trip Generation Handbook (2014) and corresponding Manual (2012)—focusing in particular to improve the use of these data and supplementary methods for urban contexts. The purpose of this study is to explore the typical data provided in the Handbook, within the context of these new improved state-of-the-art methods. As ITE’s describes, “an example of poor professional judgment is to rely on rules of thumb without understanding or considering their derivation or initial context” (Institute of Transportation Engineers, 2014, p. 3). This research aims to improve the understanding of these data—still ubiquitously used across the US—to encourage increased engagement with their meaning, and following, to provide the users (e.g., engineering, planners, agencies, and developers) with the landscape from which these data were collected and for which they represent. From here, more informed decisions can be made about whether these data provide an adequate or accurate estimation transportation impacts within varying contexts and applications. Title: Getting to Know the Data: Understanding Assumptions, Sensitivities, Uncertainty, and Being "Conservative" While Using ITE's Trip Generation Data in the Land Development Process Author: Currans, Kristina Marie Year: 2017 Many agencies rely on trip generation estimates to evaluate the transportation impacts of land development in urban and suburban areas alike. Over the past decade, substantial attention has been paid to one national set of guidelines—the Institute of Transportation Engineers (ITE) Trip Generation Handbook (2014) and corresponding Manual (2012)—focusing in particular to improve the use of these data and supplementary methods for urban contexts. The purpose of this study is to explore the typical data provided in the Handbook, within the context of these new improved state-of-the-art methods. As ITE’s describes, “an example of poor professional judgment is to rely on rules of thumb without understanding or considering their derivation or initial context” (Institute of Transportation Engineers, 2014, p. 3). This research aims to improve the understanding of these data—still ubiquitously used across the US—to encourage increased engagement with their meaning, and following, to provide the users (e.g., engineering, planners, agencies, and developers) with the landscape from which these data were collected and for which they represent. From here, more informed decisions can be made about whether these data provide an adequate or accurate estimation transportation impacts within varying contexts and applications. Close

• 4. [Article] Addressing Data Challenges for Bicycle Crash Analysis

  Although an increasing number of separated bicycle facilities have been appearing across the US over the last few years, the majority of bicyclists are still traveling on roadways shared with motorized ...

  Citation × Citation Citation Abstract Copy Title: Addressing Data Challenges for Bicycle Crash Analysis Author: Christofa, Eleni Year: 2017 Although an increasing number of separated bicycle facilities have been appearing across the US over the last few years, the majority of bicyclists are still traveling on roadways shared with motorized vehicles. As a result, bicycles are essentially double exposed to safety risk, due to their interactions with both motorized vehicles and other bicycles. In addition to this double exposure, data challenges–such as a lack of continuous counts and bicycle crash data—complicate the assessment of bicycle safety further. This research presents a bicycle crash analysis framework for estimating bicycle crash rates accounting for both bicycle and motorized vehicle exposure as well as overcoming the lack of bicycle count data. First, a novel seasonal bicycle demand model is presented that is capable of estimating monthly average daily bicyclists (MADB) and annual average daily bicyclists (AADB) using an area-specific calibration factor. This factor can be estimated using a minimum of two short-term counts or one full year of a continuous count. The proposed sinusoidal model has been developed and validated using bicycle count data from a total of 47 permanent bicycle counters in six cities and four bike-sharing systems in North America. Next, a corridor-based crash and AADB assignment is performed to relate crash with volume data. These data are then used in parallel with motorized vehicle counts in a crash rate equation that accounts for exposure of bicyclists to both vehicles and other bicyclists. Results show that the proposed “double exposure” crash rate for bicyclists unveils high risk locations for bicyclists that would have been obscured by the high vehicle volumes if the typical crash rate per AADT or AADB were used. Title: Addressing Data Challenges for Bicycle Crash Analysis Author: Christofa, Eleni Year: 2017 Although an increasing number of separated bicycle facilities have been appearing across the US over the last few years, the majority of bicyclists are still traveling on roadways shared with motorized vehicles. As a result, bicycles are essentially double exposed to safety risk, due to their interactions with both motorized vehicles and other bicycles. In addition to this double exposure, data challenges–such as a lack of continuous counts and bicycle crash data—complicate the assessment of bicycle safety further. This research presents a bicycle crash analysis framework for estimating bicycle crash rates accounting for both bicycle and motorized vehicle exposure as well as overcoming the lack of bicycle count data. First, a novel seasonal bicycle demand model is presented that is capable of estimating monthly average daily bicyclists (MADB) and annual average daily bicyclists (AADB) using an area-specific calibration factor. This factor can be estimated using a minimum of two short-term counts or one full year of a continuous count. The proposed sinusoidal model has been developed and validated using bicycle count data from a total of 47 permanent bicycle counters in six cities and four bike-sharing systems in North America. Next, a corridor-based crash and AADB assignment is performed to relate crash with volume data. These data are then used in parallel with motorized vehicle counts in a crash rate equation that accounts for exposure of bicyclists to both vehicles and other bicyclists. Results show that the proposed “double exposure” crash rate for bicyclists unveils high risk locations for bicyclists that would have been obscured by the high vehicle volumes if the typical crash rate per AADT or AADB were used. Close