Smart Governance Models to Optimise Urban Planning Under Uncertainty by Decision Trees

被引:1
|
作者
Garau, Chiara [1 ]
Desogus, Giulia [1 ]
Annunziata, Alfonso [1 ]
Coni, Mauro [1 ]
Crobu, Claudio [2 ]
Di Francesco, Massimo [2 ]
机构
[1] Univ Cagliari, Dept Civil & Environm Engn & Architecture, I-09129 Cagliari, Italy
[2] Univ Cagliari, Dept Math & Comp Sci, Via Osped 72, I-09124 Cagliari, Italy
关键词
Replicability; Smart governance; Decision-making processes; Project sustainability; Project scalability; CITIES;
D O I
10.1007/978-3-030-87010-2_41
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In recent years, the applicative approach to smart governance in urban planning field has increasingly involved the decision-making processes of public administrations and has helped to solve economic, social and environmental challenges of cities. This approach has in fact allowed administrations to understand how changes are taking place in the territory and in real time, through big data, e-governance and city dashboards. However, the literature underlines the lack of decision-making models on which an agreement had been recognized for the organization and management of new projects on an urban scale. This need involves (1) understanding clearly the needs of all actors involved in a project (public or private financiers, public administration, control offices and stakeholders), (2) making optimal decisions w.r.t. the selected criteria, (3) providing a hedge against unexpected data changes. The main applicative goal is to have a full awareness of how much every single change means in economic, logistical and time lag terms. To this end, the authors investigate the viability of Decision Trees to support decision-making processes for urban planning.
引用
收藏
页码:551 / 564
页数:14
相关论文
共 50 条
  • [41] Quantitative models for supply chain planning under uncertainty: a review
    David Peidro
    Josefa Mula
    Raúl Poler
    Francisco-Cruz Lario
    The International Journal of Advanced Manufacturing Technology, 2009, 43 : 400 - 420
  • [42] Quantitative models for supply chain planning under uncertainty: a review
    Peidro, David
    Mula, Josefa
    Poler, Raul
    Lario, Francisco-Cruz
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2009, 43 (3-4): : 400 - 420
  • [43] Robust Models for Manufacturing Capacity Planning under Demand Uncertainty
    Karnik, Aditya
    Tallichetty, Chandrashekar S.
    Saroop, Atul
    2009 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, 2009, : 310 - 315
  • [44] Qualitative models for decision under uncertainty without the commensurability assumption
    Fargier, H
    Perny, P
    UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 1999, : 188 - 195
  • [45] Accidents and decision making under uncertainty: A comparison of four models
    Barkan, R
    Zohar, D
    Erev, I
    ORGANIZATIONAL BEHAVIOR AND HUMAN DECISION PROCESSES, 1998, 74 (02) : 118 - 144
  • [46] USE OF DECISION-MODELS UNDER UNCERTAINTY IN THERMAL DESIGN
    ZMEUREANU, R
    FAZIO, P
    COMPUTER-AIDED DESIGN, 1987, 19 (10) : 523 - 526
  • [47] Impact of Smart City Planning and Construction on Community Governance under Dynamic Game
    Guo, Jie
    Ling, Wenhao
    COMPLEXITY, 2021, 2021
  • [48] CORE VS MLE FOR DECISION-MODELS UNDER UNCERTAINTY
    RAO, TVSR
    SINGH, SP
    JOURNAL OF ECONOMICS-ZEITSCHRIFT FUR NATIONALOKONOMIE, 1990, 51 (02): : 145 - 158
  • [49] Formal models and algorithms for decentralized decision making under uncertainty
    Seuken S.
    Zilberstein S.
    Autonomous Agents and Multi-Agent Systems, 2008, 17 (2) : 190 - 250
  • [50] Towards a Distributed Planning of Decision Making Under Uncertainty for a Fleet of Robots
    Jourdan, Paul
    Lozenguez, Guillaume
    Fabresse, Luc
    Bouraqadi, Noury
    PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), 2020, : 800 - 807