TY  - JOUR
PY  - 2012//
TI  - Computational intelligence in urban traffic signal control: a survey
JO  - IEEE transactions on systems, man and cybernetics part c: applications and reviews
A1  - Zhao, Dongbin
A1  - Dai, Yujie
A1  - Zhang, Zhen
SP  - 485
EP  - 494
VL  - 42
IS  - 4
N2  - Urban transportation system is a large complex nonlinear system. It consists of surface-way networks, freeway networks, and ramps with a mixed traffic flow of vehicles, bicycles, and pedestrians. Traffic congestions occur frequently, which affect daily life and pose all kinds of problems and challenges. Alleviation of traffic congestions not only improves travel safety and efficiencies but also reduces environmental pollution. Among all the solutions, traffic signal control (TSC) is commonly thought as the most important and effective method. TSC algorithms have evolved quickly, especially over the past several decades. As a result, several TSC systems have been widely implemented in the world, making TSC a major component of intelligent transportation system (ITS). In TSC and ITS, many new technologies can be adopted. Computational intelligence (CI), which mainly includes artificial neural networks, fuzzy systems, and evolutionary computation algorithms, brings flexibility, autonomy, and robustness to overcome nonlinearity and randomness of traffic systems. This paper surveys some commonly used CI paradigms, analyzes their applications in TSC systems for urban surface-way and freeway networks, and introduces current and potential issues of control and management of recurrent and nonrecurrent congestions in traffic networks, in order to provide valuable references for further research and development.<p />  <p>Language: en</p>
LA  - en
SN  - 1094-6977
UR  - http://dx.doi.org/10.1109/TSMCC.2011.2161577
ID  - ref1
ER  -