{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/44440"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/44440","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Linking travel demand management and emission estimation tools","abstract":"Passage of Clean Air Act Amendments of 1990 have placed greater responsibility on transportation planners, requiring greater integration of transportation and air quality planning processes. Inclusion of Travel Demand Management (TDM) measures into a State Implementation Plan (SIP) or a Transportation Improvement Program (TIP) requires the evaluation of these measures for emission impacts. Traditional method of analysis of a TDM measure using TDM software is labor intensive and time consuming. This process involves the execution of the four step travel demand forecasting models for each emission analysis. This could delay the process of approving a transportation program to be included in the State Implementation Plan. To simplify the process of emission impact analysis of TDMs using TDM software, a link between TDM software and MOBILE5a was developed. In developing the linkage, three parameters were considered crucial. These three parameters were the VMT mix, speed, and operating mode mix. It was determined that the changes in the values of these parameters would have substantial impact on emission factors. Methodologies were formulated to predict the changes in the values of these parameters due to the implementation of TDM measures. To predict the changes in the VMT mix factors, vehicle composition rates were developed by analyzing the 1990 National Personal Transportation Survey (NPTS). Changes in speed and operating mode mix were estimated using the methodologies developed by Sierra Research, Inc. A software model called TDMLinK was developed to link the TDM software and MOBILE5a. The three methodologies developed to predict the changes in the value of parameters were incorporated into this software. TDMLinK reports the percent reductions of emissions for each TDM scenario modeled and for each pollutant. This software extends the ability of TDM software to do screening analysis of TDM strategies.","abstract_html":"Passage of Clean Air Act Amendments of 1990 have placed greater responsibility on transportation planners, requiring greater integration of transportation and air quality planning processes. Inclusion of Travel Demand Management (TDM) measures into a State Implementation Plan (SIP) or a Transportation Improvement Program (TIP) requires the evaluation of these measures for emission impacts. Traditional method of analysis of a TDM measure using TDM software is labor intensive and time consuming. This process involves the execution of the four step travel demand forecasting models for each emission analysis. This could delay the process of approving a transportation program to be included in the State Implementation Plan. To simplify the process of emission impact analysis of TDMs using TDM software, a link between TDM software and MOBILE5a was developed. In developing the linkage, three parameters were considered crucial. These three parameters were the VMT mix, speed, and operating mode mix. It was determined that the changes in the values of these parameters would have substantial impact on emission factors. Methodologies were formulated to predict the changes in the values of these parameters due to the implementation of TDM measures. To predict the changes in the VMT mix factors, vehicle composition rates were developed by analyzing the 1990 National Personal Transportation Survey (NPTS). Changes in speed and operating mode mix were estimated using the methodologies developed by Sierra Research, Inc. A software model called TDMLinK was developed to link the TDM software and MOBILE5a. The three methodologies developed to predict the changes in the value of parameters were incorporated into this software. TDMLinK reports the percent reductions of emissions for each TDM scenario modeled and for each pollutant. This software extends the ability of TDM software to do screening analysis of TDM strategies.","abstract_has_math":false,"creators":["Rudrangi, Prashanth K."],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Civil Engineering","degree_department":"Civil Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1997,"date_issued":"1997","date_published":"1997","updated_at":"2026-07-22T22:19:57Z","subjects":["TDM","MOBILE5a","TDMLinK","emissions","TCM","VMT mix"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-08252008-162417"],"render_values":[{"text":"etd-08252008-162417","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/44440","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Civil Engineering"]},{"key":"dc:creator","label":"Author","values":["Rudrangi, Prashanth K."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T21:43:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T21:43:47Z","2008-08-25"]},{"key":"dc:date.issued","label":"Date","values":["1997"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["TDM","MOBILE5a","TDMLinK","emissions","TCM","VMT mix"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-08252008-162417"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/44440"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Passage of Clean Air Act Amendments of 1990 have placed greater responsibility on transportation planners, requiring greater integration of transportation and air quality planning processes. Inclusion of Travel Demand Management (TDM) measures into a State Implementation Plan (SIP) or a Transportation Improvement Program (TIP) requires the evaluation of these measures for emission impacts. Traditional method of analysis of a TDM measure using TDM software is labor intensive and time consuming. This process involves the execution of the four step travel demand forecasting models for each emission analysis. This could delay the process of approving a transportation program to be included in the State Implementation Plan. To simplify the process of emission impact analysis of TDMs using TDM software, a link between TDM software and MOBILE5a was developed. In developing the linkage, three parameters were considered crucial. These three parameters were the VMT mix, speed, and operating mode mix. It was determined that the changes in the values of these parameters would have substantial impact on emission factors. Methodologies were formulated to predict the changes in the values of these parameters due to the implementation of TDM measures. To predict the changes in the VMT mix factors, vehicle composition rates were developed by analyzing the 1990 National Personal Transportation Survey (NPTS). Changes in speed and operating mode mix were estimated using the methodologies developed by Sierra Research, Inc. A software model called TDMLinK was developed to link the TDM software and MOBILE5a. The three methodologies developed to predict the changes in the value of parameters were incorporated into this software. TDMLinK reports the percent reductions of emissions for each TDM scenario modeled and for each pollutant. This software extends the ability of TDM software to do screening analysis of TDM strategies."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["BTD"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Linking travel demand management and emission estimation tools"]}]}],"canonical_facts":{"dc:contributor.department":["Civil Engineering"],"dc:creator":["Rudrangi, Prashanth K."],"dc:date.accessioned":["2014-03-14T21:43:47Z"],"dc:date.available":["2014-03-14T21:43:47Z","2008-08-25"],"dc:date.issued":["1997"],"dc:description.abstract":["Passage of Clean Air Act Amendments of 1990 have placed greater responsibility on transportation planners, requiring greater integration of transportation and air quality planning processes. Inclusion of Travel Demand Management (TDM) measures into a State Implementation Plan (SIP) or a Transportation Improvement Program (TIP) requires the evaluation of these measures for emission impacts. Traditional method of analysis of a TDM measure using TDM software is labor intensive and time consuming. This process involves the execution of the four step travel demand forecasting models for each emission analysis. This could delay the process of approving a transportation program to be included in the State Implementation Plan. To simplify the process of emission impact analysis of TDMs using TDM software, a link between TDM software and MOBILE5a was developed. In developing the linkage, three parameters were considered crucial. These three parameters were the VMT mix, speed, and operating mode mix. It was determined that the changes in the values of these parameters would have substantial impact on emission factors. Methodologies were formulated to predict the changes in the values of these parameters due to the implementation of TDM measures. To predict the changes in the VMT mix factors, vehicle composition rates were developed by analyzing the 1990 National Personal Transportation Survey (NPTS). Changes in speed and operating mode mix were estimated using the methodologies developed by Sierra Research, Inc. A software model called TDMLinK was developed to link the TDM software and MOBILE5a. The three methodologies developed to predict the changes in the value of parameters were incorporated into this software. TDMLinK reports the percent reductions of emissions for each TDM scenario modeled and for each pollutant. 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