| id |
ecaade2018_112 |
| authors |
Yu, K. Daniel, Haeusler, M. Hank, Simon, Katrina and Fabbri, Alessandra |
| year |
2018 |
| title |
Data Influenced Infrastructure Generation - Combining holistic urban datasets through a digital Slime Mold algorithm for cycle path generation |
| source |
Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 647-656 |
| doi |
https://doi.org/10.52842/conf.ecaade.2018.2.647
|
| summary |
Existing infrastructure in cities has become increasingly incapable of operating at its designed efficiency. This demand has been created by the growth in population generating a larger demand and strain on the existing infrastructure. This paper explores how user-generated data could be utilised to create transport infrastructure, more specifically bicycle pathways. Through a series of 'four sprints', a pathway generation system has been adapted from the behaviour of Slime Molds (Physarum Polycephalum), in particular, its ability to define shortest paths on a terrain. The first sprint outlines the design of a Slime Mold algorithm between user-specified points, the second utilises the algorithm for pathway generation in a macro and micro urban scale (acknowledging both the existing infrastructure and cadastral), the third defines weight or effort limits for the pathways in order to suite realistic user-profiles (fitness level of cyclist groups), and the last sprint creates the start and end points for the pathway generation from user-generated data, applying the Slime Mold system to a 'real world' context. Through the four sprints, a design tool has been created that can be used to not only create and analyse cycle pathways, but tweaked for various other forms of tangible transport infrastructure. |
| keywords |
urban planning; agent based modelling; optimisation and decision support; transport planning |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (9,175,981 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:57 |
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