546 results about "Transit bus" patented technology

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

The invention relates to a method for measuring the toughness of an urban public transport network in extreme rainstorm weather. The method comprises the following steps: acquiring station and line data of the urban public transport network, rainstorm disaster information and passenger flow data; a bus-subway double-layer traffic network is constructed; based on the constructed bus-subway double-layer traffic network, establishing a linear relation between rainstorm rainfall intensity and passenger flow change, and calculating disturbance values of the rainstorm to stations and connecting edges in the network; constructing a bus-subway double-layer traffic network cascade failure model considering passenger flow, and representing a dynamic evolution process of an urban public traffic network in extreme rainstorm weather; and determining a network performance measurement index by using the constructed bus-subway double-layer traffic network cascade failure model, and measuring the toughness level of the urban public traffic network in extreme rainstorm weather. According to the method, the urban public transport network toughness can be accurately measured, and the coping capacity of an urban public transport system in extreme rainstorm weather is improved.

The invention discloses an STM32F407-based intelligent walking stick system and a control method, which are used for solving the problems of single function, high false alarm rate and insufficient data security of the traditional walking stick. The system integrates a posture detection module, a heart rate and blood oxygen monitoring module and an environment sensing module, constructs a user health risk pre-judgment model through multi-modal data fusion, realizes a three-level adaptive alarm mechanism in combination with a dynamic decision algorithm, and triggers acousto-optic warning, cloud communication and emergency contact linkage according to risk levels. The payment and data transmission reliability is guaranteed by adopting a physically isolated security architecture and a national secret encryption technology; the dynamic power management system optimizes energy consumption distribution according to scene requirements, and the endurance of equipment is remarkably prolonged. According to the system, the monitoring accuracy is improved through a closed-loop protection system (risk identification-adaptive response-energy optimization), bus payment and smart home control are supported, the system has the characteristics of low false alarm rate, high environmental adaptability and low power consumption, and a safe and convenient life auxiliary solution is provided for old users.

Disclosed herein are methods, devices, and systems for detecting bus lane moving violations. One aspect of the disclosure concerns a method comprising capturing a video showing a vehicle located in a bus lane, inputting video frames from the video to an object detection deep learning model to detect the vehicle and bound the vehicle in a vehicle bounding polygon, determining a trajectory of the vehicle in an image space of the video frames, transforming the trajectory of the vehicle in the image space into a trajectory of the vehicle in a GPS space, inputting the trajectory of the vehicle in the GPS space to a vehicle movement classifier to yield a movement class prediction and a class confidence score, and evaluating the class confidence score against a predetermined threshold based on the movement class prediction to determine whether the vehicle was moving when located in the bus lane.

The invention relates to a bus passenger flow prediction method based on multi-scale space-time deep learning, which belongs to the field of traffic information big data, is realized by a bus data collection and processing system, and comprises the following steps: S1, generating a bus arrival and departure time table and a passenger getting-on and getting-off clock-in data table; s2, generating a spatial-temporal feature vector matrix; s3, introducing external factor information corresponding to the site, and coding by adopting one-hot coding to form time-space sequence data; s4, a bus passenger flow prediction model based on multi-scale space-time deep learning is constructed and trained; and S5, performing prediction according to a real-time request of a user or a bus dispatching center, and outputting a passenger flow prediction value of each station in a future time period. According to the method, complex passenger flow changes can be accurately captured, the prediction accuracy is improved, the adaptability and generalization ability of the model are enhanced, scientific data support is provided for bus dispatching optimization, route planning and transport capacity configuration, and the operation efficiency and service level of a bus system are improved.

The invention discloses an automatic driving bus collaborative formation dynamic scheduling method, which comprises the following steps of: after introducing a vehicle formation operation, constructing a vehicle energy consumption model based on vehicle specific power, and measuring the vehicle operation energy consumption of an automatic driving bus formation in any formation mode; in an intelligent network connection environment, based on a rolling time domain optimization framework, a Markov decision process model is constructed, and an automatic driving bus collaborative formation dynamic scheduling problem is explained; defining a state variable and a decision variable of the Markov decision process model, and determining constraint conditions of related variables and a target function of the Markov decision process model; the decision space of the Markov decision process model is reduced, and an approximate dynamic programming algorithm is used to solve the automatic driving bus collaborative formation scheduling problem; a multi-step look-ahead strategy based on dynamic planning is provided, the convergence efficiency of an approximate dynamic planning algorithm is improved, and an automatic driving bus collaborative formation optimization scheduling scheme is obtained. And the automatic driving bus capacity utilization rate is improved.

The embodiment of the invention provides a training method of a signal lamp control model, a signal lamp control method, a device, equipment, a medium and a product, and relates to the technical field of urban road traffic. The method comprises the following steps: acquiring a plurality of first traffic data, constructing a state characterization matrix and a reward function according to the plurality of first traffic data, and performing model training according to the plurality of first traffic data, the state characterization matrix and the reward function to obtain a signal lamp control model. According to the method, in a model training process, a state representation matrix is utilized to dynamically quantify passing priorities of buses in all directions, and meanwhile, a reward function is utilized to guide the model training process, so that a signal lamp control model obtained through training can recognize and preferentially respond to bus passing demands, and signal lamp actions are dynamically adjusted; therefore, the collaborative optimization of multi-direction bus priority requests in a complex scene is realized, and the coordination capability and response efficiency of a bus priority system in a complex urban traffic environment are improved.

The invention provides a demand response bus intelligent scheduling method and system driven by real-time data. A demand response bus intelligent scheduling method driven by real-time data comprises the following steps: S1, collecting and fusing multi-source data in real time, and constructing a dynamic data set; s2, carrying out short-time demand prediction and clustering analysis based on LSTM and DBSCAN; s3, dynamic service area division and greedy algorithm line generation; s4, elastic vehicle scheduling based on combination of mixed integer programming and a simulated annealing algorithm; s5, dynamically adjusting the real-time ticket price driven by the supply and demand game model; s6, passenger personalized service matching is achieved through a collaborative filtering algorithm; s7, performing multi-target simulation verification on the digital twin platform; and S8, reinforcing a learning-driven dynamic feedback optimization mechanism. According to the real-time data-driven demand response bus intelligent scheduling method and system provided by the invention, the operation efficiency, the passenger satisfaction degree and the enterprise benefit of the demand response type customized bus can be remarkably improved, and the method and the system are suitable for construction of an urban intelligent bus system and have remarkable advantages.

The invention relates to an intelligent bus combined scheduling method and device and a storage medium, and relates to the field of scheduling optimization. Bus card swiping records are called to establish a card swiping station feature set, navigation station triggering is established to carry out navigation record matching analysis, and a space-time popularity prediction map is established; constructing a multi-source traffic flow data set to perform road congestion state prediction and construct a region-level road congestion state diagram; establishing a station passenger flow demand, route travel time and a congestion state according to the space-time popularity prediction map and the region-level road congestion state map; a multi-target scheduling channel is configured to take station passenger flow demands, route travel time and congestion states as input data, and route bus scheduling optimization is carried out through a passenger waiting time target, a scheduling cost target, a vehicle utilization rate target and a congestion influence target. The technical problems of insufficient data utilization, limited prediction precision and lack of flexibility of a scheduling strategy in bus scheduling are solved.

The invention relates to the technical field of urban traffic management and carbon emission reduction accounting, and provides a bus travel carbon emission reduction accounting method fused with Beidou spatio-temporal data, which comprises the following steps: acquiring spatio-temporal trajectory data based on a Beidou satellite navigation system, and constructing a passenger-trajectory-station three-dimensional correlation model for passenger flow analysis; constructing a passenger transfer analysis system based on the passenger flow analysis result; constructing a dynamic'perception-prediction-optimization 'dynamic scheduling optimization system for passenger flow detail analysis on the basis of space-time trajectory data and a reinforcement learning algorithm; and constructing a mileage-frequency distribution model and a station efficiency evaluation model based on the spatio-temporal trajectory data, respectively generating a trajectory matching optimization strategy and a differentiated station optimization scheme, and realizing adaptive optimization of the public transportation system under dynamic demand change. Compared with a traditional static data dependence method, the method achieves the dynamic and fine-grained monitoring of the operation process of the public transportation system, and effectively improves the timeliness and integrity of data.

The invention discloses a bus carbon efficiency key causal chain identification method, electronic equipment and a medium, and the method comprises the steps: constructing a line-level bus carbon efficiency variable set which comprises a control variable, a covariant and unit passenger mileage carbon emission; the control variable is a bus operation strategy; the covariable is a bus carbon efficiency influence factor; assuming that the line-level bus carbon efficiency variable set obeys a directed acyclic graph structure formed by linear equations, and solving a causal effect matrix; establishing a bus carbon efficiency causal directed graph by taking a line-level bus carbon efficiency variable as a node and a causal effect as an edge weight; and performing post-nonlinear causal relationship test and conditional independence causal test on the bus carbon efficiency causal digraph to obtain an optimized bus carbon efficiency causal digraph, thereby obtaining a bus carbon efficiency key causal link.

The invention discloses an urban passenger transport resource allocation method and system based on Internet big data, and belongs to the field of intelligent transportation. A bus station service matching model is constructed, service areas are divided through bus station space distribution, service radius and travel data, travel demand intensity and station matching degree are evaluated, and the service efficiency is improved. The method comprises the following steps: establishing an accessibility evaluation model, evaluating the accessibility level of each station through destination information around the station, establishing a bus and subway collaborative evaluation model, evaluating the substitution effect of the subway on the bus and the collaborative degree of the bus and the subway through transfer data, and establishing a station residence time optimization model. The matching degree, the accessibility, the substitution degree and the cooperation degree are imported into a station residence time optimization model to adjust the residence time of the bus at each station, a bus shift dynamic adjustment model is constructed, weather and event external factors are imported into the bus shift dynamic adjustment model to predict passenger flow changes and optimize shift setting, and the bus shift dynamic adjustment model is constructed. And the bus dispatching efficiency and the operation stability are improved.

The invention belongs to the field of bus dispatching optimization, and particularly relates to a modular bus dispatching optimization method for a bus corridor, in particular to a modular bus dispatching optimization method for the bus corridor. The objective of the invention is to solve the problems of low operation efficiency, long passenger transfer waiting time, long passenger in-bus time and high operation cost of a bus company of an existing bus system. The invention discloses a modularized bus scheduling optimization method for a bus corridor. The method comprises the following steps: 1, collecting basic information of bus stations, configuring modularized vehicles, configuring modularized bus shifts, and collecting passenger travel request data; 2, defining optimization variables; 3, judging a transfer strategy, and calculating the number of transferred passengers based on the transfer strategy; 4, splitting and combining the vehicle to complete passenger transfer, and updating shift information based on a passenger transfer result; 5, establishing a modularized bus scheduling optimization model; and 6, solving the modularized bus scheduling optimization model, and outputting an optimal scheduling scheme.

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent bus kiosk monitoring method and system with an abnormal behavior intelligent monitoring function. The method comprises the following steps: acquiring multi-source monitoring data of a target bus kiosk, and based on the multi-source monitoring data, performing character and article linkage behavior analysis on the multi-source monitoring data to generate an entity interaction feature set; extracting a timestamp sequence and a space coordinate sequence based on the entity interaction feature set, and generating a space-time parameter set based on the timestamp sequence and the space coordinate sequence; based on the entity interaction feature set, according to the space-time parameter set, analyzing whether the current entity interaction feature has an abnormal behavior in the corresponding bus kiosk space-time scene, and if yes, extracting abnormal behavior information; and according to the abnormal behavior information, determining and outputting an abnormal early warning signal. According to the method, the abnormal characteristics of the behaviors under the bus kiosk are analyzed in a multi-dimensional mode, safety early warning is achieved, and the public traffic order is guaranteed.

The invention discloses a multi-unmanned aerial vehicle distribution and bus charging combined path optimization method, which aims at minimizing task total time, constructs a mixed integer programming model based on a space-time network, and comprehensively considers unmanned aerial vehicle electric quantity constraint, demand point full coverage, bus time window and charging pile number limitation. An original model is decoupled to limit a main problem and a sub-problem by adopting branch pricing and a Dantzigzag-Wolfe decomposition theory, the main problem deals with demand coverage and charging resource allocation, and modeling is a set coverage problem; for single-machine path generation, the latter is modeled as a resource-constrained and replenishable shortest path problem with a time window dependent feature. A multi-unmanned aerial vehicle initial solution is constructed through a random generation method, a dual variable is iteratively solved after a main problem is initialized, a sub-problem is solved based on a multi-label algorithm, and global optimal solution search is realized in combination with a column generation mechanism and a branch strategy. According to the invention, through joint optimization of the departure time and path planning of the unmanned aerial vehicle, the collaborative optimization problem of distribution and charging is accurately solved.

The invention relates to a modular bus jump and relocation scheduling method considering stop time, and belongs to the field of urban public transport operation scheduling. The method comprises the following steps: acquiring geographical location information of a certain conventional bus route and a station and travel data information of bus passengers, and determining the number of passengers getting on and off the bus at the station; the method comprises the following steps: defining a jump station and relocation coupling operation as a decision variable, and establishing a relational expression between the stop time of a modular bus at a stop and the number of passengers getting on and off according to a modular bus single-door design characteristic and a bus combination form; taking the minimum sum of the passenger travel time cost and the vehicle operation cost as an objective function, setting a modular bus flow constraint and an operation time constraint, and constructing a modular bus scheduling optimization model; and solving to obtain an optimal modularized bus jump station and relocation scheduling scheme under the passenger demand of the corresponding line. According to the invention, for a bus route scene where part of bus stations are relatively high in load, modular bus operation scheduling which efficiently utilizes bus transport capacity resources is realized.

The invention relates to the technical field of image or video recognition or understanding, and discloses a bus driver driving state evaluation method, which comprises the following steps: acquiring a face image, an electrocardiosignal and voice audio data of a bus driver, performing timestamp alignment and preprocessing, and constructing a multi-mode driving state data set; extracting multi-modal features of the collected data, and mapping the multi-modal features to a unified feature space; fusing multi-modal features through a cross-modal attention mechanism, dynamically adjusting attention weight based on an emotional change difficulty index, and extracting emotional change key features; and predicting a two-dimensional continuous emotion value of the driver by using the fusion features, calculating a long-time-sequence emotion driving risk score based on an emotion stimulation dynamic model, and performing evaluation and early warning of a driving state. The problems of single-mode analysis, lack of long-period early warning and driving state static recognition in the prior art are solved, and the purposes of accurate evaluation, high safety, multi-mode fusion and long-time-sequence prediction are achieved.

The invention discloses a subway passenger flow prediction method, system and device considering subway-bus transfer, and a medium, and the method comprises the following steps: obtaining the historical passenger flow of a subway station in a certain region within a period of time and the historical passenger flow of a corresponding bus station around each subway station, and forming a data set; dividing the data set into a training set and a test set according to a time sequence; constructing a dynamic space-time decomposition and fusion network, wherein the dynamic space-time decomposition and fusion network comprises a time sequence decomposition module, a dynamic graph convolution module, a gated cross-modal attention fusion module and a dual-path space-time adaptive fusion module; and training the dynamic space-time decomposition and fusion network by using the training set and the test set, and predicting the subway passenger flow volume by using the optimized dynamic space-time decomposition and fusion network by using a mean absolute error, a root-mean-square error and a weighted mean absolute error percentage as performance evaluation indexes. The prediction precision is improved, and the practicability and generalization ability of the model are enhanced.

The invention discloses a bus dynamic scheduling method and system based on vehicle management, and the method comprises the steps: building a scheduling environment simulation system through collecting the historical data and real-time data of the operation of an electric bus, automatically generating passenger travel information based on bus line data and historical passenger getting-on data, carrying out the modeling of the bus operation as a dynamic discrete process, and carrying out the real-time scheduling of the electric bus. Constraints such as vehicle availability and charging limitation are introduced, and a bus operation simulation environment close to reality is constructed; establishing a Markov decision process based on a scheduling simulation environment, and definitely defining vehicle availability management parameters in a state space and a reward function; a Rainbow DQN model is adopted for training, a scheduling strategy is continuously optimized according to environment feedback, a dynamic scheduling scheme is generated by utilizing the model obtained through training according to real-time data reasoning, the travel requirements of passengers and the operation benefits of a bus company are considered on the premise that the vehicle availability is met, and intelligent scheduling decision support is provided for management personnel.

The invention provides a bus route mining method and device, and relates to the technical field of artificial intelligence, in particular to the technical field of intelligent traffic and deep learning. A specific embodiment of the method comprises the steps of matching a user track of a user in a bus running period with a bus route to obtain a matched track point, an unmatched track point and a matched bus route; determining a maximum matching track segment based on the matching track points and the mismatching track points; performing space-time matching on the maximum matching track segment and the real-time bus track of the matched bus route to obtain a space-time matching degree; determining a confidence coefficient based on the real-time bus track with the highest space-time matching degree; and determining the bus route corresponding to the real-time bus track with the highest time-space matching degree when the user takes the bus in response to the condition that the confidence meets a preset condition.

The invention belongs to the field of intelligent transportation, and discloses a bus lane sharing control method based on the optimal bus speed, which comprises the following steps: S1, bus position monitoring and planning speed acquisition; s2, predicting predicted arrival time and stop time; s3, calculating the time for closing the bus lane; s4, calculating the time of reopening the bus lane; and S5, sending an instruction to the lane signal lamp. According to the method, conflicts between the buses and the social vehicles are reduced through reasonable space-time resource dynamic allocation, so that the influence on the passing efficiency of the social vehicles is reduced while the priority passing of the buses is ensured. According to the method, the passing efficiency and the punctuality rate of the bus can be improved, the reliability of a bus system is improved, the safety and smoothness of an urban traffic system can be ensured, energy consumption and tail gas emission are reduced, and meanwhile the riding experience and satisfaction degree of passengers are improved.

The invention discloses a multi-line efficient transfer bus planning method based on deep reinforcement learning, and the method comprises the following steps: S1, obtaining the bus travel demand data of a traffic zone of a research region, and obtaining the bus passenger flow of a road segment of the research region; s2, according to the bus passenger flow volume of the road section of the research area in the step S1, a frequent item set mining method is adopted to carry out bus route planning, and a bus route sequence of the research area is obtained; s3, according to the bus route sequence of the research area in the step S2, bus route planning is converted into a Markov decision process, and a bus departure time table scheduling model is constructed; s4, according to the bus departure time table scheduling model constructed in the step S3, searching an optimal bus departure time by adopting a dual deep Q network method; according to the method, the bus travel efficiency is improved, the problem of low bus resource allocation is solved, and flexible and efficient allocation of bus resources is realized.

The invention belongs to the technical field of urban public transport management, and particularly relates to an electric public transport scheduling and charging scheduling method considering source-load double-side fluctuation. The objective of the invention is to solve the problems that a public transportation system has source-load double-side fluctuation due to the intermittency of existing source-side photovoltaic power generation and the randomness of load-side public transportation operation energy consumption, so that the charging demand and the power supply capacity in part of time periods are not matched, the operation task cannot be completed in time, and the energy storage utilization efficiency is low. The invention provides an electric bus scheduling and charging scheduling method considering source-load double-side fluctuation. The method comprises the steps of calculating bus battery remaining capacity based on an optimization time period; calculating the remaining capacity of the energy storage battery based on the remaining capacity of the bus battery; based on the bus battery remaining capacity and the energy storage battery remaining capacity, constructing a chance constraint planning model; and solving the opportunity constraint planning model to obtain an optimal feasible solution which comprises an electric bus scheduling and charging scheduling scheme.

The invention belongs to the technical field of bus operation management, and discloses a bus operation digital management platform based on a low-code platform. Comprising a monitoring period division module, a vehicle driving data monitoring module, a running vehicle behavior analysis module, a historical personnel data monitoring module, a real-time personnel data monitoring module, a running vehicle scheduling analysis module, a vehicle scheduling effect evaluation module and a data storage library. According to the invention, based on the historical passenger transport demand condition and the real-time passenger transport demand condition of each bus station in the current monitoring time period, whether flexible scheduling vehicles need to be added to each bus station in the current monitoring time period is judged, the passenger flow rule can be accurately informed, reasonable putting of the vehicles is realized, and the resource utilization rate is improved. According to the invention, the specific scheduling mode is further identified after whether the flexible scheduling vehicle needs to be added at each bus station is judged, so that the resource utilization efficiency is practically improved, the endless loss of the transport capacity is avoided, and the vehicle delivery is perfectly matched with the passenger flow distribution.

The invention relates to an intelligent scheduling decision support method and system for a community bus special line, and solves the problems of unreasonable transport capacity distribution and insufficient key travel demand guarantee caused by incapability of distinguishing travel purposes in an existing scheduling method, and the method comprises the steps: firstly, integrating card swiping, reservation and static data to construct a travel demand data set; secondly, through a clustering algorithm, identifying travel destination modes and extracting multi-modal features, generating a prediction report containing a passenger flow thermodynamic diagram and a space-time probability matrix through prediction of an adaptive multi-head attention model, and further calculating service priorities of the modes; then, a decision framework coordinated by route planning and station jump decision intelligent agents is constructed, a global reward function is endowed with differentiated weights according to priorities, and a dynamic scheduling scheme is jointly generated; the method has the following effects: community bus accurate scheduling based on travel purpose identification and priority guarantee is realized, and transport capacity sharing is upgraded to on-demand intelligent distribution.

The utility model relates to a lamp box structure at the top of a bus station. The lamp box structure comprises the bus station and a lamp box, a person screws one screw into each of the threaded holes in the four corners of the square groove, at the moment, the upper ends of the screws make contact with the driving rods, the driving rods drive the movable columns to move in the inclined holes through the U-shaped plates, the driving plates drive the telescopic rods to move outwards, the telescopic springs retract, and the inner ends of the telescopic rods move out of the clamping holes; when the telescopic rods in the clamping assemblies at the four corners move out of the clamping holes in the four corners of the lamp box respectively, the lamp box can be taken down from the square groove, then a new lamp box is placed in the square groove, at the moment, a person moves the screws at the four corners out of the threaded holes respectively, the telescopic springs stretch to drive the rods to move into the threaded holes, and then the lamp box is taken down from the square groove. Meanwhile, the telescopic rod moves inwards, the outer end of the telescopic rod drives the driving plate to move, the inner end of the telescopic rod enters the clamping hole to be clamped, the lamp box is clamped and fixed in the square groove, and therefore the lamp box can be replaced very conveniently.

The invention provides a bus dynamic multi-objective optimization scheduling method based on reinforcement learning. The method comprises the following steps: firstly, initializing a motorcade scale and a one-way set, and segmenting an operation time period into a plurality of rescheduling stages; constructing a reinforcement learning model of the current rescheduling stage; sequentially carrying out decision making on each decision point in the current rescheduling stage by utilizing a reinforcement learning model to form an optimal solution of the current rescheduling stage; repeating the decision-making process of the current rescheduling stage for multiple times to form an optimal solution set of the current stage; and sequentially processing each rescheduling stage until all rescheduling stages are processed. Through the scheme provided by the invention, the vehicle scheduling scheme can be more suitable for a real operation environment, conflicts between targets are fully considered, and the operation level and the service quality of buses are improved.

The invention discloses a bus shift extraction optimization method based on multi-dimensional score driving, and relates to the field of urban public traffic scheduling management, and the method comprises the following steps: initializing shift extraction parameters, determining a shift extraction target, defining time period features, and setting a score weight and a penalty coefficient; constructing a shift extraction optimization mathematical model, including defining decision variables and establishing an objective function and constraint conditions thereof; respectively constructing a multi-dimensional scoring system and a constraint verification strategy by utilizing the objective function and the constraint conditions thereof; generating candidate shift drawing schemes, calculating a comprehensive score and selecting an optimal shift drawing scheme; performing iteration processing on the optimal shift extraction scheme in combination with loop control and a scheme rollback strategy to obtain a final shift extraction scheme; and verifying and counting the final shift drawing scheme, and generating a corresponding shift drawing report. On the premise that operation balance and service integrity are ensured, global optimal selection of the shift drawing scheme is achieved, and the intelligent level and operation efficiency of bus dispatching are remarkably improved.

The invention discloses a collaborative operation optimization method for a bus and subway integrated network. The method comprises the following steps: S1, carrying out collection and standardization processing on topological structure data, train operation timetable data and passenger travel demand data; s2, completing the construction of a multi-layer coupling space-time network; s3, K short path searching is executed, and a passenger reachable path set is generated; s4, carrying out reachability verification on the passenger path selection set under a train operation rule; s5, a scheduling scheme is formed based on the distribution probability output by the Logit model; and S6, inputting the time sequence operation data into the improved Mamba network, performing constraint check and correction on the line departure logarithm and the departure moment of the first station, and forming an evaluation index for optimizing the objective function. According to the method, collaborative optimization of the train operation time sequence and the passenger travel demand under the bus and subway integrated line network is achieved, and the method is suitable for intelligent scheduling and dynamic passenger flow regulation and control scenes of a large-scale urban rail transit system.

The express cabinet comprises an unmanned aerial vehicle intelligent connection express cabinet body arranged at a bus station, an unmanned aerial vehicle take-off and landing platform is arranged on the unmanned aerial vehicle intelligent connection express cabinet body, an intelligent express delivery unmanned aerial vehicle is arranged at the top of the unmanned aerial vehicle take-off and landing platform, and a parcel distribution mechanism is arranged in the unmanned aerial vehicle intelligent connection express cabinet body. The unmanned aerial vehicle take-off and landing platform is connected to the top of the unmanned aerial vehicle intelligent connection express delivery cabinet through a connecting piece and used for allowing an unmanned aerial vehicle to stay and work, and the unmanned aerial vehicle intelligent connection express delivery cabinet is arranged at the position of a bus station. The surface of the unmanned aerial vehicle intelligent connection express cabinet is provided with the bus information electronic display board, so that a bus station is formed, and the mode of combining the unmanned aerial vehicle intelligent connection express cabinet and the bus station is integrated, so that public resources are integrated and utilized.