列车开行方案与客流需求的匹配性评估与优化研究

Matching evaluation and optimization of train operation plan and passenger flow demand

列车开行方案与客流需求匹配与否在很大程度上决定铁路运输服务的质量。高匹配度的开行方案能有效协调运营方与乘客方之间的利益,实现两者的双赢。为提升列车开行方案与客流需求的匹配度,从能力效用、运输质量和服务水平3个方面以及不同维度的6个指标构建开行方案与客流需求的匹配性评估指标体系。为使评价结果更加客观、科学、严谨,通过解析各个评价指标的特点以及评价指标之间、指标与开行方案之间的关联,比选提出结合因子分析的E-TOPSIS法进行综合评估。基于上述评估结果,针对匹配度较低时段的列车开行方案,创造性地统一了评估模型与优化模型,建立以匹配性评估模型为上层、客流平衡分配模型为下层的双层规划模型,并以保障客流基本出行需求、列车停站需求、线路及列车最大能力等为约束进行优化。最后,由案例分析可知,优化之后的开行列车数较原方案减少了2列,平均客座率提高5.7%,列车停站方案由4类变为5类,停站率增加2.5%,匹配度总体提升0.201,优化后的开行方案更加适应客流需求。研究充分表明,该方法不仅能提前预测与评估列车开行方案的执行情况,还能有效提高两者的匹配度,为往后进一步优化列车开行方案提供决策依据和指导建议。

Whether the train operation plan matches the passenger flow demand or not largely determines the quality of the railway transportation service.The operation plan with high matching degree can effectively coordinate the interests of the operator and the passenger to achieve a win-win situation.In order to improve the matching degree of the train operation plan and passenger flow demand,the evaluation index system of matching degree of train operation plan and passenger flow demand was constructed from three aspects of capacity utility,transportation quality,service level and six indicators of different dimensions.In order to make the evaluation results more objective,scientific and rigorous,by analyzing the characteristics of each evaluation index and the correlation between the evaluation index and the development plan,the E-TOPSIS method combined with factor analysis was proposed to carry out comprehensive evaluation.Based on the evaluation results,according to the low matching degree during the train operation plan,by creatively unifying the assessment model and the optimization model,a bi-level programming model with the matching evaluation model as the upper layer and the balanced passenger flow distribution model as the lower layer was established.And the optimization was carried out with the constraints of guaranteeing basic travel demand of passenger flow,train stop demand,line and maximum capacity of train.Finally,it could be seen from the case analysis that compared with the original scheme,the number of trains running after optimization was reduced by 2,the average passenger load factor was increased by 5.7%,the train stop scheme was changed from 4 to 5,the stop rate was increased by 2.5%,and the overall matching degree was improved by 0.201.The optimized scheme was more suitable for passenger flow demand.The research fully shows that this method can not only predict and evaluate the implementation of train running scheme in advance,but also effectively improve the matching degree of the two,providing decision making basis and guidance for further optimization of train operation plan in the future.