How to efficiently guide passengers and ensure the order of airport operation is an urgent transport problem for airport management. Based on the analysis of the factors that affect the driver’s decision‐making, this paper deeply explores the collaborative association of the core factors, such as the number of flight arrivals in different periods and the average seeking distance of taxis. Firstly, according to the GPS data of taxis, the paper uses clustering algorithm to get the average passenger‐seeking time from the airport and makes matching interaction between the number of flights based on time distribution and the average passenger‐carrying capacity of vehicles in the parking garage, so as to build a decision‐making model based on the number of taxis N; secondly, it takes passenger safety and traffic order as the priority and uses M/M/S queuing model to integrate the two factors. Taking the maintenance cost and passenger evacuation time as constraints, the judgment condition of minimum cost Z_min and the optimal number of boarding points S_m are solved. Finally, taking the flight and taxi data of Shanghai Hongqiao Airport as an example, the driver’s decision‐making standard is simulated, and the accuracy of the model is verified by the deviation rate. It can provide decision‐making support for taxi management of urban transportation hub and rapid evacuation of airport passengers, so as to realize the collaborative optimization of airport flight arrival and taxi carrying order.

To alleviate decision-making pressure on rail transit dispatchers in the emergency handling process, this work sorts out the scenario elements of rail transit emergency cases, establishes a scenario element system, and uses the information weight method to determine the weight of each scenario element. Based on the information of the key decision points, the complete process of emergencies is divided into various scenarios, and an emergency case representation model is constructed. The model establishes a database of historical emergency cases in rail transit, utilizes the scenarios as the search object to match the similarity of emergencies, and provides the decision-making support information to handle the current emergencies. Furthermore, the model constructed in this paper is subjected to an actual emergency case for analysis and calculation, which verify the validity and feasibility of the proposed model.

A CAPP/PPC scheduling integration model is proposed based on the decentralized decision-making architecture of the Mediator in this paper and it can be used to solve the problem of the dynamic scheduling in practical production. Mediator technology and contract net are effectively combined in this model so that mediator can be used to carry out management and control in process plan and production scheduling integration model and contract net can be used as the bidding tool in dynamic scheduling, while the equipment and the course of concrete processing in production is completed by Agent technology, whereby the task sequence and optimization, bidding process and system evaluation can be realized. The dynamic scheduling can be accomplished under the mechanism of negotiation and coordination, whereby simplifying the reasoning mechanism of Agent and reducing the information lots in communication. The scheduling simulation for a randomly achieved emergent task illustrates that the method is very effective for the dynamic scheduling and rescheduling in production system with the frequently-changed environment.

Abnormal behaviors of train drivers are a critical factor affecting the operational safety of urban rail transit. To achieve automated and efficient detection while meeting practical deployment requirements, this study proposes an end-to-end Temporal Action Detection network based on skeleton data. The network directly uses skeleton sequences as input, integrates a skeleton topology graph tailored to train driver actions for spatiotemporal feature extraction, and employs a non-shared feature propagation design to enhance classification and regression performance. Evaluated on a custom dataset of driver operations (including both standard and abnormal behaviors), the experimental results demonstrate favorable performance with high mean Average Precision (mAP) and strong accuracy. The findings show that the proposed network can accurately localize and classify driver operational behaviors, enabling precise detection of abnormal actions. Furthermore, its low parameter count and minimal storage requirements highlight strong potential for practical deployment in urban rail transit systems.

The suburban line connects the suburbs and the city centre; it is of huge advantage to attempt the express-slow mode. The passengers’ average travel time is the key factor to reflect the level of rail transport services, especially under the express-slow mode. So it is important to study the passengers’ average travel time under express-slow, which can get benefit on the optimization of operation scheme. First analyze the main factor that affects passengers’ travel time and then mine the dynamic interactive relationship among the factors. Second, a new passengers’ travel time evolution algorithm is proposed after studying the stop schedule and the proportion of express/slow train, and then membrane computing theory algorithm is introduced to solve the model. Finally, Shanghai Metro Line 22 is set as an example to apply the optimization model to calculate the total passengers’ travel time; the result shows that the total average travel time under the express-slow mode can save 1 minute and 38 seconds; the social influence and value of it are very huge. The proposed calculation model is of great help for the decision of stop schedule and provides theoretical and methodological support to determine the proportion of express/slow trains, improves the service level, and enriches and complements the rail transit operation scheme optimization theory system.

Using motion unit tracking technology, the pedestrian motion parameters were extracted from the monitoring video data at the ticket gate facilities in subway stations and the indicators of the lane change behavior were determined. The pedestrian lane change behavior spectrum of ticket gate facilities in subway stations was constructed from the three elements of types of ticket gate facilities, pedestrian flow, and lane change behavior, and the quartile method was used to determine the upper and lower thresholds of the indicators. The results showed that when the pedestrian flow was (5, 10] ped/min, the average values of displacement, distance, and cumulative side shift distance were the largest and the thresholds were the largest. When the pedestrian flow was (10, 15] ped/min, pedestrians generally adopted a faster walking speed to change lanes. With the increase of the pedestrian flow, the average value of the change in direction of the movement increased and the longitudinal distance at the gate-type ticket gate facilities was greatly affected by the pedestrian flow. The number of lane changes was generally once. The research results can provide a basis for scientifically setting up ticket gate facilities and reducing congestion risks caused by abnormal lane change behavior.