Urban Transit Technology Selection for Many-to-Many Travel Demand Using Social Welfare Optimization Approach

《Urban Transit Technology Selection for Many-to-Many Travel Demand Using Social Welfare Optimization Approach》

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作者单位: [Verma, Ashish; Raturi, Varun; Kanimozhee, S.] Indian Inst Sci Bangalore, Dept Civil Engn, Bangalore 560012, Karnataka, India. [Verma, Ashish] Indian Inst Sci Bangalore, Ctr Infrastruct Sustainable Transportat & Urban P, Bangalore 560012, Karnataka, India. Verma, A (reprint author), Indian Inst Sci Bangalore, Dept Civil Engn, Bangalore 560012, Karnataka, India.; Verma, A (reprint author), Indian Inst Sci Bangalore, Ctr Infrastruct Sustainable Transportat & Urban P, Bangalore 560012, Karnataka, India. E-Mail: ashishv@civil.iisc.ernet.in; raturi.varun@gmail.com; kanimozhee@gmail.com

摘要: The introduction of mass transit technologies is increasingly viewed as a feasible solution to mitigate urban traffic congestion in fast growing Indian cities. The type of transit technology selection for a particular city is dilemmatic as it depends on various factors like the population density and growth rate of the city, travel behavior of passengers, existing transit service characteristics, etc. This study attempts to provide a scientific tool for such decisions by developing a mathematical model to determine a feasible transit technology [Bus Rapid Transit (BRT) or Metro] for many-to-many travel demand for a city. A social welfare function is formulated and is optimized with respect to station spacing, headway, and fare for different levels of population densities and the transit technology. By comparing the current population density to the critical population density (population density at which the social welfare is equal for both BRT and Metro transit technology), the model estimates the time step for the city to introduce the transit in the future. By taking population density as the input, it can be observed that for low-population-density cities, the social welfare of the BRT technology is higher than the Metro and the optimal number of stations for the transit technologies will decrease with an increase in population density.