32 results about "Urban bus" patented technology

The invention discloses a method for identifying illegal behavior of attracting passengers outside a passenger station based on a machine learning algorithm, and relates to the technical field of vehicle management and control, and the method comprises the following identification steps: S1, data collection and preprocessing: collecting historical driving data of inter-provincial passenger vehicles, urban bus station data, urban subway station data, regular bus legal attracting passenger station data and historical punishment data, the data are preprocessed, and a vehicle specified route is generated through clustering and deviation rectification; s2, feature selection and extraction; s3, abnormal parking point location identification; and S4, passenger getting-on and getting-off action recognition. According to the invention, based on multi-source information comprehensive utilization of vehicle tracks and video data, all-around, automatic and intelligent analysis and supervision of illegal activities of attracting customers outside a station are realized, and the supervision range and efficiency are improved; by combining the vehicle parking behavior, the parking duration, the parking position and the pedestrian action characteristics, the misjudgment rate is reduced.

An ozone purification catalyst, and a preparation method therefor and an application thereof are provided. The catalyst coating uses macroporous, high specific surface and CeO2 and / or La2O3 modified Al2O3 as the carrier material, and Mn and / or Pd as the active component. The preparation method is to prepare the Al2O3-based material by a sol-gel method, and then to load the active components on the carrier material, and to dry, calcinate and solidify to obtain the ozone purification catalyst. The catalysts as prepared shows a fast and efficient purification of ozone. The complete conversion temperature covers a wide range of temperature. The catalyst has excellent texture performance, high specific surface area and large pore volume, which is beneficial to ozone purification when the car is running at high speed. The particle sizes and colors of the catalyst can be modified according to various requirements. According to the actual application, it can be coated on the radiator fins of automobile water tanks, and any place where coating is allowed in public areas such as urban bus stations, stop signs, kiosks, roadside guardrails, or exterior walls of buildings that is in contact with outdoor air.

The application discloses a kind of urban bus operation optimization control methods and systems of fusion V2X information, it is related to intelligent traffic control technical field, including, based on structured feature vector group calculation space-time resource competition degree and transfer urgency coefficient;Based on space-time resource competition degree and transfer urgency coefficient generates collaborative trigger sign and calculates time deviation value, obtains collaborative decision group;According to control instruction set, adjust vehicle speed, according to torque distribution scheme, control motor output and regenerative braking proportion, when communication is abnormal, the position of urban bus is corrected by map topological constraint;By receiving multi-source heterogeneous data and integrating into structured feature vector group, comprehensive perception and standardization processing to traffic environment and vehicle state are realized, high-quality data basis is provided;The pertinence and response capability of collaborative control are improved, traffic congestion is relieved and passenger travel experience is improved.

The present invention provides a bus coordination evaluation device and system based on travel data and GIS clustering. The system consists of a bus data system, a coordination analysis system, a GIS cluster analysis system, a display system, an information transmission device, input and output equipment, and an intelligent mobile terminal. The system is based on big data fusion mining and processing technology, can perform quantitative evaluation of urban bus lines, realize rapid evaluation and analysis of public transportation networks, and provide participatory decision-making for the optimization of urban public transportation networks.

The invention relates to an urban bus lane operation effect evaluation method based on multi-dimensional indexes, and relates to the field of public traffic evaluation and optimization. According to the technical scheme, the scale and qualified rate two-dimensional evaluation is integrated, the scale index is based on the proportion of the mileage of the special lane to the total length of the bus line network, the qualified rate is calculated through rationality scoring of the special lanes one by one, and the number quality focuses on the overall treatment effect to avoid local one-sided pursuit; the evaluation of a single special lane integrates the dimensions such as bus passing demand, bus operation efficiency, bus priority effect and lane allocation balance, selects the indexes such as peak bus arrival amount and speed ratio, advocates bus priority and pays attention to social vehicle rights and interests, and reduces the waste of passing resources by monitoring the vacancy of the special lane and the congestion of other lanes. The road-section and direction-division calculation mode can evaluate the setting conditions of all directions of all road sections respectively, and accurate positioning of weak points is convenient for fine adjustment. The method effectively improves the running efficiency of the special road, and optimizes the road resource allocation.

The application relates to the technical field of urban bus line network and timetable integration optimization, and particularly discloses a bus line network and timetable integration optimization method and device based on graph reinforcement learning. The method is used for generating a bus line network structure and a timetable in a multi-mode public transport network containing BRT, subway and shared bicycles. Firstly, a multi-mode public transport road network is constructed, and passenger flow demand data is obtained; a bus line network design and a synchronous timetable compilation process are modeled as a Markov decision process; according to a current bus line network state, the road network is reconstructed into a heterogeneous graph containing time sequence evolution arcs and spatial topology arcs, and a double attention graph neural network is used to extract candidate node features; according to the node features, an action is selected through an action network, and the bus line network state is updated; the node feature extraction, the action selection are repeated until a termination condition is met, and the bus line network structure and the line timetable are output. The application can generate a bus line network and timetable which are more coordinated with other public transport modes, and reduce the operation cost of bus enterprises and the travel and transfer time of passengers.

The application provides a kind of intelligent network connection vehicle (Connected Automated Vehicle, hereinafter referred to as CAV) trajectory optimization method for driving on city bus lane. In order to improve the mixed traffic flow state of CAV and manually driven vehicle, and in order to effectively improve the operation efficiency of CAV, the operation gap of bus lane is used to improve the operation speed of CAV, so as to realize the separation of CAV and manually driven vehicle. In the trajectory planning process, firstly, the time when CAV arrives is obtained, based on the traffic state at this time, after considering the influence of the empty capacity on the city bus lane and the signal, whether CAV can run on the bus lane is reasonably arranged, and then the trajectory of CAV running on the bus lane is solved and optimized. The method of the application is a feasible and quite economical planning method, which not only ensures the independent operation of CAV to a certain extent, but also utilizes the idle part of traffic resources.

This invention discloses a multi-target tracking algorithm for automated urban buses based on a motion model, belonging to the field of intelligent vehicle technology. It mainly includes an initialization module, a prediction module, and a trajectory association module. The initialization module reads the detection file to obtain the detection box information of each target in the current frame, and divides the detection boxes into high-resolution and low-resolution detection boxes according to a threshold, sending them to the trajectory association module. The prediction module includes setting corresponding adaptive state transition matrices for the trajectories in the previous frame, and obtaining the predicted bounding boxes and covariance matrices of the trajectories in the previous frame in the current frame through Kalman filtering. The trajectory association module performs intersection-union (IUU) calculations on the corrected predicted bounding boxes of the current frame, the high-resolution detection boxes, and the low-resolution detection boxes. This invention improves the tracking performance of multi-target tracking algorithms on automated urban buses by combining Kalman filtering with adaptive state transition matrices and camera motion compensation.

The application discloses a bus accurate in-and-out station operation algorithm based on intelligent network connection technology application, in order to solve the problem that other vehicles can affect the bus arrival time, the bus flow data between two adjacent bus stations is collected through multiple data sources, the influence of other vehicles on the bus driving time on the road is analyzed through the bus flow data, the bus flow on the road is collected through multiple bus lines, the influence of other vehicles on the bus arrival time is more accurately analyzed, the bus in-and-out station time analysis is accurate, and the service level of the urban bus is improved.

This application relates to the field of vehicle control technology, and provides a method and system for tracking the trajectory of urban buses based on BeiDou positioning. The method includes: acquiring vehicle driving motion information, vehicle BeiDou positioning information, and vehicle IMU positioning information; identifying multiple candidate road segments from preset map data based on the vehicle's BeiDou positioning information and IMU positioning information, and acquiring road geometric feature information for each candidate road segment; determining the vehicle's expected road segment from the multiple candidate road segments based on the vehicle's driving motion information and the road geometric feature information of the multiple candidate road segments, and determining the vehicle's drive control command based on the road geometric feature information of the expected road segment. This solution can improve the accuracy of vehicle control.

The present invention discloses a battery scheduling method and computing device for a city bus exchange station cluster, comprising: establishing an electric bus exchange demand model according to the electric bus exchange characteristics; determining H it , where H it represents the number of battery swapping demands at battery swap station i in time period t; a day-ahead scheduling model for battery charging and discharging of battery swap station clusters is established, whose decision variables include the number of batteries C being charged at battery swap station i in time period t it and the number of batteries being discharged, D it ; According to the constraints, the day-ahead scheduling model of battery charging and discharging of the battery swap station cluster is solved, and it is found that when the scheduling cost of the battery swap station cluster is the lowest and the fitting degree between the net load of the battery swap station cluster and the green power output is the best, C it and D it The optimal solution is obtained and used as the battery scheduling solution for the battery swap station cluster.

The present invention provides a passenger train capable of being freely assembled and dispersed independently, comprising a plurality of passenger cars capable of being freely assembled and dispersed independently. When the plurality of passenger cars are freely assembled, the plurality of passenger cars form a passenger train, any one of which can serve as the main car, and the others are followers. The carriages of the passenger train are interconnected and provided with a service corridor, and adjacent passenger cars are connected with each other by weak traction. When the plurality of passenger cars are dispersed independently, each passenger car operates independently. An articulation mechanism, a passage mechanism, and a connecting shed are provided between the rear end of the front car and the head end of the rear car of the passenger train to connect the adjacent passenger cars. The passenger train provided by the present invention can be arbitrarily disassembled and combined, that is, it can be freely assembled and operated by multiple customers, or it can be dispersed and operated independently. It does not require additional investment under the existing road infrastructure or on existing BRT roads. It solves the problem of urban bus operation rates during peak and off-peak periods, improves efficiency, saves resources, and reduces costs.

The application discloses a bus line network optimization method, device and equipment, and relates to the technical field of traffic scheduling. A line network optimization model is constructed based on an ant colony algorithm, a bus line network diagram and an OD matrix are input into the line network optimization model for iteration until a preset iteration number is reached, and an optimal path is obtained; an optimization target module is used for defining an optimization target function of the line network optimization model, initializing ant colony parameters and an importance factor according to the bus line network diagram and the OD matrix; a path selection module is used for constructing a path selection strategy according to the ant colony parameters to select a path, and a first path is obtained; a crossover and mutation module is used for performing crossover and mutation processing on the first path after punishment processing, obtaining a second path, and evaluating the second path to determine a first optimized path. The method can dynamically adjust the line network optimization model, accelerate the convergence speed and improve the global search capability. The operation efficiency of the bus line network diagram is optimized, and the operation demand of urban buses is met.

The invention discloses an optimal touring path optimization selection method for an urban landmark group based on behavior preference, and the method comprises the steps: obtaining urban landmark positions and urban road network data, calculating the shortest touring path in all touring sequences, and taking the shortest touring path as an alternative path; the selection identification degree of the alternative paths is calculated, and sightseeing visual angle attraction evaluation is carried out; acquiring urban bus station data, and carrying out bus connection accessibility evaluation on alternative paths; urban street building distribution and function attribute data are obtained, and interface function richness evaluation of alternative paths is carried out; acquiring point density data of the urban population, and evaluating the visit space vitality of the alternative paths; and finally, on the basis of user behavior preference requirements, comprehensive weighted evaluation is carried out from four dimensions, and the path with the highest comprehensive score is determined as the optimal recommended path. According to the invention, a more comprehensive and personalized urban landmark group touring path can be generated.

The invention discloses a reinforcement learning-based data-driven bus card getting-on station intelligent inference method, and relates to the technical field of intelligent traffic. According to the method, only card swiping records of a one-ticket system bus AFC system are utilized, and passenger getting-on stations are accurately calculated through the following two stages: in the first stage, self-adaptive clustering is carried out on a single-day card swiping time sequence based on deep reinforcement learning (DQN), and a preliminary getting-on station of each time cluster is obtained in combination with travel direction inference; in the second stage, the time-space stability of multi-day card swiping of the bus commuting passengers is screened, the preliminary result is corrected according to the occurrence frequency, and accidental misjudgment is eliminated. The method does not need a GPS, does not need an additional sensor, completely depends on card swiping data, is suitable for all urban buses provided with a one-ticket AFC system, is wide in coverage range, low in data requirement and low in calculation cost, and can remarkably improve the accuracy and the cross-day consistency of getting-on station calculation.

The utility model discloses a 7-meter-level short rear overhang parallel shaft electric drive low-entrance city bus chassis, which relates to the technical field of city buses, and comprises a chassis frame and a floor framework main body, the chassis frame comprises a low floor front axle, a frame front overhang section, a long wheelbase frame middle section, an ultra-short rear overhang section and a frame rear axle, the full-air spring suspension system has the advantages that the full-air spring suspension system with two front parts and four rear parts is adopted, the offset frequency is lower, the smoothness is better, the problems that a conventional composite suspension is poor in smoothness and large in vibration are solved, and the full-air spring suspension system with the two front parts and the four rear parts is adopted. Through the arrangement structure of the low-floor front axle, the long axle distance and the ultra-short rear overhang, the barrier-free passage from the front door to the rear door is achieved, getting on and off are convenient, and passengers flow smoothly and safely.

The invention provides an urban bus health degree evaluation system based on big data, which constructs a data-driven health degree evaluation model by extracting bus operation multi-source data, and realizes model calculation and evaluation. The method specifically comprises three cores of bus big data extraction and preprocessing, health degree evaluation model construction and model calculation and comprehensive evaluation. In order to solve the problems that an existing vehicle health degree evaluation method is high in theorization and insufficient in practicability, a health degree evaluation model which is close to actual business and can be expanded is constructed on the basis of big data analysis. The model can efficiently process mass data of a city-level public transportation system, realizes dynamic quantitative evaluation of the vehicle health state, and is beneficial to providing scientific vehicle operation and maintenance decision support for enterprise managers.

The application discloses a kind of urban public transport corridor design methods based on multi-objective optimization model, this method can fuse city land use POI (Point of Interest, point of interest) data, resident trip OD (Origin-Destination, origin-destination) data and corridor construction cost, to realize the line selection and level planning of multi-angle consideration city public transport corridor.The present application is based on multi-objective optimization theory, forms the mathematical model of public transport corridor design, compared with the experience judgment method and two-step clustering method widely used at present, the method can be based on existing traffic demand and future development trend, obtain more accurate and reasonable public transport corridor design result, greatly improve the efficiency of urban public transport corridor planning, reduce the time and cost of artificial design.

The application discloses a kind of urban public transport network optimization method considering scheduling, comprising: processing bus card data, vehicle shift data, line site data, analyze the influencing factors in the process of passenger line;For the influence factor analysis of passenger riding habit, vehicle shift, analyze the influence relationship of the selection of each type of passenger line and riding habit, respectively with the same bus station OD inter-route selection proportion and Person correlation coefficient define its correlation degree, obtain the Pearson correlation coefficient of the selection of different types of passengers and shift;Through the actual-virtual bus network construction mode, construct line and scheduling synchronous optimization model, optimize the output optimization result by the line site layout and vehicle scheduling synchronous optimization by the constructed model.The application considers the influence of vehicle scheduling in the process of public transport network optimization, optimizes the layout of public transport network and vehicle scheduling simultaneously;It is convenient for passenger travel, reduces travel cost.

The invention relates to a hybrid electric vehicle charging scheduling system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic systems and electric vehicle charging scheduling. The method comprises the following steps: firstly, constructing a hybrid charging framework integrating a fixed charging station and a mobile energy distributor; secondly, establishing a refined traffic and energy consumption model considering vehicle following behaviors, road congestion and urban bus type MED operation conditions; then, a multi-agent near-end strategy optimization algorithm is adopted, a central controller serves as a global coordinator to be responsible for task scheduling and load balancing, and multi-agent combined optimization of electric vehicle charging mode selection and a driving path is achieved; and finally, converting an original complex mixed integer nonlinear optimization problem into an intelligent decision problem which can be solved online through a multi-agent reinforcement learning framework which is trained in a centralized manner and executed in a decentralized manner. According to the invention, the charging time, the driving energy consumption, the driving distance and the charging cost of the electric vehicle in the hybrid charging system can be effectively reduced.

The invention relates to an urban bus priority active management and control method and system based on vehicle and road cloud integration. The method comprises the following steps: acquiring road network data in a research area; dividing a core area boundary and a core area of the research area; dividing a peripheral traffic control sub-area and a peripheral critical line; constructing a three-dimensional macroscopic fundamental diagram model, and determining a traffic flow control threshold value of social vehicles adjacent to the intersection; predicting the traffic flow of social vehicles and public traffic vehicles at the upstream intersection; and comparing the social vehicle flow predicted value with a traffic flow control threshold value, and formulating a management and control strategy including an optimal vehicle route according to a comparison result, the public transport vehicle flow predicted value and the road network data. According to the invention, social vehicles can be actively guided to enter a core area with an optimal route, regional management is realized with less data processing amount, traffic congestion of a large area is avoided, the problems of urban road congestion and space-time resource waste are solved, and the efficiency of urban bus operation is improved.

The application discloses a kind of bus departure timetable optimization method considering multiple bus lines transfer in charging station, first input and electric bus charging transfer various parameters, with cost-related various parameters;Second, the charging demand of each vehicle and the passenger transfer demand between each line are calculated, and the selection constraint of electric bus charging station and the charging transfer constraint in charging station are established;Finally, with the minimum bus operation cost and the maximum transfer passenger number as objective function, with bus scheduling scheme and the departure timetable of each line bus as decision variable, the bus scheduling model of multiple bus lines transfer in charging station is established.The application can meet the charging demand of bus and the passenger transfer demand in charging station simultaneously by reasonably scheduling the bus needing charging according to certain departure time, can increase the attraction of bus charging to transfer passengers, and promote the electrification process of urban bus.

The invention relates to the technical field of scheduling systems, and particularly discloses a smart city bus scheduling system, which comprises a data acquisition module, a scheduling optimization module, a passenger service module, a communication module, a data analysis module and a safety monitoring module, the data acquisition module is used for collecting operation data, passenger flow data and traffic condition data of buses; the scheduling optimization module is used for processing data and generating a bus scheduling scheme; the passenger service module provides real-time bus information, travel planning and electronic ticket buying service for passengers; the communication module realizes real-time communication among the public transport vehicle, the dispatching center and the system; the data analysis module is used for analyzing historical data and predicting future passenger flow and traffic conditions; the safety monitoring module is used for monitoring the operation of the bus and guaranteeing the safety of passengers and the bus; according to the invention, an efficient, safe and humanized smart city bus scheduling system is constructed, and the operation efficiency and the service quality of city public transportation are remarkably improved.

The present application provides a city bus boarding and alighting safety assistance system, comprising: a sensor module configured to collect video image information from inside and outside the vehicle; a CAN data transmission module configured to transmit vehicle data information; an on-board terminal module configured to process and store the video image information and data information, and simultaneously obtain vehicle position information, connected to the sensor module and the CAN data transmission module, respectively; a human-computer interaction module configured to display the data information, the data information including passenger capacity information, vehicle operation information, external environment information, and alarm information, connected to the on-board terminal module. The city bus boarding and alighting safety assistance system provided by the present application can effectively avoid safety hazards and traffic accidents when boarding and alighting a bus, and bring convenience to passengers taking the bus.

1. Name of this design product: Bus (9.5 meters). 2. Application of this design product: for use in city buses and passenger cars. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1. 5. The bottom surface of this design product is difficult to see or cannot be seen during use, so the top view is omitted.

This application relates to the field of vehicle control technology, and provides a method and system for tracking the trajectory of urban buses based on BeiDou positioning. The method includes: acquiring vehicle driving motion information, vehicle BeiDou positioning information, and vehicle IMU positioning information; identifying multiple candidate road segments from preset map data based on the vehicle's BeiDou positioning information and IMU positioning information, and acquiring road geometric feature information for each candidate road segment; determining the vehicle's expected road segment from the multiple candidate road segments based on the vehicle's driving motion information and the road geometric feature information of the multiple candidate road segments, and determining the vehicle's drive control command based on the road geometric feature information of the expected road segment. This solution can improve the accuracy of vehicle control.

Embodiments of the present application provide a bus scheduling method based on reinforcement learning, comprising the following steps: S110, initializing road network data, and performing regional grid division on the road network; S120, dividing trajectory data according to time periods, and calculating traffic flow of different regions in different time periods; S130, combining real-time ride request data, calculating and updating the traffic flow; S140, when a bus arrives at a route endpoint, obtaining a current road network state from multiple dimensions; S150, based on a reinforcement learning neural network, combining the updated traffic flow obtained in step S130 and the road network state obtained in step S140, calculating feedback data of the bus driving to different routes; and S160, repeating steps S130-S150, obtaining reinforcement learning model parameters, and performing bus scheduling based on the trained reinforcement learning model. Through this method, the current road network state and traffic flow can be fully considered, urban bus resources can be effectively utilized, bus operation efficiency can be improved, and urban traffic congestion can be alleviated.

The utility model provides a multifunctional stop board for urban buses, which relates to the technical field of stop boards and comprises a stop board frame, glass is arranged on one side of the stop board frame, and a mounting mechanism is arranged on the other side of the stop board frame. According to the utility model, the mounting mechanism and the fixing mechanism are arranged, a reel of roll paper is sleeved on a rotating rod, a support frame is inserted into a positioning block and is fixed by a clamping block, that is, the roll paper is fixed through the support frame and the positioning block, the support frame, the roll paper and the positioning block are inserted into the stop board frame, and a side disc is rotated to fix a threaded sleeve and the stop board frame in a threaded manner; roll paper is placed in the stop board frame, the stop board frame is provided with glass and a paper outlet hole, the remaining condition of the roll paper is observed through the glass, and the roll paper can be drawn out through the paper outlet hole for use, so that the stop board frame has good paper supply measures, and the situation that passengers suddenly need paper towels when waiting for buses is reduced; the situation that service measures are affected due to the fact that the bus stop board singly provides advertising is avoided, and convenience of the bus stop board is improved.

The utility model belongs to the technical field of urban bus folding slope plates, and particularly relates to an urban bus folding slope plate device which comprises a bottom plate, a rotating shaft rotationally installed in the bottom plate, a top plate fixedly installed on the rotating shaft, a plurality of anti-slip strips fixedly installed on the top of the top plate, a second groove formed in the bottom plate, and a supporting plate rotationally arranged in the second groove. A third groove is formed in the bottom of the top plate, the top end of the supporting plate extends into the third groove, a through groove is formed in the supporting plate, a limiting rod is slidably installed in the through groove, and the two ends of the limiting rod penetrate through the two sides of the third groove correspondingly and extend to the outside; the limiting assembly is located in the limiting rod and used for limiting the position of the limiting rod; when the whole device is used, it is ensured that the whole device can be folded, so that the whole device can occupy a small space during transportation, the whole device is convenient to transport, and meanwhile, the whole device can be conveniently held by a worker after being folded, and the whole device is convenient to carry.