大数据时代下的公交客流仿真排班设计

Bus Passenger Flow Simulation Scheduling Design Based in the Era of Big Data

数据是智能交通建设的基础。传统的公交智能化排班侧重于经验和技术传承,容易忽视数据的整体协调,对于低满载率、班次运行时间不可控、线路串车等问题缺乏有效的技术手段。为了解决以上问题,本文提出了一个公交大数据挖掘和仿真排班方法,通过应用客流量、线路和站点登降量、实际满载率等数据,创新地根据服务优先、成本优先、综合服务与成本三种策略,完成排班参数调优,达到运力精准投放、合理分配人车资源的目标。实践结果表明:该公交客流仿真排班设计方法,符合公交企业智能排班需求,对公交智能化排班建设具有较大的改善作用。

Data is the foundation of intelligent transportation construction,the traditional intelligent scheduling of public transportation focuses on the inheritance of experience and technology,which easily overlooks the overall coordination of data.There is no effective technical means for problems such as low full load rate,uncontrollable operation time of shifts,and line congestion.In order to solve the above problems,this article proposes a big data mining and simulation scheduling method for public transportation.By applying data such as passenger flow OD,line and station boarding and landing volumes,and actual load factor,innovative scheduling parameters are optimized based on three strategies:service priority,cost priority,and comprehensive service and cost,achieving the goal of precise capacity allocation and reasonable allocation of human and vehicle resources.The practical results indicate that the simulation scheduling design method for public transportation passenger flow meets the intelligent scheduling needs of public transportation enterprises and has a significant improvement effect on the construction of intelligent bus scheduling.