Abstract
As the population in cities continues to increase, large-city problems, including traffic congestion and environmental pollution, have become increasingly serious. The construction of smart cities can relieve large-city problems, promote economic growth, and improve the quality of life for citizens. Intelligent transportation is one of the most important issues in smart cities that aims to make transportation safe, efficient, and environmentally friendly. There exist many optimization problems to achieve intelligent transportation, and most of them contain large-scale data and complex features that challenge traditional optimization methods. With the powerful search efficiency, evolutionary computation has been widely used to solve these optimization problems. In this paper, a two-layer taxonomy is introduced to review the research of evolutionary computation for intelligent transportation in smart cities. In the first layer, related studies are classified into three categories (land, air, and sea transportation) based on the application scene of the optimization problem. In the second layer, three categories (government, business, and citizen perspectives) based on the objective of the optimization problem are introduced for further classification. A detailed review of related studies is presented based on the two-layer taxonomy. Future research directions and open issues are also discussed to inspire researchers.
| Original language | English |
|---|---|
| Pages (from-to) | 83-102 |
| Number of pages | 20 |
| Journal | IEEE Computational Intelligence Magazine |
| Volume | 17 |
| Issue number | 2 |
| Early online date | 12 Apr 2022 |
| DOIs | |
| Publication status | Published - May 2022 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2005-2012 IEEE.
Funding
This work was supported in part by the National Key Research and Develop-ment Program of China under Grant 2019YFB2102102, in part by the National Natural Science Foundations of China under Grant 62176094 and Grant 61873097, in part by the KeyArea Research and Development of Guangdong Province under Grant 2020B010166002, in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003, in part by the National Research Foundation of Korea (NRF-2021H1D3A2A01082705), and in part by the Hong Kong GRF-RGC General Research Fund under Grant 11209819 (CityU 9042816) and Grant 11203820 (CityU 9042598).
