#urban intelligence

movement path generation: urban scenario definition, tree-search structure based on established network connectivities, displaying values for relevant parameters (e.g., proximity, landmark, heritage, etc.), decision-making differs with each path increment with respect to defined scenario and path parameter

Source: ©dciric, 2024

science of networks

Grand Paris complete graph generation zoomed in_ exercise: network generation based on proximity links - first 10 iterations; territorial coverage: Greater Paris territory - smaller city area. Software: Grasshopper - Rhinoceros

The Science of Networks: Grand Paris

©Dragana Ciric, 2023

STRAND 2023 Exhibition

On Architecture - Challenges in Design

Cities are getting smarter—not just with sensors and cameras, but with AI that thinks, predicts, and optimizes in real time. From managing rush-hour traffic to reducing energy waste, AI is the invisible backbone helping urban environments become cleaner, faster, and more sustainable.

🚦 Trash doesn't pile up. Lights don’t stay on unnecessarily. Buses arrive when and where they’re needed. That’s the power of AI-driven urban intelligence.

🔍 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐀𝐈 𝐢𝐬 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐭𝐡𝐞 𝐜𝐢𝐭𝐲 𝐠𝐫𝐢𝐝:

✅ 𝐓𝐫𝐚𝐟𝐟𝐢𝐜 𝐅𝐥𝐨𝐰 & 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 AI-powered traffic lights adapt in real time to reduce congestion and emissions. Cameras + predictive models reroute traffic after accidents before gridlock forms.

✅ 𝐖𝐚𝐬𝐭𝐞 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Smart bins send fill-level data to AI systems that optimize pickup routes, reducing fuel use and missed pickups.

✅ 𝐄𝐧𝐞𝐫𝐠𝐲 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 & 𝐆𝐫𝐢𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 AI helps utilities predict demand surges and adjust in real time—lowering blackout risks and enabling smarter use of renewables.

✅ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 IoT sensors + AI monitor infrastructure health, predicting failures before they happen—saving costs and lives.

✅ 𝐄𝐦𝐢𝐬𝐬𝐢𝐨𝐧 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 & 𝐀𝐢𝐫 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 AI analyzes sensor data to pinpoint pollution sources and suggest mitigation strategies in real time.

📌 𝐓𝐡𝐞 𝐁𝐢𝐠 𝐏𝐢𝐜𝐭𝐮𝐫𝐞: Smart cities don’t just collect data—they act on it. With AI, urban centers evolve into adaptive ecosystems that serve people better and respond faster to change.

🔗 Read More: https://technologyaiinsights.com/

the "next move" search operation from multiple locations (starting points of multiple agents) within the network, displaying distance from the point of origin

Software: Grasshopper Rhinoceros

Path construction based on search and decision-making operations performed at the starting and each consecutively occupied point/location within the network

Software: Grasshopper, Rhinoceros

search operation from the single point within the network; the algorithm connects the point of the agents location with searched destination points; the search is based on proximity parameter, displaying the connections line between the origin and possible destination point; the search is constrained by the number of options to be analysed, but the constraint factor can also be defined by searchable domain or radius

the path construction tree grafts and branches containing closest points of the second and third iteration for a specific point of the first iteration are displayed to indicate one of the possible path development configurations

Software: Grasshopper Rhinoceros

localisation and search of possible moves (connectivities) from a given location (state S) based on constrained proximity parameters

Grasshopper workflow