摘要
铁路枢纽运能瓶颈是导致车流积压、网络运输效率降低的主要因素之一。在分析运能瓶颈特点及产生机理的基础上,从拥堵强度、拥堵时长和拥堵趋势三个模块总结瓶颈影响因素,进而提炼可获取的数据指标及各指标的重要性标准。基于以上指标,利用遗传算法对长短时记忆网络的超参数进行优化,设计基于GA-LSTM网络的瓶颈识别模型。最后,基于某铁路枢纽的实际数据进行案例分析,对比各项深度学习方法,证明GA-LSTM网络的识别效果更佳;给出模型框架的应用范围:包括实时识别瓶颈情况和模拟不同指标数据的变化情况以提前开展瓶颈疏解工作。
The capacity bottleneck of a railway hub is one of the main factors leading to the backlog of traffic and reduced efficiency of network transport.Based on the analysis of the characteristics and mechanism of capacity bottlenecks,this paper summarized the bottleneck influencing factors in three modules of congestion intensity,congestion duration and congestion trend,and then extracted the available data indicators and the importance criteria of each indicator.Based on the above indicators,this paper used Genetic Algorithm(GA)to optimize the hyperparameters of Long Short-Term Memory Network(LSTM)and designed a bottleneck identification model based on GA-LSTM network.Finally,a case study based on the actual data of a railroad hub was conducted to compare various deep learning methods,which proved better effect of the GA-LSTM network in identifying bottlenecks.The application scope of the model framework was given,including real-time identification of bottlenecks and simulation of the changes of different indicator data to carry out bottleneck elimination in advance.
作者
王攸妙
宋瑞
李光晔
薛守强
WANG Youmiao;SONG Rui;LI Guangye;XUE Shouqiang(Key Laboratory of Transport Industry of Big Data Application Technologies for ComprehensiveTransport,Beijing Jiaotong University,Beijing 100044,China)
出处
《铁道学报》
EI
CAS
CSCD
北大核心
2023年第4期16-24,共9页
Journal of the China Railway Society
基金
国家自然科学基金(62076023)
中国国家铁路集团有限公司科技研究开发计划(N2021X025)。
关键词
货运市场
铁路枢纽
瓶颈识别
长短时记忆网络
遗传算法
freight market
railway hub
bottleneck identification
Long Short-Term Memory Network
Genetic Algorithm
