Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities

Many cities around the world are facing immense pressure due to the expediting growth rates in urban population levels. The notion of ‘smart cities’ has been proposed as a solution to enhance the sustainability of cities through effective urban management of governance, energy and transportation. The research presented herein examines the applicability of a mathematical framework to enhance the sustainability of decisions involved in zoning, land-use allocation and facility location within smart cities. In particular, a mathematical optimisation framework is proposed, which links through with other platforms in city settings, for optimising the zoning, land-use allocation, location of new buildings and the investment decisions made regarding infrastructure works in smart cities. Multiple objective functions are formulated to optimise social, economic and environmental considerations in the urban space. The impact on underlying traffic of location choices made for the newly introduced buildings is accounted for through optimised assignment of traffic to the underlying network. A case example on urban planning and infrastructure development within a smart city is used to demonstrate the applicability of the proposed method.

[1]  Mumtaz Karatas,et al.  An iterative solution approach to a multi-objective facility location problem , 2018, Appl. Soft Comput..

[2]  Aurelio Tommasetti,et al.  A Review of Smart Cities Based on the Internet of Things Concept , 2017 .

[3]  Richard A. Slaughter,et al.  The IT revolution reassessed part three: Framing solutions , 2018, Futures.

[4]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[5]  Alasdair Reid,et al.  Smart cities: Under-gridding the sustainability of city-districts as energy efficient-low carbon zones , 2018 .

[6]  Danièle Dubois,et al.  Urban soundscapes: Experiences and knowledge , 2005 .

[7]  Nenad Mladenović,et al.  Local and Variable Neighborhood Searches for Solving the Capacitated Clustering Problem , 2017 .

[8]  Brian W. Kernighan,et al.  AMPL: A Modeling Language for Mathematical Programming , 1993 .

[9]  Biao Li,et al.  Integer programming for urban design , 2019, Eur. J. Oper. Res..

[10]  Giacomo Verticale,et al.  Energy Optimization and Management of Demand Response Interactions in a Smart Campus , 2016 .

[11]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  Shashank Bharadwaj,et al.  Impact of congestion on greenhouse gas emissions for road transport in Mumbai metropolitan region , 2017 .

[13]  Dong-Ming Yan,et al.  Computational network design from functional specifications , 2015, ACM Trans. Graph..

[14]  Akin Tascikaraoglu,et al.  Evaluation of spatio-temporal forecasting methods in various smart city applications , 2018 .

[15]  Ernesto Cipriani,et al.  Exploiting floating car data for time-dependent Origin–Destination matrices estimation , 2018, J. Intell. Transp. Syst..

[16]  David Rey,et al.  A Bi-level Mixed Integer Programming Model to Solve the Multi-Servicing Facility Location Problem, Minimising Negative Impacts Due to an Existing Semi-Obnoxious Facility , 2018 .

[17]  T. V. Woensel,et al.  From managing urban freight to smart city logistics networks , 2017 .

[18]  George Mavrotas,et al.  Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problems , 2009, Appl. Math. Comput..

[19]  David Rey,et al.  Sustainable urban facility location: Minimising noise pollution and network congestion , 2017 .

[20]  A. Vanolo Is there anybody out there? The place and role of citizens in tomorrow’s smart cities , 2016 .

[21]  Sajid Ali,et al.  Determination of the Most Optimal On-Shore Wind Farm Site Location Using a GIS-MCDM Methodology: Evaluating the Case of South Korea , 2017 .

[22]  Yi Peng,et al.  Smart city with Chinese characteristics against the background of big data: Idea, action and risk , 2018 .

[23]  Egon Balas,et al.  Integer Programming , 2021, Encyclopedia of Optimization.

[24]  Juan M. Corchado,et al.  Stochastic Navigation in Smart Cities , 2017 .

[25]  Mark Stevenson,et al.  City planning and population health: a global challenge , 2016, The Lancet.

[26]  Luis Muñoz,et al.  Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case , 2016 .

[27]  Michael Patriksson,et al.  The Traffic Assignment Problem: Models and Methods , 2015 .

[28]  Baizhan Li,et al.  Urbanisation and its impact on building energy consumption and efficiency in China , 2009 .

[29]  Sajid Ali,et al.  Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data , 2017 .

[30]  Mattias Höjer,et al.  Smart sustainable cities - Exploring ICT solutions for reduced energy use in cities , 2014, Environ. Model. Softw..

[31]  Luis Muñoz,et al.  The Business Model Evaluation Tool for Smart Cities: Application to SmartSantander Use Cases , 2017 .

[32]  Robert Barrett,et al.  Toward V2I communication technology-based solution for reducing road traffic congestion in smart cities , 2015, 2015 International Symposium on Networks, Computers and Communications (ISNCC).

[33]  Stephan Maier,et al.  Smart energy systems for smart city districts: case study Reininghaus District , 2016, Energy, Sustainability and Society.

[34]  Igor Wojnicki,et al.  Improving Control Efficiency of Dynamic Street Lighting by Utilizing the Dual Graph Grammar Concept , 2018 .

[35]  Ibrahim Dincer,et al.  Smart energy systems for a sustainable future , 2017 .

[36]  Hui Zhao,et al.  Locating Charging Stations of Various Sizes with Different Numbers of Chargers for Battery Electric Vehicles , 2018, Energies.

[37]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[38]  Jonathan F. Bard,et al.  Practical Bilevel Optimization: Algorithms and Applications , 1998 .

[39]  Alberto Ferraris,et al.  The cities of the future: Hybrid alliances for open innovation projects , 2018, Futures.

[40]  Yoshiki Yamagata,et al.  Simulating a future smart city: An integrated land use-energy model , 2013 .

[41]  Angel P. del Pobil,et al.  The Role of Internet of Things (IoT) in Smart Cities: Technology Roadmap-oriented Approaches , 2018 .

[42]  Diane J. Cook,et al.  Activity-Aware Energy-Efficient Automation of Smart Buildings , 2016 .

[43]  J. G. Wardrop,et al.  Some Theoretical Aspects of Road Traffic Research , 1952 .

[44]  Neeraj Kumar,et al.  Fuzzy rough set based energy management system for self-sustainable smart city , 2018 .

[45]  Simon Joss,et al.  Sustainable–Smart–Resilient–Low Carbon–Eco–Knowledge Cities; Making sense of a multitude of concepts promoting sustainable urbanization , 2015 .

[46]  Matthias Ehrgott,et al.  Multicriteria Optimization , 2005 .

[47]  Antonio Colmenar-Santos,et al.  Integration of distributed generation in the power distribution network: The need for smart grid control systems, communication and equipment for a smart city — Use cases , 2014 .

[48]  Ming K Lim,et al.  The use of smart technologies in enabling construction components reuse: A viable method or a problem creating solution? , 2017, Journal of environmental management.

[49]  Imtiaz Parvez,et al.  Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach , 2016 .

[50]  Fernando Augusto Silva Marins,et al.  Algorithms applied in decision-making for sustainable transport , 2018 .

[51]  Ligang Liu,et al.  MIQP‐based Layout Design for Building Interiors , 2018, Comput. Graph. Forum.

[52]  Jose Villar,et al.  Energy management and planning in smart cities , 2016 .

[53]  J G Wardrop,et al.  CORRESPONDENCE. SOME THEORETICAL ASPECTS OF ROAD TRAFFIC RESEARCH. , 1952 .