The walking time in metro stations is influenced by passenger flow with large fluctuation. Therefore, this paper proposes a method of station walking time calculation considering the influence of passenger flow: firstly, the time, entry, and exit direction and volume distribution characteristics of station passenger flow are analyzed, and the threshold of passenger flow that affects walking time is defined; then, an entry and exit walking time calculation model is established by five independent travel time chains and three station walking time constraints, and the transfer walking time is calculated based on the entry and exit walking time results; finally, the accuracy and validity of the walking time projection model are verified by using the calculation results. The results show that the calculation results are accurate and the validity of the model reaches 91.5%, and the constraint effect on walking time at station time is most obvious when the passenger flow reaches 3000 passengers per 30 min.
Real-time and accurate travel time information between bus stations is critical for passengers to make suitable travel plans to reduce waiting time at the stops. By mining and analyzing bus operational data, it can be obtained that factors such as the variation of vehicle speed in adjacent sections and the proportion of bus lanes between stations have affected the travel time between bus stations. Therefore, considering the temporal feature, spatial feature, and weather feature as the prediction model’s input, travel time between bus stations prediction model based on eXtreme Gradient Boosting (XGBoost) was trained and established. The 28-day bus operation data of a certain bus line in Guangzhou was used for training and verification, and they were compared with the prediction models based on K -Nearest Neighbors (KNN), BP neural network, and Light Gradient Boosting Machine (LightGBM). In comparison with other models, the lowest MAPE of 11.96% was found for the XGBoost prediction model, which is 9.30% lower than other models on average. The sensitivity analysis of the proposed prediction model was further conducted: temporally, the accuracy of the prediction model was best during the flat peak hours; spatially, the MAPE of the model gradually decreased as the number of line units increased, and when the number of line units exceeded 18, the accuracy of the prediction model stabilized and was lower than 7%. The results confirm that the XGBoost model outperforms the KNN, BP, and LightGBM in terms of fitting, accuracy, and stability.
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.
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.
Abnormal passenger behavior in rail transit has become a top priority, as it affects operational safety. Passenger travel time is the main basis for identifying abnormal behavior while considering the flexibility of travel time. Currently, the main method is to use absolute threshold discrimination based on the distribution of travel time. However, there is a problem of missing abnormal passenger behavior due to the large difference in travel time between the Origin-Destinations (ODs). Therefore, this paper proposes a method of setting corresponding thresholds for each OD. By analyzing the percentile curves of the overall and individual OD pairs, it was found that the turning point of the curve had a significant feature, and the difference between the two sides of the curve was obvious. This paper proposes a bilateral fitting method, and the results show that this method can calculate the relative threshold for different OD pairs. The significant advantages of this method are its low cost and wide coverage.
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