In the actual operation of a bus, due to the influences of the passenger flow, traffic conditions and other factors, the vehicle back-station time is often delayed, which brings difficulties in commuting according to a timetable that results in the discontinuity of the bus. This is also the main disadvantage of static bus scheduling. Therefore, the “Entropy model of dynamic bus dispatching based on the prediction of back-station time” is proposed, which can be used for decreasing the passive effect of discontinuity by extending the departure interval of an early bus in advance, and to realize fairness in adjustments of the departure interval by using entropy theory. Finally, the model is validated by two examples, and the results show that the model can match the distribution pattern of the bus departure interval before and after an adjustment and as far as possible, it can reduce bus breaks, balance the occupancy rate and improve the stability of bus operations.

Public transport priority is the development trend in public transport, and signal priority is its main means. In order to further improve the accuracy of delay calculation and realize the priority of bus signals, this paper proposes the idea of multiple conversion criteria and consideration of stop time for the coordination and control of bus and car mixed traffic flow trunk roads. First of all, on the basis of in-depth analysis of the differences in the characteristics of bus and car models, a multi-conversion standard delay calculation method is proposed, and its effectiveness is verified by simulation. The results show that compared with the single conversion standard delay calculation method, the average delay error of cars and buses calculated by this method is reduced by 22.54% and 82.21%, respectively. Then, the influence of bus stops on bus speed and delay is further analyzed, and the coordinated control model of bus priority trunk roads considering bus stops is constructed with the passenger capacity of each bus line and the per capita delay as the goal, and the solution is given. Finally, 178 randomly generated examples are used to verify and analyze the effectiveness and sensitivity of this model.