Mouromtzev D.I., Sender A.V., Chirkin A.M., Lisitsa N.I. Automatic analysis of local routes and adjacent house territory for urban planning support. Scientific and Technical Journal of Information Technologies, Mechanics and Optics
, 2017, vol. 17, no. 1, pp. 75–80. doi: 10.17586/2226-1494-2017-17-1-75-80
The paper presents research results of supporting the planning automation procedures and design in the field of urban design in order to reduce the design cycle duration. We show the methods of development and formalization of quantitative criteria for urban design. Two algorithms are proposed to support the urban area planning process at the early design stages without sufficient time-consuming. The planning process involves several alternatives. The algorithms give the possibility to automate the calculation of the required components of the urban area plan. The first algorithm assesses the required adjacent house territorybased on the existing configuration of buildings with the possibility of its preservation, as well as restrictions on the further planning stages in view of maintaining the existing plan. The second algorithm performs a plan of possible routes between the buildings and enables to carry out an optimal integration of the projected quarter in the urban environment. The given solution provides for a formal review of the project by specialists at the later stages of planning. Although the offered automated tools do not provide an optimal solution, they give the possibility to estimate the potential of planning decisions at the early stages of design. Implementation of the evaluation criteria in the automated design system enables the architects to reduce the number of errors detected by the specialized experts at the later stages of work. The planner's overall operation process is accelerated and simplified significantly. The proposed algorithms are integrated into the system of urban planning qua-kit.ethz.ch. Development results can be used in the planning process as well as for educational purposes.
urban area planning, urban design, automated planning decisions assessment tools Acknowledgements.
The work is a part of ADvISE research project (“Data analysis for understanding the impact of urban design on social performance of a city (ADvISE)” ETH Zürich project). This work was partially supported by RHF grant 16 -23-41007 References
1. Koohsari MJ, Sugiyama T., Mavoa S., Villanueva K., Badland H, Giles-Corti B., Owen N. Street network measures and adults' walking for transport: application of space syntax. Health Place
, 2016, vol. 38, pp. 89–95. doi: 10.1016/j.healthplace.2015.12.009
2. Koohsari M.J., Kaczynski A.T., Mcormack G.R., Sugiyama T. Using space syntax to assess the built environment for physical activity: applications to research on parks and public open spaces. Leisure Sciences
, 2014, vol. 36, no. 2, pp. 206–216. doi: 10.1080/01490400.2013.856722
3. Baran P.K., Rodriguez D.A., Khattak A.J. Space syntax and walking in a new urbanist and suburban neigh bourhoods. Journal of Urban Design
, 2008, vol. 13, no. 1, pp. 5–28. doi: 10.1080/13574800701803498
4. Avital M., Te’Eni D. From generative fit to generative capacity: exploring an emerging dimension of information systems design and task performance. Information Systems Journal
, 2009, vol. 19, no. 4, pp. 345–367. doi: 10.1111/j.1365-2575.2007.00291.x
5. Liu B., Chen X. 2013. Uncertain multiobjective programming and uncertain goal programming. Journal of Uncertainty Analysis and Applications
, 2013, vol. 3, no. 1, pp. 1–10.doi: 10.1186/s40467-015-0036-6
6. Frazer J. Creative design and the generative evolutionary paradigm. In Creative Evolutionary Systems
, 2002, pp. 253–274. doi: 10.1016/b978-155860673-9/50047-1
7. Janssen P. A generative evolutionary design method. Digital Creativity
, 2006, vol. 17, no. 1, pp. 49–63. doi: 10.1080/14626260600665736
8. Knox W.B., Stone P. Framing reinforcement learning from human reward: reward positivity, temporal discounting, episodicity, and performance. Artificial Intelligence
, 2015, vol. 225, pp. 24–50. doi: 10.1016/j.artint.2015.03.009
9. Koksalan M., Wallenius J., Zionts S. An early history of multiple criteria decision making. Journal of Multi-Criteria Decision Analysis
, 2013, vol. 20, no. 1-2, pp. 87–94. doi: 10.1002/mcda.1481
10. Naik N., Philipoom J., Raskar R., Hidalgo C. Streetscore - predicting the perceived safety of one million streetscapes. Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshops
, 2014. doi: 10.1109/cvprw.2014.121
11. Sant’Anna A.P. Probabilistic Composition of Preferences, Theory and Applications. Springer, 2015, 141 p.
12. Shneiderman B. Leonardo’s Laptop: Human Needs and the New Computing Technologies. MIT Press, 2003, 281 p.
13. Turner A., Penn A., Hillier B. An algorithmic definition of the axial map. Environment and Planning B: Planning and Design
, 2005, vol. 32, no. 3, pp. 425–444. doi: 10.1068/b31097
14. Zitzler E., Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation
, 1999, vol. 3, no. 4, pp. 257–271. doi: 10.1109/4235.797969