TRANSITION TO «GREEN» ECONOMY IN RUSSIA: CURRENT AND LONG-TERM CHALLENGES
Abstract
Nowadays, many believe that there is no way to ecological stability other than transition to «environmentally oriented economy». In urban areas, the main sources of pollutants are industrial enterprises and automobile transport. To reduce the adverse environmental impacts one needs special methods of air quality control. Specifically, research in this field is aimed at developing of control systems for the city transport in order to predict the environmental response to changing traffic parameters and take appropriate measures to improve the situation. In this work it is demonstrated how the method of transport system control, based on simulation modeling, has been implemented. The optimization experiment has been performed on a simulation model adjusting the parameters of parts of a city road network for adequate decision making. Model experimenting has made it possible to establish the optimal traffic density and average current rates, without exceeding the pollution quotas, and calculate the consequences of changing in the number of vehicle car fleet on city roads. The experiment was carried out in the city of Naberezhnye Chelny, Russia.
Keywords
Acknowledgements
This work was funded by the subsidy allocated
to Kazan Federal University for the project part
of the state assignment in the sphere of scientific
activities.
References
1. Economic and Social Council. Integration Segment: Sustainable Urbanization. 2014, 1-8.
2. Global Status Report On Road Safety. Time For Action. World Health Organization. 2009, 287.
3. Horton, B.; Ulrich Speck, S. Green Economy. Europe’s environment: An Assessment of Assessments; Publication Ofiice of the European Union: Luxembourg, 2011; 92-138.
4. Hygienic requirements for air quality residential areas. Sanitary - epidemiological rules and norms. SanPin 2.1.6.98300; Russian Ministry of Health: Moscow, 2000; 47.
5. Makarova, I.; Khabibullin, R.; Belyaev, E.; Zhdanov, D. Intellectualization of transport systems for the benefit of safety and the sustainable development of territories. Journal of International Scientific Publications: Ecology&Safety. 2013, 7(3), 189-199.
6. Molina, Mario J.; Molina, Luisa T. Megacities and Atmospheric Pollution. Journal of the Air & Waste Management Association. 2004, 54(6), 644-680.
7. Naoto, M.; Watanabe, T.; Feng, J. Route Optimization Using Q-Learning for On-Demand Bus Systems. Knowledge-Based Intelligent Information and Engineering Systems. 2008, 5178, 567-574.
8. OND-86. Method of calculating the concentrations in the air of harmful substances contained in industrial emissions; Gidrometeoizdat: Leningrad, 1987; 35.
9. Petruhin, V.; Vazhensky, V.; Donchenko, V. Pollution of urban atmosphere by vehicles and environmental risks to public health: methodology and experience ratings / Transport: science, technology, management. 1996, 9. 33-35.
10. Reich, Alicia A. Transportation Efficiency. Strategic Planning for Energy and the Environment. 2012, 32(2), 32-43.
11. Saharidis, G.K.D.; Dimitropoulos, C.; Skordilis, E. Minimizing waiting times at transitional nodes for public bus transportation in Greece. An International Journal “Operational Research”. 2013, 14(3), 341-359.
12. Shimamoto, H.; Murayama, N.; Fujiwara, A. Evaluation of an existing bus network using a transit network optimisation model: a case study of the Hiroshima City Bus network. Transportation. J. 2010, 37(5). 801–823.
13. The general plan of the city of Naberezhnye Chelny: Materials on the study of the project. Explanatory note. Tatinvestgrazhdanproekt: Kazan, 2009, 140.