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To improve bus passengers’ degree of comfort, it is necessary to determine the real-time crowd coefficient in the bus. With this concern, this paper employed the RBF Neural Networks approach to predict the number of passengers in the bus based on historical data. To minimize the impact of the randomness of passenger flow on the determination of bus crowd coefficient, a cloud model-based bus crowd coefficient identification method was proposed. This paper first selected the performance measurements for determining bus crowd coefficient and calculated the digital characteristics of the cloud model based on the boundary values of the selected performance measures under six Levels-of-Service (LOSs). Then the subclouds obtained under the six LOSs were synthesized into a standard cloud. According to the predicted number of passengers in the bus, the passenger density and loading frequency were calculated, which were imported into the cloud generator to set up the bus crowd coefficient identification model. By calculating the crowd degrees of identification cloud and template cloud at each site, this paper determined the crowed coefficient of each bus station. Finally, this paper took the bus line No. 10 in Dalian city as case study to verify the proposed model. It was found that the crowd coefficients of the selected route ranged from 60.265 to 109.825, and the corresponding LOSs ranged between C and F. The method of discriminating bus crowding coefficient can not only effectively determine the congestion coefficient, but also effectively avoid the fuzziness and randomness of the crowding coefficient judgment in the bus, which has strong theoretical and practical significance.

In order to reveal the traffic characteristics at signalized intersections with road-side bus lane, the analysis methods of capacity and delay at entrance lane were studied. Selecting a signalized intersection with road-side bus lane as investigation location, several traffic parameters were surveyed. Considering the traffic conflict between right-turn vehicles and through buses in this kind of entrance lane and based on the Gap Acceptance Theory, the calculation model of saturation flow rate, which is a key parameter both in the formulas of capacity and delay at signalized intersection mentioned in HCM2000, was improved. Using the investigation data, the calculation results of the traditional model and improved model were compared and analyzed. The research indicated that, comparing with common entrance lane, the capacity of right-turn lane at the entrance lane with bus lane should be reduced, and the travelling delay of bus lane and its inside adjacent lane should be increased. The degree of these effects is related to bus volume, right-turn volume and headway between buses. [ABSTRACT FROM AUTHOR]

The characteristics of traffic conflicts occurring at the time of direction changes were analyzed to better understand the problems of a temporary reversible lane. Using Huangpu Road as the investigation site, data of traffic conflicts before and after direction changes were investigated. The data on traffic conflict characteristics were compared and analyzed with mathematical statistics. Using the regression analysis method, a prediction model of the traffic conflicts was constructed. This paper also discussed the utilization ratio problem for this kind of lane. The research showed obvious and regular differences between before and after direction changing in a temporary reversible lane. There is an observable relationship between the traffic conflict, flow, and density. The problem of the lower lane utilization ratio of a temporary reversible lane usually exists. [ABSTRACT FROM AUTHOR]