Rapid economic development exerts pressure on all infrastructure services vital for economic efficiency and social sustainability, particularly transport infrastructure. In India, sustaining this increase in economic productivity is contingent on meeting the mobility demand that such economic growth creates, and hence on optimally utilizing existing infrastructure (Justus (1998); Gowda (1999)). In addition, transport accounts for a substantial and growing proportion of air pollution in Indian cities, contributes significantly to greenhouse gas emissions, and is a major consumer of energy (Ramanathan and Parikh (1999)). Transport is also the largest contributor to noise pollution, and has substantial safety and waste management concerns (Singh (2000)). Finally, access to transport services is considered critical for addressing equity concerns by facilitating access to primary education and employment generation facilities. Transport infrastructure is also important for integrating rural communities in the socioeconomic structure of the nation.
This calls for a greater share of public transport in meeting mobility needs. Not only is an efficient public bus system important for meeting the mobility needs in this rapidly growing economy, but a higher share of bus transport would also reduce pollution, both local and global, and energy demand. Hence, it is incumbent on governments in developing countries to institute appropriate policy initiatives to increase the share of public transport. Such interventions must be informed by research that identifies factors influencing the demand for public transport. The most common method for characterizing the influence of such variables is by estimating the elasticity of demand with respect to each of these variables. To this end, this paper estimates the elasticity of demand at the state level with respect to price, income, and service quality. All states with public bus transport in India are included in this study.
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There are two major types of empirical transit demand studies, namely, those derived from the Random Utility Theory that analyze the choice of a transport mode (Winston (1983); Oum (1989)), and those derived from consumer utility maximization that analyze continuous consumption patterns. Demand analysis in the case of a continuous variable, in turn, follows one of two approaches. The first approach estimates a system of equations simultaneously for several commodities or commodity groups. The second focuses only on one commodity, or a commodity group, and hence essentially estimates the demand in a single market. In either case, with a complete systems approach that is theoretically more consistent, a more comprehensive dataset is required that includes demand for, or expenditures on, all commodity groups. In the absence of such an extensive dataset, equations are specified in a more ad hoc manner including cross–commodity influences from only close substitutes and complements (Thomas (1987)).
This research uses an unbalanced aggregate panel dataset between 1990/91 and 2000/01 for 22 large states in India to assess the price and income effects on public bus transport demand. Here, direct price elasticities can be obtained after estimating an ad hoc aggregate single equation demand model. The current research is possibly one of few studies that use panel data for the estimation of the Indian bus transport demand.
The paper is organized as follows. The next section briefly describes the public road transport sector in India. Section 3.0 discusses the relevant literature on number, timing, and spatial distribution of trips by mode in estimating travel demand, all of which are infinitely faceted and hence can result in a large variety of alternatives for each consumer, making travel demand modeling complex (Jovicic and Hansen (2003)). The specification used in this research is given in section 4.0. The estimation process and the data used are given in section 5.0. Section 6.0 presents the results of the analysis and discusses the implications therein, and finally, section 7.0 concludes.
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Rapid economic development exerts pressure on all infrastructure services vital for economic efficiency and social sustainability, particularly transport infrastructure. In India, sustaining this increase in economic productivity is contingent on meeting the mobility demand that such economic growth creates, and hence on optimally utilizing existing infrastructure (Justus (1998); Gowda (1999)). In addition, transport accounts for a substantial and growing proportion of air pollution in Indian cities, contributes significantly to greenhouse gas emissions, and is a major consumer of energy (Ramanathan and Parikh (1999)). Transport is also the largest contributor to noise pollution, and has substantial safety and waste management concerns (Singh (2000)). Finally, access to transport services is considered critical for addressing equity concerns by facilitating access to primary education and employment generation facilities. Transport infrastructure is also important for integrating rural communities in the socioeconomic structure of the nation.
This calls for a greater share of public transport in meeting mobility needs. Not only is an efficient public bus system important for meeting the mobility needs in this rapidly growing economy, but a higher share of bus transport would also reduce pollution, both local and global, and energy demand. Hence, it is incumbent on governments in developing countries to institute appropriate policy initiatives to increase the share of public transport. Such interventions must be informed by research that identifies factors influencing the demand for public transport. The most common method for characterizing the influence of such variables is by estimating the elasticity of demand with respect to each of these variables. To this end, this paper estimates the elasticity of demand at the state level with respect to price, income, and service quality. All states with public bus transport in India are included in this study.
3
There are two major types of empirical transit demand studies, namely, those derived from the Random Utility Theory that analyze the choice of a transport mode (Winston (1983); Oum (1989)), and those derived from consumer utility maximization that analyze continuous consumption patterns. Demand analysis in the case of a continuous variable, in turn, follows one of two approaches. The first approach estimates a system of equations simultaneously for several commodities or commodity groups. The second focuses only on one commodity, or a commodity group, and hence essentially estimates the demand in a single market. In either case, with a complete systems approach that is theoretically more consistent, a more comprehensive dataset is required that includes demand for, or expenditures on, all commodity groups. In the absence of such an extensive dataset, equations are specified in a more ad hoc manner including cross–commodity influences from only close substitutes and complements (Thomas (1987)).
This research uses an unbalanced aggregate panel dataset between 1990/91 and 2000/01 for 22 large states in India to assess the price and income effects on public bus transport demand. Here, direct price elasticities can be obtained after estimating an ad hoc aggregate single equation demand model. The current research is possibly one of few studies that use panel data for the estimation of the Indian bus transport demand.
The paper is organized as follows. The next section briefly describes the public road transport sector in India. Section 3.0 discusses the relevant literature on number, timing, and spatial distribution of trips by mode in estimating travel demand, all of which are infinitely faceted and hence can result in a large variety of alternatives for each consumer, making travel demand modeling complex (Jovicic and Hansen (2003)). The specification used in this research is given in section 4.0. The estimation process and the data used are given in section 5.0. Section 6.0 presents the results of the analysis and discusses the implications therein, and finally, section 7.0 concludes.
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