Simulation Analysis on Influence of Congestion Propagation on Operation of Carsharing Systems
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摘要:
随着共享汽车渗透率的不断增加，站点、路段层面的车辆溢出和拥挤传播现象日趋严重. 为刻画拥挤传播对汽车共享系统运行的影响机理，首先，搭建具有时变性和状态相关性的汽车共享系统排队网络；其次，基于C# 语言和O2DES离散事件仿真框架，提出并设计考虑车路交互影响和拥挤传播现象的汽车共享系统仿真模型，分析动态随机环境下站点与路段层面的拥挤传播现象对汽车共享系统运行的影响；最后，以成都市三站点的小规模汽车共享系统为例，在不同转运比例、需求和道路拥堵场景下，将该模型与引入虚拟空间的无穷排队模型进行对比分析. 研究结果表明：站点和路段层面的拥挤传播现象会导致系统服务率下降9.3%～16.9%，相比无穷排队模型，考虑拥挤传播现象的排队模型更能反映汽车共享系统的实际运营过程；当路网的道路占用率为70% （路网处于中度拥堵）时，考虑拥挤传播现象的汽车共享系统可实现最大收益；汽车共享系统的引入会为道路资源的动态分配带来新变化，当公共交通转向汽车共享系统的用户占比超过70%时，路网拥堵加剧，不利于汽车共享系统的有效运营和可持续发展.
Abstract:With the increasing penetration of carsharing, vehicle overflow and congestion propagation at the level of station and path tend to be serious. In order to describe the influence mechanism of congestion propagation on the operation of carsharing systems, firstly, a queuing network of the carsharing system is built with time-varying and state-dependence properties. Secondly, based on C# language and O2DES framework of discrete event simulation, a simulation model of the carsharing system under dynamic stochastic environment is proposed, which allows for the influence of vehicle–road interaction and congestion propagation. The influence of congestion propagation on the operation of the carsharing system is analyzed in terms of the station and path levels. Finally, a small-scale carsharing system, i.e., three stations in Chengdu, is exemplified. The proposed model and the infinite queuing model in virtual space are compared and analyzed under different transfer ratios, demands and road congestion scenarios. The results show that congestion propagation at the stations and paths will decline the system service rate by 9.3%–16.9%. Compared with the infinite queuing model, the proposed model can better reflect the actual operation of the carsharing system because of considering congestion propagation. When the occupancy rate of the road network reaches 70% (the road network is in moderate congestion), the proposed carsharing system can achieve maximum benefits. The introduction of the carsharing system will bring new changes to the dynamic allocation of road resources. When the proportion of users from public transportation to the carsharing system exceeds 70%, it will intensify the congestion of the road network, which is not conducive to the effective operation and sustainable development of the carsharing system.
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图 8 不同拥堵场景下的性能指标
Figure 8. Performance indicators under different congestion scenarios
表 1 速度模型标准点取值
Table 1. Representative point values of velocity model
相对密度 平均行驶速度/（km•min−1） ${\text{ } }{f_{m,{\text{p} } } }(t){\text{ = } }0$ ${v_{m,0,{\text{p} } } } = 1.00$ ${f_{m,{\text{p} } } }(t) = {b_{m,1,{\text{p} } } }(t){\text{ = } }0.1$ ${v_{m,1,{\text{p} } } } = 0.55$ ${f_{m,{\text{p} } } }(t) = {b_{m,2,{\text{p} } } }(t){\text{ = } }0.2$ ${v_{m,2,{\text{p} } } } = 0.40$ 
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表 2 两种排队模型的性能指标求解结果
Table 2. Solved performance indicators of two queuing models
模型 系统服务率/% 车辆利用率/% 虚拟空间
存储车辆
占比率/%订单流失率/% 利润/
（元•d−1）拥挤传播
排队模型78.50 23.78 0 21.50 4081.50 无穷排队模型 91.33 31.09 34.28 8.67 5139.60
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