From Vision to Reality: The Use of Artificial Intelligence in Different Urban Planning Phases

In an urban context, the use of artificial intelligence (AI) can help to categorise and analyse large amounts of data quickly and efficiently. The AI approach can make municipal administration and planning processes more efficient, improve environmental and living conditions (e.g., air quality, inventory of road damages, etc.), or strengthen the participation of residents in decision-making processes. The key to this is “machine learning” that has the ability to recognise patterns, capture models, and learn on the basis of big data via the application of automated statistical methods. However, what does this mean for urban planning and the future development of cities? Will AI take over the planning and design of our cities and actively intervene in and influence planning activities? This article applies a systematic literature review supplemented by case study analyses and expert interviews to categorise various types of AI and relate their potential applications to the different phases of the planning process. The findings emphasize that AI systems are highly specialised applications for solving and processing specific challenges and tasks within a planning process. This can improve planning processes and results, but ultimately AI only suggests alternatives and possible solutions. Thus, AI has to be regarded as a planning tool rather than the planning solution. Ultimately, it is the planners who have to make decisions about the future development of cities, taking into account the possibilities and limitations of the AI applications that have been used in the planning process.

Authors

Institutions

Publication Details

Journal
Urban Planning
Published
2024-08-22
DOI
https://doi.org/10.17645/up.8576
Citations
29
Primary Topic
Smart Cities and Technologies
Type
article
Field-Weighted Citation Impact
9.27
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From Vision to Reality: The Use of Artificial Intelligence in Different Urban Planning Phases

Lars Sievers, Frank Othengrafen, Eva Reinecke
29 citations
Urban Planning
Smart Cities and Technologies
9.27
article

From Vision to Reality: The Use of Artificial Intelligence in Different Urban Planning Phases

Lars Sievers, Frank Othengrafen, Eva Reinecke
article en
29 citations

Abstract

In an urban context, the use of artificial intelligence (AI) can help to categorise and analyse large amounts of data quickly and efficiently. The AI approach can make municipal administration and planning processes more efficient, improve environmental and living conditions (e.g., air quality, inventory of road damages, etc.), or strengthen the participation of residents in decision-making processes. The key to this is “machine learning” that has the ability to recognise patterns, capture models, and learn on the basis of big data via the application of automated statistical methods. However, what does this mean for urban planning and the future development of cities? Will AI take over the planning and design of our cities and actively intervene in and influence planning activities? This article applies a systematic literature review supplemented by case study analyses and expert interviews to categorise various types of AI and relate their potential applications to the different phases of the planning process. The findings emphasize that AI systems are highly specialised applications for solving and processing specific challenges and tasks within a planning process. This can improve planning processes and results, but ultimately AI only suggests alternatives and possible solutions. Thus, AI has to be regarded as a planning tool rather than the planning solution. Ultimately, it is the planners who have to make decisions about the future development of cities, taking into account the possibilities and limitations of the AI applications that have been used in the planning process.

Urban PlanningVol. 10
TU Dortmund University (DE)
Sustainable cities and communities
Openalex Percentile: Top 2%
Smart Cities and Technologies
9.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

From Vision to Reality: The Use of Artificial Intelligence in Different Urban Planning Phases — Lars Sievers, Frank Othengrafen, et al. · Urban Planning (2024) | TGRS Research Map | TGRS