基于乘客到达率的公交线路非固定发车间隔优化模型及算法设计
Optimization Model and Algorithm Design of Bus Lines Non-Fixed Headways Problem Based on Passenger Arrival Rates

DOI: 10.12677/OJTT.2016.51002, PP. 7-16

Keywords: 乘客到达率，发车间隔优化，云遗传算法，筛选算子
Passenger Arrival Rates, Bus Headways Optimization, Cloud Genetic Algorithm, Filter Operator

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Abstract:

从公交线路基于空间维度(站点)和时间维度的乘客到达率出发，在不增加既定公交资源条件下，建立考虑公交线路运营商及乘客两方面成本的公交线路非固定发车间隔优化模型。引入“优良基因片段”的理念构建“筛选算子”和“替换算子”，设计了基于云遗传算法(CGA)的改进算法GCGA求解。算例仿真实验结果表明文中提出的优化模型有良好的适用性及经济性，所得到的非固定的发车间隔更符合乘客出行需求。同时，文中设计的GCGA算法的收敛速度及优化结果要优于一般GA算法。
Based on passenger arrival rates of bus lines along space dimension (Bus Station) and time di-mension, this paper establishes an optimization model of bus lines non-fixed headways, considering bus operators’ and passengers’ cost within current bus resources. By introducing “Good Gene” concept to build the “Filter operator” and “Replacement Operator”, the paper designs an improved algorithm GCGA from Cloud-model-based Genetic Algorithm (CGA). Simulation results show that optimization model proposed in this paper has good applicability, and convergence speed and optimization results of GCGA are superior to GA.

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