Smart City Gnosys
Smart city article details
| Title | A Machine-Learning Approach To A Mobility Policy Proposal |
|---|---|
| ID_Doc | 2487 |
| Authors | Shulajkovska M.; Smerkol M.; Dovgan E.; Gams M. |
| Year | 2023 |
| Published | Heliyon, 9, 10 |
| DOI | http://dx.doi.org/10.1016/j.heliyon.2023.e20393 |
| Abstract | The objective of the URBANITE project is to design an open-data, open-source, smart-city framework to enhance the decision-making processes in European cities. The framework's basis is a robust and user-friendly simulation tool that is supplemented with several innovative service modules. One of the modules, a multi-output, machine-learning unit, is deployed on the simulation results, enabling city officials to more effectively analyse vast quantities of data, discern patterns and trends, and so facilitate advanced policy decisions. The city's decision makers define potential city scenarios, key performance indicators, and a utility function, while the module assists in identifying the policy that is best aligned with the stipulated constraints and preferences. One of the main improvements is a speeding up of the policy testing for the decision makers, reducing the time needed for one policy verification from 3 hours to around 10 seconds. The system was evaluated for Bilbao's Moyua area, where it suggested strategies that could result in a decrease in emissions of more than 5% CO2, NOx, PM in the selected area and a broader part of the city with a machine-learning accuracy of 91%. The system was therefore able to provide valuable insights into effective policies for restricting private traffic in specific districts and identifying the most advantageous times for these restrictions. © 2023 The Author(s) |
| Author Keywords | Machine learning; Mobility policy; Smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
24074  | 0.921 | Shulajkovska M.; Smerkol M.; Noveski G.; Gams M. | Enhancing Urban Sustainability: Developing An Open-Source Ai Framework For Smart Cities | Smart Cities, 7, 5 (2024) |
| 8953 | 0.866 | França R.P.; Monteiro A.C.B.; Arthur R.; Iano Y. | An Overview Of The Machine Learning Applied In Smart Cities | Lecture Notes in Intelligent Transportation and Infrastructure, Part F1386 (2021) |
| 5173 | 0.86 | Zheng Y.; Hao Q.; Wang J.; Gao C.; Chen J.; Jin D.; Li Y. | A Survey Of Machine Learning For Urban Decision Making: Applications In Planning, Transportation, And Healthcare | ACM Computing Surveys, 57, 4 (2024) |
| 50394 | 0.853 | Papitha Christobel T.; Meenakshi S.; Rajeswari P.; Mohanambal K.; Giri R.K. | Smart City Optimization Through Machine Learning For Enhancing Urban Efficiency And Sustainability | Nanotechnology Perceptions, 20, S5 (2024) |
| 36027 | 0.852 | Zareba M.; Cogiel S.; Danek T.; Weglinska E. | Machine Learning Techniques For Spatio-Temporal Air Pollution Prediction To Drive Sustainable Urban Development In The Era Of Energy And Data Transformation | Energies , 17, 11 (2024) |
| 60587 | 0.851 | Alshanbari A.H.; Altalhi S.H.; Alsharif S.S.; Alharthi G.M.; Althubiti R.W.; Meshref H. | Using Machine Learning Techniques To Moderate Co2 Emission In Ksa Future Smart Cities | 2025 2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025 (2025) |