Artificial Intelligence in Urban Planning

Please let me know if you have suggestions for publications to add (tom.sanchez[at]


Abarca-Alvarez, F. J., Campos-Sanchez, F. S., & Reinoso-Bellido, R. (2018). Demographic and dwelling models by artificial intelligence: Urban renewal opportunities in Spanish Coast. International Journal of Sustainable Development and Planning, 13(7), 941–953.

Abdel-Moneim, N. M., & El Seddawy, A. I. (2021). Towards a Better Urban Design in Respect to Environmental Behavior Using Knowledge Discovery. Journal of Southwest Jiaotong University, 56(3), Article 3.

Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189.

Ahmed, S., Hossain, Md. F., Kaiser, M. S., Noor, M. B. T., Mahmud, M., & Chakraborty, C. (2021). Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities. In C. Chakraborty, J. C.-W. Lin, & M. Alazab (Eds.), Data-Driven Mining, Learning and Analytics for Secured Smart Cities: Trends and Advances (pp. 23–47). Springer International Publishing.

Aithal, B. H., & Ramachandra, T. (2016). Visualization of urban growth pattern in Chennai using geoinformatics and spatial metrics. Journal of the Indian Society of Remote Sensing, 44(4), 617–633.

Alavi, H. S., Churchill, E. F., Wiberg, M., Lalanne, D., Dalsgaard, P., Fatah gen Schieck, A., & Rogers, Y. (2019). Introduction to Human-Building Interaction (HBI) Interfacing HCI with Architecture and Urban Design.

Alexander, E. R. (2001). What Do Planners Need to Know? Journal of Planning Education and Research, 20(3), 376–380.

Alexander, E. R. (2014). “Planning” or e-Planning?: Implications for Theory, Education and Practice. International Journal of E-Planning Research (IJEPR), 3(1), 1–15.

ALHARTHI, S., ALHARTHI, A., & ALHARTHI, M. (2019). Sustainable Development Goals In The Kingdom Of Saudi Arabia’s 2030 Vision. WIT Transactions on Ecology and the Environment, 238, 455–467.

Ali, U., & Mahmood, T. (2018). Using Deep Learning to Predict Short Term Traffic Flow: A Systematic Literature Review. In T. Kováčiková, Ľ. Buzna, G. Pourhashem, G. Lugano, Y. Cornet, & N. Lugano (Eds.), Intelligent Transport Systems – From Research and Development to the Market Uptake (pp. 90–101). Springer International Publishing.

Allaert, G., De Sutter, R., Kellens, W., & Vanneuville, W. (2009). Intelligent decision support system based geo-infonnation technology and spatial planning for sustainable water management in Flanders, Belgium.

Allam, S. (2018). The Future of Urban Models in the Big Data and AI Era: A Bibliometric Analysis. Sudhir Allam,” THE FUTURE OF URBAN MODELS IN THE BIG DATA AND AI ERA: A BIBLIOMETRIC ANALYSIS”, International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320–2882.

Allam, Z. (2020). Urban chaos and the AI Messiah. In Cities and the Digital Revolution (pp. 31–60). Springer.

Allam, Z. (2021a). Big Data, Artificial Intelligence and the Rise of Autonomous Smart Cities. In Z. Allam (Ed.), The Rise of Autonomous Smart Cities: Technology, Economic Performance and Climate Resilience (pp. 7–30). Springer International Publishing.

Allam, Z. (2021b). Introducing the Concept of Autonomous City. In Z. Allam (Ed.), The Rise of Autonomous Smart Cities: Technology, Economic Performance and Climate Resilience (pp. 1–6). Springer International Publishing.

Allam, Z. (2021c). On Complexity, Connectivity and Autonomy in Future Cities. In Z. Allam (Ed.), The Rise of Autonomous Smart Cities: Technology, Economic Performance and Climate Resilience (pp. 31–47). Springer International Publishing.

Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91.

Allam, Z., & Newman, P. (2018). Redefining the Smart City: Culture, Metabolism and Governance. Smart Cities, 1(1), 4–25.

Almahmood, M., & Skov-Petersen, H. (2020). Public Space Public Life 2.0: Agent-based Pedestrian Simulation as a Dynamic Visualisation of Social Life in Urban Spaces. Journal of Digital Landscape Architecture, 305–317.

Al-Ramini, A., Takallou, M. A., Piatkowski, D. P., & Alsaleem, F. (2022). Quantifying changes in bicycle volumes using crowdsourced data. Environment and Planning B: Urban Analytics and City Science, 23998083211066104.

Al-Sayed, K. (2017). Thinking systems in urban design: A prioritised structure model. In Explorations in Urban Design (pp. 195–206). Routledge.

Ameer, S., & Shah, M. A. (2018). Exploiting big data analytics for smart urban planning. 1–5.

Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for Human-AI Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–13). Association for Computing Machinery.

Angelidou, M., Psaltoglou, A., Komninos, N., Kakderi, C., Tsarchopoulos, P., & Panori, A. (2018). Enhancing sustainable urban development through smart city applications. Journal of Science and Technology Policy Management, 9(2), 146–169.

Anttiroiko, A.-V. (2015). Smart planning: The potential of web 2.0 for enhancing collective intelligence in urban planning. In Emerging Issues, Challenges, and Opportunities in Urban E-Planning (pp. 1–32). IGI Global.

Anttiroiko, A.-V., & Caves, R. W. (2014). Urban Planning 3.0: Impact of recent developments of the Web on urban planning. In IT in the public sphere: Applications in administration, government, politics, and planning (pp. 233–257). IGI Global.

Aqib, M., Mehmood, R., Alzahrani, A., & Katib, I. (2020). A Smart Disaster Management System for Future Cities Using Deep Learning, GPUs, and In-Memory Computing. In R. Mehmood, S. See, I. Katib, & I. Chlamtac (Eds.), Smart Infrastructure and Applications: Foundations for Smarter Cities and Societies (pp. 159–184). Springer International Publishing.

Arndt, L. T., Philips, J. W., Cordeiro, A. D., Romano, C. A., & Catai, R. E. (2014). Domain ontology for urban land management. Proceedings of the Institution of Civil Engineers-Urban Design and Planning, 167(2), 58–68.

Arribas-Bel, D., Garcia-López, M.-À., & Viladecans-Marsal, E. (2021). Building(s and) cities: Delineating urban areas with a machine learning algorithm. Journal of Urban Economics, 125, 103217.

As, I., Basu, P., & Talwar, P. (n.d.). Artificial Intelligence in Urban Planning and Design. Retrieved June 6, 2022, from

Aschwanden, G. D., Haegler, S., Bosché, F., Van Gool, L., & Schmitt, G. (2011). Empiric design evaluation in urban planning. Automation in Construction, 20(3), 299–310.

Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483–485.

Athey, S. (2019). The Impact of Machine Learning on Economics. In 21. The Impact of Machine Learning on Economics (pp. 507–552). University of Chicago Press.

Ayaz, N., & Akay, B. (2020). Smart Municipalities in Tourism: In E. Çeltek (Ed.), Advances in Hospitality, Tourism, and the Services Industry (pp. 391–413). IGI Global.

Baaj, M. H., & Mahmassani, H. S. (1992). Artificial intelligence-based system representation and search procedures for transit route network design. Transportation Research Record, 1358, Article 1358.


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Ballouch, Z., Hajji, R., & Ettarid, M. (2022). Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urban Areas. In F. Barramou, E. H. El Brirchi, K. Mansouri, & Y. Dehbi (Eds.), Geospatial Intelligence: Applications and Future Trends (pp. 67–77). Springer International Publishing.

Balmaceda, B., & Fuentes, M. (2016). Cities and methods from complexity science. Journal of Systems Science and Complexity, 29(5), 1177–1186.

Banerjee, S., Chakraborty, C., & Das, D. (2020). An approach towards GIS application in smart city urban planning. In Internet of Things and Secure Smart Environments (pp. 71–110). Chapman and Hall/CRC.

Bao, W., Lianju, N., & Yue, K. (2019). Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications, 128, 301–315.

Baoxing, Q. (2018). Resilient urban design methods and principles based on the complex adaptive system theory. Landscape Architecture Frontiers, 6(4), 42–47.

Baran, G. (2020). The Opportunities and Threats Resulting from Robotic Process Automation in Public Service Development. Zarządzanie Publiczne, 52, 17–27.

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Barbosa Jardim, A., Müh, M., Kondziela, A., & Häusler, A. (2021). Synthetic and Tangible Agents for an Activity-based Urban Planning Tool. 805–815.

Barbosa, L., Pham, K., Silva, C., Vieira, M. R., & Freire, J. (2014). Structured Open Urban Data: Understanding the Landscape. Big Data, 2(3), 144–154.

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Batty, M. (2007). Planning support systems: Progress, predictions, and speculations on the shape of things to come. 26.

Batty, M. (2018a). Artificial intelligence and smart cities.

Batty, M. (2018b). Science in Planning: Theory, Methods and Models. In Planning Knowledge and Research. Routledge.

Batty, M. (2021). Planning education in the digital age. Environment and Planning B: Urban Analytics and City Science, 48(2), 207–211.

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Bazan-Krzywoszańska, A., Lach, R., & Mrówczyńska, M. (2020). City as a System Supported by Artificial Intelligence. Urban and Regional Planning. Special Issue: Management of the City-A Multi-Branch Task, 5(2), 32–39.

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Belhadi, A., Djenouri, Y., Srivastava, G., & Lin, J. C.-W. (2021). SS-ITS: secure scalable intelligent transportation systems. The Journal of Supercomputing, 1–17.

Belkaid, A., & Abdelkader Ben Saci, I. H. (2021). Human-Computer Interaction for Urban Rules Optimization.

Ben-David, A., & Frank, E. (2009). Accuracy of machine learning models versus “hand crafted” expert systems – A credit scoring case study. Expert Systems with Applications, 36(3, Part 1), 5264–5271.

Bennett, A. (2021). Autonomous vehicle driving algorithms and smart mobility technologies in big data-driven transportation planning and engineering. Contemporary Readings in Law and Social Justice, 13(1), 20–29.

Berta, M., Caneparo, L., Montuori, A., & Rolfo, D. (2016). Semantic urban modelling: Knowledge representation of urban space. Environment and Planning B: Planning and Design, 43(4), 610–639.

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Bickley, S. J., Chan, H. F., & Torgler, B. (2022). Artificial intelligence in the field of economics. Scientometrics, 127(4), 2055–2084.

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Bienvenido-Huertas, D., Farinha, F., Oliveira, M. J., Silva, E. M. J., & Lança, R. (2020). Comparison of artificial intelligence algorithms to estimate sustainability indicators. Sustainable Cities and Society, 63, 102430.

Biswas, S., Chen, F., Sistrunk, A., Muthiah, S., Chen, Z., Self, N., Lu, C.-T., & Ramakrishnan, N. (2020). Geospatial Clustering for Balanced and Proximal Schools. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13358–13365.

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Boeing, G., Batty, M., Jiang, S., & Schweitzer, L. (2021). Urban Analytics: History, Trajectory, and Critique. 20.

Bolívar, M. P. R., & Meijer, A. J. (2016). Smart Governance: Using a Literature Review and Empirical Analysis to Build a Research Model. Social Science Computer Review, 34(6), 673–692.

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Borri, D., & Camarda, D. (2009). The cooperative conceptualization of urban spaces in AI-assisted environmental planning. 197–207.

Borri, D., & Camarda, D. (2013). Modelling space perception in urban planning: A cognitive ai-based approach. In Contemporary Challenges and Solutions in Applied Artificial Intelligence (pp. 3–9). Springer.

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Cai, C. J., Winter, S., Steiner, D., Wilcox, L., & Terry, M. (2019). “Hello AI”: Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 104:1-104:24.

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