基于云模型的公交车内拥挤度判别

Determination of Bus Crowding Coefficient Based on Cloud Mode

为了提高公交车内乘客的舒适性,需要对公交车内的拥挤度进行实时判别。首先对公交车内早高峰的客流量进行实时调查。其次针对公交车内拥挤度判别的模糊性和随机性,提出一种基于云模型的公交车内拥挤度判别的方法。在利用云模型进行拥挤度判别时,选取乘客拥挤度的度量标准,根据它们在不同服务水平下的边界值计算云的数字特征,将6种服务水平下求得的子云合成标准云,根据早高峰车内人数,将求得的立席密度和乘载率代入云发生器,建立待识别云模型。最后通过对各个站点的待识别云和模板云的拥挤度进行计算,判断公交各个站点的拥挤度。以济南市B218路公交为例进行案例分析,利用云模型对各个站点的拥挤度进行判别,所选取的站点拥挤系数从64.3变化为118.0,对应的服务水平从C变化为F。该方法不仅能有效判别公交车内的拥挤度,而且能有效地避免公交车内拥挤度判别的模糊性和随机性,具有较强的理论意义和实践意义。

To enhance the comfort of bus passengers,it is essential to assess accurately the real-time crowd coefficient inside the bus.In this research,a real-time survey of passenger flow was conducted during the morning peak hour to address this concern.To minimize the impact of random passenger flow on determining the bus crowd coefficient,a cloud model-based method for crowd level identification is proposed.Firstly,performance measurements are selected to determine the bus crowd coefficient,and the digital characteristics of the cloud model are calculated based on the boundary values of these measures across six levels of service.Then,the sub-clouds obtained for each LOS were synthesized into a standard cloud.By considering the number of passengers,passenger density,and loading frequency,a cloud generator is utilized to establish the bus crowd coefficient identification model.By calculating the crowd degrees of the identification cloud and template cloud at each site,the crowd coefficient is determined for each bus station.Finally,a case study is conducted on bus line B218 in Jinan city to verify the proposed model.The results indicate the crowd coefficients ranging from 64.3 to 118 for the case study route,corresponding to LOSs between C and F.This method of discriminating bus crowding coefficients not only effectively determines congestion coefficients but also mitigates the fuzziness and randomness associated with crowd coefficient assessments,thereby carrying significant theoretical and practical implications.