The Experts below are selected from a list of 24690 Experts worldwide ranked by ideXlab platform
Raymond Chan - One of the best experts on this subject based on the ideXlab platform.
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Stretching resources: sensitivity of optimal bus frequency allocation to stop-level Demand Elasticities
Public Transport, 2014Co-Authors: Ömer Verbas, Charlotte Frei, Hani S. Mahmassani, Raymond ChanAbstract:Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where Elasticities are used in frequency allocation, a single across the board value or two respective values for peak and off-peak are used for the entire set of routes and stops throughout the day. With growing availability of ridership data, estimation of spatially and temporally disaggregated Elasticities is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of Elasticities cor- responding to varying levels of disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a transit network frequency setting framework: (1) temporal Elasticities based on time of day, (2) spatial Elasticities via grouping stops into Demand, supply and land use classes and (3) spatio-temporal Elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to Elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer. It is also important to capture longer-term effects—over a year or more—because seasonal activity patterns may bias elasticity estimates over shorter time horizons.
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stretching resources sensitivity of optimal bus frequency allocation to stop level Demand Elasticities
Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013Co-Authors: Ömer Verbas, Charlotte Frei, Hani S. Mahmassani, Raymond ChanAbstract:Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where Elasticities are used in frequency allocation, a single across the board value is typically used for all routes and all times of the day. The most advanced applications might use two values, for peak and off-peak respectively. With growing availability of ridership data from many sources, estimation of spatially and temporally disaggregated Elasticities of Demand with respect to service frequency is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of Elasticities corresponding to varying levels of spatial and temporal disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a Transit Network Frequency Setting framework: (1) temporal Elasticities based on time of day, (2) spatial Elasticities via grouping stops into Demand, supply and land use classes and (3) spatio-temporal Elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to Elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer to other modes. It is also important to capture longer term effects—over a year or more—in these models because seasonal activity patterns (e.g. school trips, vacation) may bias elasticity estimates over shorter time horizons. The experiments demonstrate that spatial detail in ridership elasticity estimation results in meaningful improvements in an objective function minimizing wait time and maximizing ridership, even when time periods are aggregated. Since much of this data is available at census tract level and collected by regional planning authorities, transit agencies could implement this frequency allocation formulation using rather coarse data.
Marcelo Olarreaga - One of the best experts on this subject based on the ideXlab platform.
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Import Demand Elasticities and Trade Distortions
Review of Economics and Statistics, 2008Co-Authors: Hiau Looi Kee, Alessandro Nicita, Marcelo OlarreagaAbstract:This paper provides a systematic estimation of import Demand Elasticities for a broad group of countries at a very disaggregated level of product detail. We use a semiflexible translog GDP function approach to formally derive import Demands and their Elasticities, which are estimated with data on prices and endowments. Within a theoretically consistent framework, we use the estimated Elasticities to construct Feenstra's (1995) simplification of Anderson and Neary's trade restrictiveness index (TRI). The difference between TRIs and import-weighted tariffs is shown to depend on the tariff variance and the covariance between tariffs and import Demand Elasticities. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
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import Demand Elasticities and trade distortions
The Review of Economics and Statistics, 2004Co-Authors: Hiau Looi Kee, Alessandro Nicita, Marcelo OlarreagaAbstract:To study the effects of tariffs on gross domestic product (GDP), one needs import Demand Elasticities at the tariff line level that are consistent with GDP maximization. These do not exist. The authors modify Kohli's (1991) GDP function approach to estimate Demand Elasticities for 4,625 imported goods in 117 countries. Following Anderson and Neary (1992, 1994) and Feenstra (1995), they use these estimates to construct theoretically sound trade restrictiveness indices, and GDP losses associated with existing tariff structures. Countries are revealed to be 30 percent more restrictive than their simple or import-weighted average tariffs would suggest. Thus, distortion is nontrivial. GDP losses are largest in China, Germany, India, Mexico, and the United States.
Ömer Verbas - One of the best experts on this subject based on the ideXlab platform.
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Stretching resources: sensitivity of optimal bus frequency allocation to stop-level Demand Elasticities
Public Transport, 2014Co-Authors: Ömer Verbas, Charlotte Frei, Hani S. Mahmassani, Raymond ChanAbstract:Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where Elasticities are used in frequency allocation, a single across the board value or two respective values for peak and off-peak are used for the entire set of routes and stops throughout the day. With growing availability of ridership data, estimation of spatially and temporally disaggregated Elasticities is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of Elasticities cor- responding to varying levels of disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a transit network frequency setting framework: (1) temporal Elasticities based on time of day, (2) spatial Elasticities via grouping stops into Demand, supply and land use classes and (3) spatio-temporal Elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to Elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer. It is also important to capture longer-term effects—over a year or more—because seasonal activity patterns may bias elasticity estimates over shorter time horizons.
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stretching resources sensitivity of optimal bus frequency allocation to stop level Demand Elasticities
Transportation Research Board 92nd Annual MeetingTransportation Research Board, 2013Co-Authors: Ömer Verbas, Charlotte Frei, Hani S. Mahmassani, Raymond ChanAbstract:Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where Elasticities are used in frequency allocation, a single across the board value is typically used for all routes and all times of the day. The most advanced applications might use two values, for peak and off-peak respectively. With growing availability of ridership data from many sources, estimation of spatially and temporally disaggregated Elasticities of Demand with respect to service frequency is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of Elasticities corresponding to varying levels of spatial and temporal disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a Transit Network Frequency Setting framework: (1) temporal Elasticities based on time of day, (2) spatial Elasticities via grouping stops into Demand, supply and land use classes and (3) spatio-temporal Elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to Elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer to other modes. It is also important to capture longer term effects—over a year or more—in these models because seasonal activity patterns (e.g. school trips, vacation) may bias elasticity estimates over shorter time horizons. The experiments demonstrate that spatial detail in ridership elasticity estimation results in meaningful improvements in an objective function minimizing wait time and maximizing ridership, even when time periods are aggregated. Since much of this data is available at census tract level and collected by regional planning authorities, transit agencies could implement this frequency allocation formulation using rather coarse data.
Amer Shalaby - One of the best experts on this subject based on the ideXlab platform.
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empirical analysis of long run Elasticities and asymmetric effects of transit Demand determinants
Transportation Research Record, 2020Co-Authors: Dena Kasraian, Amer ShalabyAbstract:The effects of transit ridership determinants can be quantified as Demand Elasticities which are often used to inform transit planning and policy making. This study seeks to determine the impacts o...
Hiau Looi Kee - One of the best experts on this subject based on the ideXlab platform.
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Import Demand Elasticities and Trade Distortions
Review of Economics and Statistics, 2008Co-Authors: Hiau Looi Kee, Alessandro Nicita, Marcelo OlarreagaAbstract:This paper provides a systematic estimation of import Demand Elasticities for a broad group of countries at a very disaggregated level of product detail. We use a semiflexible translog GDP function approach to formally derive import Demands and their Elasticities, which are estimated with data on prices and endowments. Within a theoretically consistent framework, we use the estimated Elasticities to construct Feenstra's (1995) simplification of Anderson and Neary's trade restrictiveness index (TRI). The difference between TRIs and import-weighted tariffs is shown to depend on the tariff variance and the covariance between tariffs and import Demand Elasticities. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
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import Demand Elasticities and trade distortions
The Review of Economics and Statistics, 2004Co-Authors: Hiau Looi Kee, Alessandro Nicita, Marcelo OlarreagaAbstract:To study the effects of tariffs on gross domestic product (GDP), one needs import Demand Elasticities at the tariff line level that are consistent with GDP maximization. These do not exist. The authors modify Kohli's (1991) GDP function approach to estimate Demand Elasticities for 4,625 imported goods in 117 countries. Following Anderson and Neary (1992, 1994) and Feenstra (1995), they use these estimates to construct theoretically sound trade restrictiveness indices, and GDP losses associated with existing tariff structures. Countries are revealed to be 30 percent more restrictive than their simple or import-weighted average tariffs would suggest. Thus, distortion is nontrivial. GDP losses are largest in China, Germany, India, Mexico, and the United States.