Journal
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 4, Pages 370-388Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15472450.2018.1504294
Keywords
Multi-objective optimization; dynamic predictive control; traffic network; cell transmission model; genetic algorithm; 2010 Mathematics Subject Classification; Primary
Funding
- Fundamental Research Funds for the Central Universities [N170503012, N170308028]
- National Natural Science Foundation of China [11172197, 11332008, 11572215]
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Traffic congestion in urban network has been a serious problem for decades. In this paper, a novel dynamic multi-objective optimization method for designing predictive controls of network signals is proposed. The popular cell transmission model (CTM) is used for traffic prediction. Two network models are considered, i.e., simple network which captures basic macroscopic traffic characteristics and advanced network that further considers vehicle turning and different traveling routes between origins and destinations. A network signal predictive control algorithm is developed for online multi-objective optimization. A variety of objectives are considered such as system throughput, vehicle delay, intersection crossing volume, and spillbacks. The genetic algorithm (GA) is applied to solve the optimization problem. Three example networks with different complexities are studied. It is observed that the optimal traffic performance can be achieved by the dynamic control in different situations. The influence of the objective selection on short-term and long-term network benefits is studied. With the help of parallel computing, the proposed method can be implemented in real time and is promising to improve the performance of real traffic network.
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