Smart City Gnosys
Smart city article details
| Title | Es-Band: A Novel Approach To Coordinate Green Wave System With Adaptation Evolutionary Strategies |
|---|---|
| ID_Doc | 24326 |
| Authors | Zheng Y.; Ma D.; Jin F.; Zhao Z. |
| Year | 2019 |
| Published | Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2019 |
| DOI | http://dx.doi.org/10.1145/3356470.3365532 |
| Abstract | Urban arterial traffic coordination control catches great attention in the process of smart city construction. To achieve the optimum signal timing, many studies attempt to adjust green splits of a cycle time according to the distance between the road intersections. However, the existing green wave traffic control system usually has a sophisticated calculation, which depends upon the stability of vehicle speed and traffic flow, leading to weak robustness. Therefore, this short paper puts forward ES-Band, that is, a novel approach to control arterial traffic coordination with the help of artificial intelligence. ES-Band introduces the Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES), a scalable alternative to reinforcement learning, into signal timing. Different traffic variables are adopted as parameters for searching the optimal value by CMA-ES. In order to evaluate the feasibility and effectiveness of ES-Band, we import the real traffic flow data of Zhongshan Road in Ningbo, Zhejiang Province, China, into traffic environment simulator for training and carry out a series of experiments. The results have shown that the ES-Band outperforms the traditional methods in terms of a better convergence, lower travel time, and fewer stops. © 2019 Association for Computing Machinery. |
| Author Keywords | CMA-ES; Green Wave Coordinate; Intelligent Signal Control; Traffic Simulator |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
12771  | 0.864 | Cao M.; Li V.O.K.; Shuai Q. | Book Your Green Wave: Exploiting Navigation Information For Intelligent Traffic Signal Control | IEEE Transactions on Vehicular Technology, 71, 8 (2022) |
| 21559 | 0.863 | Chen J.-Y.; Wei F.-F.; Chen T.-Y.; Hu X.-M.; Jeon S.-W.; Wang Y.; Chen W.-N. | Earl-Light: An Evolutionary Algorithm-Assisted Reinforcement Learning For Traffic Signal Control | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (2024) |
| 5265 | 0.859 | Chandra B.R.; Swamy K.C.T.; Tirupal T.; Chandra I.S. | A Survey On Implementation Of Swarm Evolutionary Algorithms To Vehicular Communication | 7th International Conference on Trends in Electronics and Informatics, ICOEI 2023 - Proceedings (2023) |
| 21429 | 0.855 | Skoropad V.N.; Deđanski S.; Pantović V.; Injac Z.; Vujičić S.; Jovanović-Milenković M.; Jevtić B.; Lukić-Vujadinović V.; Vidojević D.; Bodolo I. | Dynamic Traffic Flow Optimization Using Reinforcement Learning And Predictive Analytics: A Sustainable Approach To Improving Urban Mobility In The City Of Belgrade | Sustainability (Switzerland), 17, 8 (2025) |
| 25829 | 0.851 | Thamaraiselvi K.; Bohra A.R.; Vishal V.; Sunkara P.S.; Sunku B.; Nityajignesh B. | Exploring Traffic Signal Control: A Comprehensive Survey On Reinforcement Learning Techniques | 3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025 (2025) |
| 50634 | 0.85 | Ahmadi K.; Allan V.H. | Smart City: Application Of Multi-Agent Reinforcement Learning Systems In Adaptive Traffic Management | 2021 IEEE International Smart Cities Conference, ISC2 2021 (2021) |