The unreliable service and the unstable operation of a high-frequency bus line are shown as bus bunching and the uneven distribution of headways along the bus line. Although many control strategies, such as the static and dynamic holding strategies, have been proposed to solve the above problems, many of them take on some oversimplified assumptions about the real bus line operation. So it is hard for them to continuously adapt to the evolving complex system. In view of this dynamic setting, we present an adaptive holding method that combines the classic approximate dynamic programming (ADP) with the multistage look-ahead mechanism. The holding time, the only control means used in this study, will be determined by estimating its impact on the operation stability of the bus line system in the remaining observation period. The multistage look-ahead mechanism introduced into the classic Q-learning algorithm of the ADP model makes it easy that the algorithm gets through its earlier unstable phase more quickly and easily. During the implementation of the new holding approach, the past experiences of holding operations can be cumulated effectively into an artificial neural network used to approximate the unavailable Q-factor. The use of a detailed simulation system in the new approach makes it possible to take into account most of the possible causes of instability. The numerical experiments show that the new holding approach can stabilize the system by producing evenly distributed headway and removing bus bunching thoroughly. Compared with the terminal station holding strategies, the new method brings a more reliable bus line with shorter waiting times for passengers.

Pricing for ridepooling services may be affected by travel needs, penalties, and ride service quality. Firstly, this paper explores pricing methods based on the perceived value of the ride as compared to the distance–time prices and penalties and proposes a ridepooling travel utility function for pricing strategies. Then this paper develops a combined optimization ridepooling scheduling model integrating order allocation and path optimization (CORM), and designs a hybrid simulated annealing (HSA) heuristic algorithm for solving this model. Based on the Cournot game theory, this paper further develops a ridepooling bidding model (RBM) that can simultaneously optimize the seekers’ utility and the profit of the ridepool platform. The effectiveness of the CORM and HSA are verified, and the superiority of the RBM has also been proved by a numerical simulation example. The results show the following: (1) neither the low distance–time price and high penalty price strategy nor the high distance–time and low penalty price strategy is an absolute advantage strategy for ridepooling passengers and the platform; (2) compared with the CORM, the RBM can not only effectively improve the profit of ridepooling platform, but also improve the success rate of ridepooling.

With the popularization of the Internet and the widespread application of mobile terminal, travelers are increasingly dependent on traffic information. It is particularly important to construct and develop more accurate discrete traffic state prediction models. Considering that most of the previous studies about traffic state prediction are essentially a prediction of specific parameters, this paper proposes a generalized fractional difference (GFD)-auto regressive moving average (ARMA)-Fisher combined model that can directly predict the discrete traffic state. First, using the original historical traffic flow data as training samples for cluster analysis, we obtain different traffic state classification samples. Then, using the Fisher discriminant method, we develop a discrete traffic state recognition model based on the traffic state classification samples. The model can help us identify the discrete state of the future traffic flow when we input a set of predicted traffic parameters to it. In order to improve the accuracy of the model prediction, this paper creatively proposes to apply the GFD method to the stabilization of original traffic flow data and then develop an ARMA model to predict the traffic parameters processed by GFD (GFD-ARMA). Last, we obtain the predicted discrete traffic state using the calibrated Fisher discrimination model whose inputs are the parameters predicted by the GFD-ARMA. The proposed method is tested using field data from CHangzhou, China. The results suggest that the developed GFD-ARMA-FISHER method shows a higher accuracy for traffic state prediction and is better than other similar methods.

The unreliable service and the unstable operation of a high frequency bus line are shown as bus bunching and the uneven distribution of headways along the bus line. Although many control strategies, such as the static and dynamic holding strategies, have been proposed to solve the above problems, many of them take on some oversimplified assumptions about the real bus line operation. So it is hard for them to continuously adapt to the evolving complex system. In view of this dynamic setting, we present an adaptive holding method which combines the classic approximate dynamic programming (ADP) with the multi-stage look-ahead mechanism. The holding time, that is the only control means used in this study, will be determined by estimating its impact on the operation stability of the bus line system in the remained observation period. The multi-stage look-ahead mechanism introduced into the classic Q-learning algorithm of the ADP model makes the algorithm get through its earlier unstable phase more quickly and easily. During the implementation of the new holding approach, the past experiences of holding operations can be cumulated effectively into an artificial neural network used to approximate the unavailable Q-factor. The use of a detailed simulation system in the new approach makes it possible to take into accounts most of the possible causes of instability. The numerical experiments show that the new holding approach can stabilize the system by producing evenly distributed headway and removing bus bunching thoroughly. Comparing with the terminal station holding strategies, the new method brings a more reliable bus line with shorter waiting times for passengers.

In order to explore the mechanism of urban residents ' risk perception on ride-pooling intention under the background of public health crisis, based on the moderated mediation effect model, this paper explores the relationship between residents ' risk perception, ride-pooling attitude and ride-pooling intention through correlation analysis and mediation effect analysis, and uses bootstrap test to analyze the moderating effect of gender and residence risk level. The results show that: (1) There is a significant negative correlation between risk perception and the two dimensions of ride-pooling attitude and intention; there is a significant positive correlation between ride-pooling attitude and ride-pooling intention. (2) The attitude of ride-pooling plays a complete mediating role between risk perception and ride-pooling intention. (3) The direct predictive effect of risk perception on ride-pooling intention and the mediating effect of ride-pooling attitude will not be regulated by gender. The direct predictive effect of risk perception on ride-pooling intention will be regulated by the risk level of residence. Based on the above research results, some suggestions on epidemic prevention measures and platform management are put forward to provide reference for urban governance and ride-pool optimization.