8 results about "Automated fare collection" patented technology

The invention relates to the technical field of rail transit intelligence, and discloses a rail transit intelligent scheduling management method and system, and the method comprises the steps: collecting the entering and exiting data of passengers, the number of people in a waiting area, the train load factor and the platform congestion degree through an automatic fare collection system, a video monitor and a sensor of each station of rail transit; the collected passenger flow data are preprocessed, a box plot about passenger flow distribution is constructed, and sudden passenger flow fluctuation areas are identified in different time windows; a K-means clustering algorithm is adopted to classify passenger flow modes in peak periods, and the distribution type of passenger flow fluctuation is analyzed; a time sequence prediction model is constructed by adopting a Transform model in combination with weather, holidays and festivals and emergencies, the future short-term and medium-and-long-term passenger flow trend is predicted, and the train departure interval is optimized; and based on the predicted passenger flow distribution, a scheduling optimization objective function is constructed, and a reinforcement learning algorithm is combined. The method has the advantage of improving the passenger flow prediction precision in the peak period.

This invention provides a method, system, computer equipment, and storage medium for predicting passenger flow at rail transit stations. The method extracts directed origin-destination (OD) pairs from the entry and exit records of an automated fare collection system using a fully directed network; determines the travel time for non-transfer and single-transfer journeys; determines the real-time location of passengers based on the OD pairs and travel time; determines the passenger flow at each station within a target time period based on the passengers' real-time locations; acquires and processes weather factors; constructs a CNN-BiLSTM-Attention passenger flow prediction model; and uses the passenger flow at each station within the target time period and weather factors as input variables to predict the passenger flow at each station using the CNN-BiLSTM-Attention passenger flow prediction model. This invention considers the influence of weather factors, time dependence, and spatial characteristics, enhancing the focus on key features and improving the accuracy of station passenger flow prediction.

The application relates to the technical field of rail transit, and provides a multi-level rail transit fusion pricing method, device, equipment and medium, the method comprises the following steps: converting the pricing rules of each ticket system corresponding to multi-level rail transit modes into the same pricing dimension to obtain the pricing parameters of each ticket system; dividing at least one pricing section of each OD path in a rail transit network according to different ticket system types; for each OD path, determining the section ticket price of each pricing section and determining the OD ticket price of the OD path based on the pricing parameters of the ticket system corresponding to each pricing section and the section mileage information of the pricing section; generating a ticket price parameter file based on each OD ticket price; and each level of automatic fare collection system charges passengers according to the ticket price parameter file. The multi-level rail transit fusion pricing method provided by the application realizes barrier-free transfer and one-ticket access of multi-level rail transit, and significantly improves the overall operation efficiency of the rail transit network and the user travel experience.

The application discloses a chip-driven token ticket recycling electromechanical integrated control method, and belongs to the technical field of automatic ticket selling and checking equipment control. The method comprises the following steps: acquiring infrared photoelectric sensor data, pressure microswitch data, Hall sensor data and motor current data collected by a motor current detection circuit and performing time sequence analysis and current ripple feature extraction; generating ticket movement trajectory and mechanical component health state; determining a control mode; adjusting PWM duty cycle in combination with a mapping relationship for torque conversion; generating a motor control signal to drive the motor; and executing a ticket recycling operation. The application adopts the means of fusing multi-source sensing data to generate ticket movement trajectory and mechanical component health state, and dynamically determines the control mode and adjusts the motor output torque based on the same, so that the adaptive ability, control precision and long-term operation reliability of the ticket recycling operation can be improved.

The invention provides a dynamic graph federal learning method for cross-line network urban rail passenger flow prediction, which comprises the following steps: preprocessing local automatic fare collection data of a client to obtain a local signal graph, and constructing and compressing an urban rail passenger flow dynamic space association graph of the client; local passenger flow mode space features of the urban rail passenger flow dynamic space association diagrams of all the clients are extracted and uploaded to a server for aggregation, so that a global urban rail network passenger flow dynamic space-time diagram is constructed, local model parameters of the clients are trained and optimized, and the local model parameters are uploaded to the server for aggregation; and according to an aggregation result of the local model parameters, the client updates the local model parameters and completes training of a local time sequence prediction model, so that the urban rail line network passenger flow is predicted, through a federated learning framework, AFC data leakage risks are avoided, cross-line global information is fully utilized, the passenger flow prediction accuracy is improved, cross-line communication overhead is greatly reduced, and the urban rail line network passenger flow prediction efficiency is improved. The real-time prediction efficiency is improved, and a reliable basis is provided for global urban rail network operation and train shift scheduling.

The invention provides a rail transit automatic fare collection standardized software system, which is applied to an automatic fare collection system of urban rail transit, and is characterized in that the standardized software system and station terminal equipment are connected with a station computer system, and the standardized software system comprises an application layer, a business layer, a data layer, an interface layer, a communication layer and an equipment layer, the application layer is a user-oriented layer of an AGM, a BOM, an SC and a reader-writer and realizes an overall function, the service layer realizes specific service logic of a software function, the data layer processes and stores service data, the interface layer performs interface calling and parameter and message assembly on an external system and an internal module, the communication layer realizes link management and data receiving and transmitting of bottom layer communication, and the service layer performs service data processing and storage on the external system and the internal module. And the equipment layer encapsulates a driving interface of hardware equipment for an upper layer to control and call. According to the invention, the system architecture is simplified, the software structure is optimized, the application service logic is unified, different software and hardware interfaces are adapted, and the super-large-scale network operation capability is realized.

Based on deep reinforcement learning, the urban rail passenger flow and train coordination organization optimization method is proposed. Firstly, based on the AFC data of urban rail transit, the passenger arrival law and travel chain are analyzed, and the OD matrix of urban rail transit network is generated by using the diffusion model. Secondly, a simulation model reflecting the train operation and passenger flow state is constructed to provide a verification environment for the optimization process. For train operation scheme and passenger flow scheduling, a DQN model is designed and trained and learned by using the simulation model. This method can reasonably match passenger demand and transport resources, effectively shorten passenger waiting time, and reduce train operating costs.