National Academies Press: OpenBook

Guide to Truck Activity Data for Emissions Modeling (2019)

Chapter: Appendices A Through G

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Page 65
Suggested Citation:"Appendices A Through G." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
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Page 65
Page 66
Suggested Citation:"Appendices A Through G." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
×
Page 66
Page 67
Suggested Citation:"Appendices A Through G." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
×
Page 67
Page 68
Suggested Citation:"Appendices A Through G." National Academies of Sciences, Engineering, and Medicine. 2019. Guide to Truck Activity Data for Emissions Modeling. Washington, DC: The National Academies Press. doi: 10.17226/25484.
×
Page 68

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

65 Appendices A through G of this guide are published as NCHRP Web-Only Document 261: Case Studies of Truck Activity Data for Emissions Modeling, available on the TRB website at http:// www.trb.org/Main/Blurbs/178906.aspx. Appendices A Through G

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TRB’s National Cooperative Highway Research Program (NCHRP) Research Report 909: Guide to Truck Activity Data for Emissions Modeling explores methods, procedures, and data sets needed to capture commercial vehicle activity, vehicle characteristics, and operations to assist in estimating and forecasting criteria pollutants, air toxics, and greenhouse gas emissions from goods and services movement.

Goods movement is a vital part of the national economy, with freight movement growing faster than passenger travel. The growth in freight traffic is contributing to urban congestion, resulting in hours of delay, increased shipping costs, wasted fuel, and greater emissions of greenhouse gas and criteria pollutants. The limited national data on urban goods movement are insufficient for a thorough understanding of the characteristics of the trucks operating in metropolitan areas and the complex logistical chains that they serve.

For instance, there are at least three different segments of urban freight—long haul, drayage, and pickup and delivery. It is believed that truck fleet characteristics differ between the segments, but only local registration data exist at a level of detail needed to support regional transportation plans, transportation improvement plans, and state implementation plans. The lack of data on all types of commercial trucks affects model estimation and results in inaccurate base year emissions inventories, limiting the ability to design and implement effective policies to reduce freight-related emissions.

NCHRP Research Report 909 enumerates various sources of truck data and how they can be obtained and used to support emissions modeling.

NCHRP Web-Only Document 210: Input Guidelines for Motor Vehicle Emissions Simulator Model (Porter et al., 2014a, 2014b, 2014c) provides guidance on developing local inputs to the MOVES mode. It covers all vehicle types, but is not specific to trucks. NCHRP Research Report 909 supplements NCHRP Web-Only Document 210 by describing the use of various data sources to obtain truck-specific inputs.

Appendices A through G to NCHRP Research Report 909 are published as NCHRP Web-Only Document 261 and contain seven case studies that serve as the basis for much of the guidance provided in NCHRP Research Report 909.

NCHRP Research Report 909 is also supplemented by three MS Excel files that contain data from the case studies:

Case Studies #1 and #7

Case Study #2

Case Studies #3, #4, and #6

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