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浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Gravitational search algorithm based adaptive low emission signal timing
LI Shi-wu1, XU Yi1, WANG Lin-hong1, SUN Wen-cai1, BIE Yi-ming2
1.Transportation School, Jilin University, Changchun 130022, China; 2. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
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Abstract  

The importance of the operation time in adaptive low emission signal timing was analyzed combining with adaptive signal timing strategy, and the advantages of  gravitational search algorithm (GSA) over normal search algorithms were analyzed. The adaptive low emission signal timing model and the gravitational search algorithm were referred to propose the GSA based adaptive low emission signal timing method in order to reduce vehicle emission at intersections. The real traffic data were used to verify the method. Results show that under the same accuracy, the vehicle emission of GSA based adaptive low emission signal timing method is 2.82% lower than the vehicle emission of the normal optimization based signal timing method, and the operation time is 36.56% shorter than the normal optimization based signal timing method. The GSA based adaptive low emission signal timing method has better optimization effect and faster operation speed, and is suitable for adaptive low emission optimization．

Published: 10 September 2015
CLC:  U 495  
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LI Shi-wu, XU Yi, WANG Lin-hong, SUN Wen-cai, BIE Yi-ming. Gravitational search algorithm based adaptive low emission signal timing. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2015, 49(7): 1313-1318.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2015.07.016     OR     http://www.zjujournals.com/eng/Y2015/V49/I7/1313


基于万有引力搜索算法的低排放自适应配时

结合自适应配时策略分析运算时间在低排放自适应配时中的重要性和基本万有引力搜索算法（GSA）较常规搜索算法的优势,以减少交叉口处机动车排放为目的,借鉴低排放自适应配时模型和GSA优化算法原理,提出基于GSA的低排放自适应信号配时方法.以实际交通状态数据为基础数据进行实例验证.结果显示,在相同精度下使用基于GSA配时方法较基于一般优化方法的配时的车均排放低2.82%,运算时间短36.56%.证明了基于GSA的低排放自适应信号配时方法具有较好的优化效果和较高的运算速度,适用于低排放自适应配时的优化.

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