The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Zakia Wadud - One of the best experts on this subject based on the ideXlab platform.
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Imperfect reversibility of air Transport Demand: Effects of air fare, fuel prices and price transmission
Transportation Research Part A: Policy and Practice, 2015Co-Authors: Zakia WadudAbstract:There are recent evidence that air Transport Demand may not have a perfectly reversible relationship with income and jet fuel prices, as is assumed in most Demand models. However, it is not known if the imperfectly reversible effects of jet fuel price are a result of asymmetries in the supply side, i.e., asymmetries in cost pass through from fuel prices to air fare, or of Demand side behavioral asymmetries whereby people value gains and losses differently. This paper uses US time series data and decomposes air fare and fuel price into three component series to develop an econometric model of air Transport Demand that is capable of capturing the potential imperfectly reversible relationships and test for the presence or absence of reversibility. We find that air Transport Demand shows asymmetry with respect to air fare, indicating potential imperfect reversibility in consumer behavior. We also find evidence of asymmetry and hysteresis in cost pass-through from jet fuel prices to air fare, showing rapid increases in airfare when fuel prices increases but a slower response in the opposite direction.
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The asymmetric effects of income and fuel price on air Transport Demand
Transportation Research Part A-policy and Practice, 2014Co-Authors: Zakia WadudAbstract:Forecasts of passenger Demand are an important parameter for aviation planners. Air Transport Demand models typically assume a perfectly reversible impact of the Demand drivers. However, there are reasons to believe that the impacts of some of the Demand drivers such as fuel price or income on air Transport Demand may not be perfectly reversible. Two types of imperfect reversibility, namely asymmetry and hysteresis, are possible. Asymmetry refers to the differences in the Demand impacts of a rising price or income from that of a falling price or income. Hysteresis refers to the dependence of the impacts of changing price or income on previous history, especially on previous maximum price or income. We use US time series data and decompose each of fuel price and income into three component series to develop an econometric model for air Transport Demand that is capable of capturing the potential imperfectly reversible relationships and test for the presence or absence of reversibility. We find statistical evidence of asymmetry and hysteresis – for both, prices and income – in air Transport Demand. Implications for policy and practice are then discussed.
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The asymmetric effects of income and fuel price on air Transport Demand
Transportation Research Part A: Policy and Practice, 2014Co-Authors: Zakia WadudAbstract:Forecasts of passenger Demand are an important parameter for aviation planners. Air Transport Demand models typically assume a perfectly reversible impact of the Demand drivers. However, there are reasons to believe that the impacts of some of the Demand drivers such as fuel price or income on air Transport Demand may not be perfectly reversible. Two types of imperfect reversibility, namely asymmetry and hysteresis, are possible. Asymmetry refers to the differences in the Demand impacts of a rising price or income from that of a falling price or income. Hysteresis refers to the dependence of the impacts of changing price or income on previous history, especially on previous maximum price or income. We use US time series data and decompose each of fuel price and income into three component series to develop an econometric model for air Transport Demand that is capable of capturing the potential imperfectly reversible relationships and test for the presence or absence of reversibility. We find statistical evidence of asymmetry and hysteresis - for both, prices and income - in air Transport Demand. Implications for policy and practice are then discussed. © 2014 Elsevier Ltd.
Roger Fouquet - One of the best experts on this subject based on the ideXlab platform.
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trends in income and price elasticities of Transport Demand 1850 2010
Energy Policy, 2012Co-Authors: Roger FouquetAbstract:The purpose of this paper is to estimate trends in income and price elasticities and to offer insights for the future growth in Transport use, with particular emphasis on the impact of energy and technological transitions. The results indicate that income and price elasticities of passenger Transport Demand in the United Kingdom were very large (3.1 and -1.5, respectively) in the mid-nineteenth century, and declined since then. In 2010, long run income and price elasticity of aggregate land Transport Demand were estimated to be 0.8 and -0.6. These trends suggest that future elasticities related to Transport Demand in developed economies may decline very gradually and, in developing economies, where elasticities are often larger, they will probably decline more rapidly as the economies develop. Because of the declining trends in elasticities, future energy and technological transitions are not likely to generate the growth rates in energy consumption that occurred following transitions in the nineteenth century. Nevertheless, energy and technological transitions, such as the car and the airplane, appear to have delayed and probably will delay declining trends in income and price elasticity of aggregate land Transport Demand.
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Trends in income and price elasticities of Transport Demand (1850–2010)
Energy Policy, 2012Co-Authors: Roger FouquetAbstract:The purpose of this paper is to estimate trends in income and price elasticities and to offer insights for the future growth in Transport use, with particular emphasis on the impact of energy and technological transitions. The results indicate that income and price elasticities of passenger Transport Demand in the United Kingdom were very large (3.1 and -1.5, respectively) in the mid-nineteenth century, and declined since then. In 2010, long run income and price elasticity of aggregate land Transport Demand were estimated to be 0.8 and -0.6. These trends suggest that future elasticities related to Transport Demand in developed economies may decline very gradually and, in developing economies, where elasticities are often larger, they will probably decline more rapidly as the economies develop. Because of the declining trends in elasticities, future energy and technological transitions are not likely to generate the growth rates in energy consumption that occurred following transitions in the nineteenth century. Nevertheless, energy and technological transitions, such as the car and the airplane, appear to have delayed and probably will delay declining trends in income and price elasticity of aggregate land Transport Demand.
Geoffrey Clifton - One of the best experts on this subject based on the ideXlab platform.
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Examining Local Interaction Between Public Transport Demand and Land Use Characteristics Using Geographically Weighted Regression
2020Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A Geographically Weighted Regression approach is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a Travel Zone level. The global model of Geographically Weighted Regression suggests that increasing land use density and walkability as well as providing a better accessibility to the Sydney Central Business District have positive impacts on public Transport Demand. The local model of Geographically Weighted Regression shows that the impacts of the land use characteristics on public Transport Demand distinctively vary spatially, and the estimated parameters may have different signs in some areas as compared to the global model. This paper highlights the way in which the relationship between travel Demand and land use is heterogenous over geographical space which cannot be captured by conventional multivariate regression models.
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forecasting public Transport Demand for the sydney greater metropolitan area a comparison of univariate and multivariate methods
Transport Research Forum, 2014Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:Public Transport Demand forecasting is important for urban public Transport planning. In the Sydney Greater Metropolitan Area (SGMA), bus and train as the two major public Transport systems account for around two million trips per day. Understanding future changes in public Transport Demand in response to different policy scenarios gives important information for Transport policy formulation. This paper reports forecasts of public Transport Demand in the SGMA using an Autoregressive Integrated Moving Average (ARIMA) model and a dynamic Partial Adjustment Model (PAM). The ARIMA model is estimated using monthly train and bus boarding data from 2007 to 2011. The PAM model estimates Demand elasticities with respect to each of a number of public Transport determinants, including the public Transport fare, the socio-demographics of public Transport users, the level of public Transport service and land use characteristics. The PAM model is estimated using a pseudo panel dataset constructed from the Sydney Household Travel Survey from 1997 to 2009. The forecast accuracy of the two methods is compared to the actual Demand observed in 2010 and 2011, using a holdout sample. The PAM model is then used for forecasting future public Transport Demand for the SGMA, for a number of policy scenarios. The forecasting results suggest that the ARIMA model can achieve better prediction accuracy in the short term, whereas a PAM model is preferred if the objective is to forecast future Demand in response to various policy scenarios.
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The spatial interactions between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area
2012Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A Geographically Weighted Regression approach, consisting of a global model and a local model is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a Travel Zone level. The global model of Geographically Weighted Regression identifies that public Transport Demand in Sydney is significantly higher in Travel Zones with higher land use density, better walking environment, and better accessibility to the Sydney Central Business District. The local model of Geographically Weighted Regression shows that the impacts of the land use characteristics on public Transport Demand vary spatially with local estimated parameters having signs opposite to those of the global model in some areas. This paper highlights the way in which the relationship between public Transport Demand and land use is heterogenous over geographical space which cannot be captured by conventional multiple regression models.
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The spatial interactions between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area
2012Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A geographically weighted regression approach is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a travel zone level. The global model of geographically weighted regression suggests that increasing land use density and walkability as well as providing a better accessibility to the Sydney central business district have positive impacts on public Transport Demand. The local model of geographically weighted regression shows that the impacts of the land use characteristics on public Transport Demand distinctively vary spatially, and the estimated parameters may have different signs in some areas as compared to the global model. This paper highlights the way in which the relationship between travel Demand and land use is heterogeneous over geographical space which cannot be captured by conventional multivariate regression models.
Chi-hong Tsai - One of the best experts on this subject based on the ideXlab platform.
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Examining Local Interaction Between Public Transport Demand and Land Use Characteristics Using Geographically Weighted Regression
2020Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A Geographically Weighted Regression approach is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a Travel Zone level. The global model of Geographically Weighted Regression suggests that increasing land use density and walkability as well as providing a better accessibility to the Sydney Central Business District have positive impacts on public Transport Demand. The local model of Geographically Weighted Regression shows that the impacts of the land use characteristics on public Transport Demand distinctively vary spatially, and the estimated parameters may have different signs in some areas as compared to the global model. This paper highlights the way in which the relationship between travel Demand and land use is heterogenous over geographical space which cannot be captured by conventional multivariate regression models.
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Identifying Short-Run and Long-Run Public Transport Demand Elasticities in Sydney A Pseudo Panel Approach
Journal of Transport Economics and Policy, 2014Co-Authors: Chi-hong Tsai, Corinne MulleyAbstract:This paper applies a pseudo panel approach to analyse public Transport Demand and estimate short-run and long-run Demand elasticities in Sydney. A dynamic Partial Adjustment Model is employed to capture the lagged adjustments of public Transport users' travel behaviour, which differentiate long-run Demand from short-run Demand. The public Transport Demand model incorporates different drivers of Demand determinants, including public Transport price, the socio-economics of travellers, land use characteristics, and the level of public Transport service. The impacts on public Transport Demand are presented in terms of short-run and long-run Demand elasticities. © 2014 LSE and the University of Bath
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forecasting public Transport Demand for the sydney greater metropolitan area a comparison of univariate and multivariate methods
Transport Research Forum, 2014Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:Public Transport Demand forecasting is important for urban public Transport planning. In the Sydney Greater Metropolitan Area (SGMA), bus and train as the two major public Transport systems account for around two million trips per day. Understanding future changes in public Transport Demand in response to different policy scenarios gives important information for Transport policy formulation. This paper reports forecasts of public Transport Demand in the SGMA using an Autoregressive Integrated Moving Average (ARIMA) model and a dynamic Partial Adjustment Model (PAM). The ARIMA model is estimated using monthly train and bus boarding data from 2007 to 2011. The PAM model estimates Demand elasticities with respect to each of a number of public Transport determinants, including the public Transport fare, the socio-demographics of public Transport users, the level of public Transport service and land use characteristics. The PAM model is estimated using a pseudo panel dataset constructed from the Sydney Household Travel Survey from 1997 to 2009. The forecast accuracy of the two methods is compared to the actual Demand observed in 2010 and 2011, using a holdout sample. The PAM model is then used for forecasting future public Transport Demand for the SGMA, for a number of policy scenarios. The forecasting results suggest that the ARIMA model can achieve better prediction accuracy in the short term, whereas a PAM model is preferred if the objective is to forecast future Demand in response to various policy scenarios.
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The spatial interactions between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area
2012Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A Geographically Weighted Regression approach, consisting of a global model and a local model is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a Travel Zone level. The global model of Geographically Weighted Regression identifies that public Transport Demand in Sydney is significantly higher in Travel Zones with higher land use density, better walking environment, and better accessibility to the Sydney Central Business District. The local model of Geographically Weighted Regression shows that the impacts of the land use characteristics on public Transport Demand vary spatially with local estimated parameters having signs opposite to those of the global model in some areas. This paper highlights the way in which the relationship between public Transport Demand and land use is heterogenous over geographical space which cannot be captured by conventional multiple regression models.
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The spatial interactions between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area
2012Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A geographically weighted regression approach is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a travel zone level. The global model of geographically weighted regression suggests that increasing land use density and walkability as well as providing a better accessibility to the Sydney central business district have positive impacts on public Transport Demand. The local model of geographically weighted regression shows that the impacts of the land use characteristics on public Transport Demand distinctively vary spatially, and the estimated parameters may have different signs in some areas as compared to the global model. This paper highlights the way in which the relationship between travel Demand and land use is heterogeneous over geographical space which cannot be captured by conventional multivariate regression models.
Corinne Mulley - One of the best experts on this subject based on the ideXlab platform.
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Examining Local Interaction Between Public Transport Demand and Land Use Characteristics Using Geographically Weighted Regression
2020Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A Geographically Weighted Regression approach is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a Travel Zone level. The global model of Geographically Weighted Regression suggests that increasing land use density and walkability as well as providing a better accessibility to the Sydney Central Business District have positive impacts on public Transport Demand. The local model of Geographically Weighted Regression shows that the impacts of the land use characteristics on public Transport Demand distinctively vary spatially, and the estimated parameters may have different signs in some areas as compared to the global model. This paper highlights the way in which the relationship between travel Demand and land use is heterogenous over geographical space which cannot be captured by conventional multivariate regression models.
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Identifying Short-Run and Long-Run Public Transport Demand Elasticities in Sydney A Pseudo Panel Approach
Journal of Transport Economics and Policy, 2014Co-Authors: Chi-hong Tsai, Corinne MulleyAbstract:This paper applies a pseudo panel approach to analyse public Transport Demand and estimate short-run and long-run Demand elasticities in Sydney. A dynamic Partial Adjustment Model is employed to capture the lagged adjustments of public Transport users' travel behaviour, which differentiate long-run Demand from short-run Demand. The public Transport Demand model incorporates different drivers of Demand determinants, including public Transport price, the socio-economics of travellers, land use characteristics, and the level of public Transport service. The impacts on public Transport Demand are presented in terms of short-run and long-run Demand elasticities. © 2014 LSE and the University of Bath
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forecasting public Transport Demand for the sydney greater metropolitan area a comparison of univariate and multivariate methods
Transport Research Forum, 2014Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:Public Transport Demand forecasting is important for urban public Transport planning. In the Sydney Greater Metropolitan Area (SGMA), bus and train as the two major public Transport systems account for around two million trips per day. Understanding future changes in public Transport Demand in response to different policy scenarios gives important information for Transport policy formulation. This paper reports forecasts of public Transport Demand in the SGMA using an Autoregressive Integrated Moving Average (ARIMA) model and a dynamic Partial Adjustment Model (PAM). The ARIMA model is estimated using monthly train and bus boarding data from 2007 to 2011. The PAM model estimates Demand elasticities with respect to each of a number of public Transport determinants, including the public Transport fare, the socio-demographics of public Transport users, the level of public Transport service and land use characteristics. The PAM model is estimated using a pseudo panel dataset constructed from the Sydney Household Travel Survey from 1997 to 2009. The forecast accuracy of the two methods is compared to the actual Demand observed in 2010 and 2011, using a holdout sample. The PAM model is then used for forecasting future public Transport Demand for the SGMA, for a number of policy scenarios. The forecasting results suggest that the ARIMA model can achieve better prediction accuracy in the short term, whereas a PAM model is preferred if the objective is to forecast future Demand in response to various policy scenarios.
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The spatial interactions between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area
2012Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A Geographically Weighted Regression approach, consisting of a global model and a local model is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a Travel Zone level. The global model of Geographically Weighted Regression identifies that public Transport Demand in Sydney is significantly higher in Travel Zones with higher land use density, better walking environment, and better accessibility to the Sydney Central Business District. The local model of Geographically Weighted Regression shows that the impacts of the land use characteristics on public Transport Demand vary spatially with local estimated parameters having signs opposite to those of the global model in some areas. This paper highlights the way in which the relationship between public Transport Demand and land use is heterogenous over geographical space which cannot be captured by conventional multiple regression models.
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The spatial interactions between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area
2012Co-Authors: Chi-hong Tsai, Corinne Mulley, Geoffrey CliftonAbstract:This paper presents a public Transport Demand model incorporating land use density, diversity, design, and accessibility to examine the relationship between public Transport Demand and land use characteristics in the Sydney Greater Metropolitan Area. A geographically weighted regression approach is employed to identify the spatial variation of the land use variables and their impacts on public Transport Demand at a travel zone level. The global model of geographically weighted regression suggests that increasing land use density and walkability as well as providing a better accessibility to the Sydney central business district have positive impacts on public Transport Demand. The local model of geographically weighted regression shows that the impacts of the land use characteristics on public Transport Demand distinctively vary spatially, and the estimated parameters may have different signs in some areas as compared to the global model. This paper highlights the way in which the relationship between travel Demand and land use is heterogeneous over geographical space which cannot be captured by conventional multivariate regression models.
