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, per-formance 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 num-ber 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 tem-plate cloud at each site, the crowd coefficient is determined for each bus station. Finally, a case study is con-ducted 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 miti-gates the fuzziness and randomness associated with crowd coefficient assessments, thereby carrying significant theoretical and practical implications.
维普
万方数据
