An Organizational Scheme for Metro Traffic Based on Long-short Route Optimization
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摘要: 针对地铁客流分布不均衡情况,对地铁行车组织方案进行优化以缓解部分站点客流压力.建立乘客出行时间价值和平均载客率同时最优的双目标优化模型.假定乘客到达车站的时刻服从均匀分布,以此计算在途和等候时间价值.为了满足各个行车段的不同特征,在大小交路模式下定义不同交路段的载客率和列车使用车底数,更加符合运行的实际情况.以广州地铁6号线的运行情况为例,运用基于全局搜索的粒子群算法进行求解,验证了模型的有效性和合理性.结果表明:考虑单一出行时间价值的模型与该模型相比,出行时间价值降低3.21%,但平均载客率提高20.2%;考虑单一平均载客率的模型与该模型相比,平均载客率提高11.98%,但出行时间价值降低了1.68%,因此综合考虑乘客出行时间价值和平均载客率的优化模型可以使2个目标函数值同时达到最优.Abstract: With purpose to reduce the traffic pressure of partial stations caused by uneven distribution of passenger flow,optimizing methods of the organization scheme of rail transit is proposed in this study.A model of bi-obj ective func-tion is developed,which includes value of travel time and load ratio of passengers.Values of transit time and waiting time are computed based on an assumption that passengers arrive at one station subordinates to uniform distribution.In order to reflect the characteristics differences of each routing section,load ratio of passengers and car base of different routings are defined based on long-short route optimization,which is more in accordance with the actual situation of the operation. A case study of Metro Line 6 in Guangzhou is conducted to verify the validity and rationality of this model.A particle swarm optimization algorithm based on global search is applied to solve the problem.The results show that,compared with the model in this study,the model only considering the value of travel time of passengers is reduced by 3.21%, while average ratio of passenger load is increased by about 20.2%.The average ratio of passenger load considering single travel time value is increased by about 2.55% under the circumstances of value of passengers travel time is reduced by 11. 98% when only considering the value of passengers travel time and the passenger load ratio comprehensively.In conclu-sion,the model propose in this study could satisfy two obj ective function values at the same time.
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Key words:
- rail transit /
- routing method /
- long-short route /
- traffic organization /
- Particle Swarm Optimization
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