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Analysis of interaction among land use, transportation network and air pollution using stochastic nonlinear programming

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Abstract

This paper presents two novel models for land use and transportation to address the development of different functional zones in urban areas by considering the design of an efficient transportation network and reducing air pollution. Objective functions of the first model are maximizing utility function and maximizing reliability index. The utility is formulated as a function of travel cost and zonal attractiveness. Reliability index is defined as the probability that flow in each link of the network is less than the design capacity. Maximizing this probability is equivalent to minimizing congestion in the network. In addition, maximizing utility and minimizing carbon monoxide emission in the network are considered as objective functions in the second model. The formulated models are nonlinear and stochastic. We implement the ε-constraint method for solving these bi-objective optimization problems. We analyze the models and solution characteristics of some examples. In addition, we evaluate the relation between computing time and complexity of the model. In this study, for the first time in the open literature, stochastic bi-objective optimization models are formulated to analyze interaction among land use, transportation network and air pollution. We also extract and summarize some useful insights on the relationship among land use, transportation network and environmental impact associated with them.

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Acknowledgments

Financial Support for this work from the European Commission LOG4GREEN Project Grant #287091 under FP7 program is gratefully acknowledged.

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  1. Department of Industrial Engineering, Koc University, Rumelifeneri Yolu, Sariyer, Istanbul, 34450, Turkey

    N. Shahraki & M. Turkay

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  1. N. Shahraki
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Correspondence to M. Turkay.

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Shahraki, N., Turkay, M. Analysis of interaction among land use, transportation network and air pollution using stochastic nonlinear programming. Int. J. Environ. Sci. Technol. 11, 2201–2216 (2014). https://doi.org/10.1007/s13762-014-0566-3

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