40 results about "Taxis" patented technology

The invention relates to the technical field of taxi transport capacity scheduling, in particular to a cruising taxi dynamic transport capacity scheduling method and system based on a space-time network, and the method comprises the steps: obtaining a road map layer and dividing a grid region, building a position and time combination point to recognize a cruising state, and extracting a waving track amplification to form a request trend region; and screening the empty vehicles, calculating the arrival path distance and sorting, extracting the get-off gathering points to generate guide grid points, and correcting the path end point to form a dynamic scheduling result set. The method comprises the following steps of: judging a vehicle state by constructing a position and time combination point; extracting a waving track amplification to identify an active area; screening empty vehicles by combining road connectivity and path distance; strengthening a connection relationship between resources and a target area, extracting a guide grid point based on get-off point aggregation intensity and distance convergence, and correcting a path end point to point to a passenger flow area. And the resource delivery collaboration and the scheduling rhythm matching degree are improved.

The invention provides an automatic driving taxi charging and discharging scheduling method based on POI area perception and multi-target reinforcement learning. The method comprises the following steps of 1, obtaining urban historical renting order data, and modeling orders, automatic driving electric taxis and charging stations; 2, acquiring data of an urban POI buffer area, and performing area modeling; step 3, establishing an electric taxi order receiving-charging environment model; 4, establishing a charging station-electric taxi matching model; 5, constructing a state space and an action space structure in combination with POI region features; step 6, constructing a deep reinforcement learning network based on an IPPO algorithm; 7, performing real-time charging scheduling based on a deep reinforcement learning network; according to the intelligent scheduling method for the automatic driving electric taxi team, urban spatial feature recognition, energy adjustment optimization and deep strategy learning are integrated, intelligent regulation and control of the urban electric taxi team by the scheduling platform can be realized, and the method has high timeliness and practical value.

The application is suitable for the field of spatio-temporal data mining technology, and provides a taxi supply and demand imbalance road section detection method, which comprises the following steps: extracting a GPS data entry sequence corresponding to each license plate from a taxi GPS original data set in chronological order; determining a taxi supply point and a taxi demand point according to all GPS data entry sequences; determining a correction point of the taxi supply point and the taxi demand point in a city road network; finely dividing the city road network to obtain a final city road network; taking a road section with a taxi demand correction point in the final city road network as a seed road section, and expanding the seed road section along the final city road network through an iterative optimization method to obtain a plurality of candidate taxi supply and demand imbalance road sections; performing significance test by using a Monte Carlo simulation method, and taking a candidate taxi supply and demand imbalance road section that passes the significance test as a taxi supply and demand imbalance road section. The application can improve the accuracy of taxi supply and demand imbalance road section detection.

The invention discloses a transportation hub transport capacity scheduling system based on big data, and particularly relates to the technical field of traffic scheduling, which comprises a data aggregation and sensing module, an early warning and triggering module, a scheduling scheme simulation and decision module and an instruction distribution and execution monitoring module. The system collects subway, bus, taxi, online car-hailing and passenger flow data in real time through a multi-source interface and a sensor, and a unified transport capacity diagram is generated after cleaning and anomaly detection; carrying out transport capacity gap identification and early warning based on a multi-index coupling risk analysis model; a plurality of scheduling schemes are generated and evaluated through a microscopic simulation model integrated with passenger behavior prediction, and an optimal scheme is recommended; finally, the scheme is converted into a specific instruction to be distributed and executed, and real-time monitoring and feedback are performed to form closed-loop management. According to the invention, the transformation from passive response to active intervention and from experience decision to scientific scheduling is realized, and the transport capacity cooperation efficiency and passenger evacuation capability of the transportation hub are significantly improved.

The invention discloses an intelligent management system applied to cruising taxis to obtain short-distance reloading opportunities, belongs to the technical field of intelligent traffic, and aims to solve the problem of service quality caused by receiving short-distance orders by taxis in transportation junctions. The system automatically identifies and verifies short-distance orders received at a hub station through data acquisition, order identification, point management, reloading opportunity granting and other modules, and grants points to a driver according to multi-dimensional factors such as waiting time and mileage. And the driver can use the obtained credits to exchange the authority of queuing-free access to the reloading channel. According to the invention, multi-source data such as vehicle snapshot, a GPS and a taximeter are fused to carry out order authenticity verification, and an excitation strategy can be dynamically optimized based on operation data. According to the scheme, traditional passive punishment is converted into active excitation, the driver and passenger contradiction is effectively solved, and the taxi operation efficiency and the travel experience of passengers are remarkably improved.

The invention relates to the technical field of public transportation, in particular to an airport taxi real-time scheduling system based on deep learning, which is characterized in that heat vector diagrams of passengers, taxis and private cars are generated by comprehensively acquiring and fusing passenger, vehicle and environment data of an airport area; and demand prediction and transport capacity suggestion of each region of the airport in a future time period are realized by combining external factors. According to the system, a private car popularity vector diagram is introduced to correct passenger and taxi popularity vector diagrams, and multi-source data and a neural network model are fused to carry out intelligent decision making, so that the initiative and accuracy of airport taxi scheduling are improved.

The application discloses a dynamic carpooling scheduling method for unmanned electric taxis, which comprises the following steps: obtaining data and performing cleaning; mapping the data in each hexagonal grid; establishing a travel demand feature matrix and a spatial dependence matrix; constructing a spatio-temporal graph convolutional neural network based on an attention mechanism to obtain a travel demand prediction result; setting a constraint condition for single-car matching in a dynamic carpooling scenario; constructing a single-car matching decision model based on distributed deep reinforcement learning based on the travel demand prediction result and the single-car matching constraint condition; designing a Double DQN solving algorithm; performing real-time order matching and idle vehicle scheduling based on the result; notifying the matched vehicles to complete the corresponding orders; and notifying the idle vehicles that have not been matched to perform regional scheduling or charging energy supplementing. In the dynamic carpooling of multiple passengers and a single car, the electric vehicle endurance journey constraint and the vehicle charging energy supplementing demand are considered, so that the quality is improved and the actual operation requirements are met.

The invention discloses a detection and processing method for two-segment trajectory cheating, which identifies and repairs jumping, rollback and overlapping phenomena through a vector included angle cosine value and an ellipse method, thereby improving the integrity of trajectory data and ensuring the accurate calculation of mileage. In combination with trajectory clustering and a Markov chain model, the system can realize more accurate short-term and long-term trajectory prediction, and platform scheduling is optimized; by means of similarity analysis and unsupervised learning, the system can efficiently detect abnormal track behaviors, and automatic recognition can be achieved even under the condition that no annotated data exists; besides, through a transfer learning framework and in combination with track data of taxis and buses, the system can effectively detect illegal online taxi-hailing behaviors, and the problem of insufficient annotation data is solved; finally, the system accurately calculates the driving mileage of the vehicle through the repaired track, and the fairness and accuracy of cost calculation are ensured.

A drive unit for air vehicle, which allows building the vertical take-off and landing vehicles, intended for use, for instance, in the production of flying taxis, as well as in the model-making branch and in the toy industry.The drive unit is composed of the air channel, in the form of a straight segment of a tube with circular section, which has fans with engines fixed on its both ends. The vertical draft force outlet-inlet nozzle opening is located between fixed fans of the drive unit.

An integrated intelligent safety and surveillance system for vehicles combining multiple cameras (internal fisheye, front, rear), radar collision sensor, high-precision GPS unit, environmental sensors (temperature, humidity, air quality), and ambient LED lighting within a single embedded unit with internal battery and optional solar power. Features protocol-agnostic communication architecture (GSM / 3G / 4G / 5G, Wi-Fi, Bluetooth, satellite, CAN bus) and supports multiple platforms (Android, Arduino, ESP, Raspberry Pi, STM32). Provides collision warning, 360-degree monitoring, real-time GPS tracking, geofencing, stolen vehicle recovery, driving behavior analysis, and risk heat maps. Targets commercial vehicles, taxis, ride-sharing, and fleet management. Core innovation: integrating all systems into one flexible platform with comprehensive spatial-temporal analytics.

1. Name of the product in this design: Display screen. 2. Purpose of this design: To display vehicle status and advertising information on the roof of taxis. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key features: the front view.

This utility model discloses a car rubber floor mat with a drainage structure, comprising a rubber floor mat body, an internal drainage mechanism, and limiting plates on the front and rear sides of the top left side of the rubber floor mat body. The drainage mechanism includes absorbent cotton, a rubber sheet, several drainage grooves, and limiting grooves. The bottom of the absorbent cotton is fixedly connected to the top of the rubber sheet. The drainage grooves are located on the top of the rubber floor mat body, and the limiting grooves are located on the left side of the top of the rubber floor mat body. This invention solves the problem that some existing car rubber floor mats lack a drainage structure, which, especially in rainy seasons and in vehicles such as taxis and ride-hailing vehicles where passengers frequently get in and out, can easily bring rainwater into the surface of the car rubber floor mat, causing water to accumulate on the surface and become slippery, affecting riding comfort and safety when getting in and out of the vehicle.

The invention relates to the field of traffic communication transmission control, in particular to a vehicle-to-vehicle communication data transmission control method and device for a taxi. According to the method, the distribution of distances between vehicles in each taxi local area and the difference of vehicle speeds are analyzed to obtain the crowding degree of each taxi at the current moment, and the crowding degree of each taxi at the current moment is calculated according to the change of the position point of each vehicle in each taxi local area at each moment in a preset time period before the current moment. The method comprises the following steps: acquiring a trajectory disorder index of each vehicle in a local area at the current moment, acquiring a communication transmission weight of each taxi at the current moment in combination with the crowding degree of each taxi at the current moment, and performing communication transmission with different priorities on each taxi based on the communication transmission weight. According to the invention, the taxi with congestion or high collision risk can transmit communication data in time, and the timeliness of taxi communication is ensured.

The image shown in the image diagram is for displaying the results of analysis of vehicle operation data such as taxis and other commercial vehicles. When a target area is clicked on the map located on the left side of the image, data such as sales amount, average unit price, or number of sales for a certain period of time, day of the week, etc. for the clicked target area is displayed in a table located on the right side of the map. A list button for selecting an employee is displayed in a vertical rectangular frame located on the right side of the image.

The invention belongs to the technical field of intelligent traffic and low carbon, and particularly discloses a hub taxi intelligent scheduling method and system based on dynamic zoning, and the method comprises the steps: dividing passengers into three levels: short distance, midway and long distance; state detection and code management are carried out on the taxis entering the hub area, and a vehicle attribute set is constructed; the method comprises the following steps of: dividing lanes in a hub passenger waiting area into three types of short-distance special lanes, long-distance special lanes and flexible adjustment lanes, and periodically adjusting the number of the lanes of each type based on passenger type distribution data and taxi queuing data which are acquired in real time; and based on the passenger level, the identification number and the vehicle attribute set, realizing optimal matching of the passenger and the taxi through a multi-factor weighted matching algorithm, and planning an optimal driving path of the order. According to the invention, the problems that the prior art lacks a systematic scheme of demand perception-dynamic adjustment-precise matching-effect feedback and cannot meet the demands of drivers, passengers and environments at the same time are solved.

This invention belongs to the field of intelligent transportation and low-carbon technology, specifically disclosing a hub taxi intelligent dispatching method and system based on dynamic zoning. The method includes: classifying passengers into three levels: short-distance, medium-distance, and long-distance; performing status detection and coding management on taxis entering the hub area to construct a vehicle attribute set; dividing the lanes in the hub waiting area into three types: dedicated short-distance lanes, dedicated long-distance lanes, and flexible adjustment lanes, and periodically adjusting the number of each type of lane based on real-time collected passenger type distribution data and taxi queuing data; and achieving optimal matching between passengers and taxis through a multi-factor weighted matching algorithm based on passenger level, identification number, and vehicle attribute set, and planning the optimal driving route for the order. This invention solves the problem that existing technologies lack a systematic solution for demand perception, dynamic adjustment, precise matching, and effect feedback, and cannot simultaneously meet the needs of drivers, passengers, and the environment.

The application relates to the field of traffic communication transmission control, in particular to a car-to-car communication data transmission control method and equipment for taxis. The method obtains the congestion degree of each taxi at the current moment by analyzing the distance distribution between each vehicle in the local area of each taxi and the speed difference, obtains the trajectory disorder index of each vehicle in the local area at the current moment according to the position point change of each vehicle in the local area at each moment in a preset time period before the current moment of each taxi, and obtains the communication transmission weight of each taxi at the current moment in combination with the congestion degree of each taxi at the current moment. The communication transmission of each taxi is carried out with different priorities based on the communication transmission weight. The application can make the taxis with greater congestion or collision risk transmit communication data in time, and ensure the timeliness of taxi communication.

The invention provides a method, system and device for predicting a passenger-carrying hotspot, a medium and a program product. The method comprises the following steps: acquiring historical data of a taxi passenger-carrying point; performing data clustering on historical data based on a density clustering algorithm of a greedy strategy to obtain a passenger-carrying hotspot clustering result; the passenger-carrying hotspot clustering result comprises a plurality of passenger-carrying hotspots for taxi drivers to select. According to the method, data mining is performed on the sampled historical data through the density clustering algorithm of the greedy strategy, so that the problems that a traditional clustering algorithm cannot well process large-scale data and the clustering effect is poor due to insufficient priori knowledge can be avoided; meanwhile, the adaptability of a search radius and a density threshold value during clustering of a density data set with uneven density is improved, the clustering efficiency of large-scale data is improved, a passenger-carrying hotspot clustering result serves as a decision-making basis for reasonable distribution and scheduling of taxies, the scheduling efficiency and the passenger-carrying success rate are improved, and the service life of the taxies is prolonged. And the operation efficiency of the taxi can be improved.

This invention discloses a charging guidance method and system for two-shift electric taxis based on Q-learning. The method first constructs an electric taxi operation process model to simulate the vehicle's route selection and state of charge changes under different states. Second, it establishes a charging behavior model based on the Q-learning algorithm. By defining discretized time, region, and state of charge spaces, it sets passenger-finding and charging actions and designs a sum-of-benefits penalty function that includes state of charge threshold constraints at shift change times, training to obtain the optimal charging decision strategy. Finally, the guidance effect is evaluated from three dimensions: charging cost, satisfaction of residents' travel needs, and grid power supply balance. This invention effectively reduces unnecessary charging times through intelligent guidance, smooths the charging load during shift change times, lowers operating costs, and simultaneously improves grid stability and residents' travel security.

This invention relates to the field of vehicle management technology, specifically to a management system for managing unauthorized substitute drivers. The system includes a management settings module, a non-driving fare meter unlocking / locking module, an information data management module, an anti-fraud electronic service supervision module, and a hardware interaction module. The management settings module manages the activation and deactivation of non-driving detection, automatically locking the fare meter upon detection failure, and sets the detection interval, detection frequency, and facial recognition matching threshold. The non-driving fare meter unlocking / locking module is used for locking and unlocking the fare meter. The information data management and query module stores information on officially hired drivers. The anti-fraud electronic service supervision module obtains driver information. The hardware interaction module enables two-way data communication between the dynamic monitoring platform and the taxi. This system controls driver qualifications from the source, manages the entire process, completely eradicates industry management loopholes related to unauthorized substitute drivers in taxis, and reduces operational risks for businesses.

The application discloses a two-stage carpool matching method and system for connecting taxis at a large traffic hub, and the method comprises the following steps: constructing a hub taxi carpool model by taking the shortest total driving mileage of vehicles and the minimum number of service vehicles as optimization targets, and considering the hub passenger characteristics and parameters including large luggage, departure and arrival time as constraints; obtaining passenger requests, taking the path vector similarity of fused direction and space-time characteristics as a benchmark, and clustering the passenger requests by using an adaptive hierarchical clustering algorithm to obtain passenger request clusters; and solving a carpool path planning problem by using an improved adaptive genetic algorithm based on the hub taxi carpool model and the passenger request clusters, so as to obtain a passenger matching scheme and a carpool driving path. The two-stage carpool matching method can effectively quantify and cluster similar passenger requests, can improve the calculation efficiency by 85%, and can reduce the waiting time and detour distance of passengers.

The image shown in the image diagram is for displaying the results of an analysis of vehicle operation data for taxis and other commercial vehicles. When a target area is clicked on the map located on the left side of the image, data such as sales amount, average unit price, or number of business trips for a certain period of time and day of the week for the clicked target area is displayed in the upper bar graph located on the right side of the map. When a bar graph selected from the upper bar graph is clicked, detailed data is displayed in the lower bar graph located below the map. A list button for selecting an employee is displayed in a vertical rectangular frame located on the right side of the image.

Disclosed herein are system, method, and computer program product embodiments for utilizing non-RAM memory to implement calculating a first wind metric over a geographic area, wherein the first wind metric reflects total wind velocity vector differences. The method further calculates a second wind metric over the geographic area, wherein the second wind metric reflects a horizontal wind gradient near obstacles. The method further generates a first data set of the first wind metric and a second data set of the second wind metric and combines the first data set and the second data set into a combined data set representing a combination of the first wind metric and the second wind metric. The combined data set represents wind hazards in an urban environment with buildings and is used to identify or generate low altitude flighted vehicle navigation paths for urban flighted vehicles, such as drones or air taxis.

The application provides an unmanned taxi intensive scheduling method based on demand density prediction, and relates to the technical field of demand prediction and scheduling. The method firstly aggregates the attribute characteristics of the unmanned taxi demand on different demand frequencies and different time layers; extracts the external characteristics of the unmanned taxi demand; then fuses the space-time characteristics and external characteristics of the unmanned taxi demand to obtain the prediction value of the unmanned taxi demand in different fields; then predicts the unmanned taxi demand from the time layer and the external characteristics; finally, on the basis of the predicted unmanned taxi demand, a heuristic genetic algorithm is used to realize the matching of the unmanned taxi and the passenger. The method eliminates the over-reliance of the demand density of the unmanned taxi on a certain characteristic, can dispatch the unmanned taxi to the area with high potential income in time, improves the order acceptance rate and profit rate of the unmanned taxi, and realizes the global supply and demand balance.

The application relates to the technical field of intelligent transportation, in particular to an intelligent scheduling method and device for unmanned taxis, wherein the method comprises the following steps: based on received vehicle data, passenger data and environment data, a spatial topology structure for connecting target unmanned taxis, passengers and charging stations is constructed; spatial dependency relationships among target unmanned taxi nodes, passenger nodes and charging station nodes are captured to generate spatial coding features and time coding features, and the spatial coding features and the time coding features are fused by combining corresponding weights to generate space-time features of the target unmanned taxis, so that passenger matching results and / or path planning and / or charging decisions of the target unmanned taxis are generated based on the space-time features. Therefore, the problems that traditional unmanned taxi scheduling methods do not fully consider complex factors in actual driving scenarios, scheduling decisions are still mostly single-target oriented, lack of dynamic adaptability and cannot meet the growing travel demands of passengers and the like are solved.

The application discloses a short-distance order intelligent matching and load balancing scheduling system for cruising taxis, and particularly relates to the technical field of intelligent traffic management, and the system comprises a topology evidence module, a queue gating module, a batch matching module and a closed-loop processing module; wherein the topology evidence module is used for unified access, time benchmark alignment and site topology modeling of multi-source data of key sites and surrounding roads to generate a sequence evidence chain of vehicles; the queue gating module is used for reconstructing real-time sequences of vehicle queue areas, forming a queue sequence list, and generating a sequence evidence credibility and a load gating quantity of each vehicle; the batch matching module is used for completing short-distance order identification, release batch generation, sequence layer range determination, vehicle and order matching and passenger pickup window allocation within a rolling scheduling window; and the closed-loop processing module is used for executing issuing, process monitoring, abnormality processing, rollback adjustment and audit solidification.

The invention relates to the technical field of public transportation, in particular to an airport taxi real-time scheduling system based on deep learning, which is characterized in that heat vector diagrams of passengers, taxis and private cars are generated by comprehensively acquiring and fusing passenger, vehicle and environment data of an airport area; and demand prediction and transport capacity suggestion of each region of the airport in a future time period are realized by combining external factors. According to the system, a private car popularity vector diagram is introduced to correct passenger and taxi popularity vector diagrams, and multi-source data and a neural network model are fused to carry out intelligent decision making, so that the initiative and accuracy of airport taxi scheduling are improved.

The invention discloses a taxi passenger virtual queuing-oriented number calling type scheduling system and a use method thereof. The system comprises a vehicle pause area, a queuing passenger loading area and a passenger waiting area. The vehicle pause area comprises a plurality of passenger getting-on areas connected with the queuing passenger getting-on area; each passenger taking area is provided with a vehicle queuing statistical system connected with the taxi dispatching system, and passenger taking temporary berths are arranged at the positions connected with the queuing passenger taking areas; each vehicle queuing statistical system is used for counting the queuing condition of taxis, sending the corresponding queuing condition to the taxi scheduling system, and guiding the corresponding taxi to the corresponding passenger-taking temporary berth after any taxi and the corresponding passenger reach a passenger-carrying agreement and the corresponding passenger completes sign-in; the queuing passenger loading area and the passenger waiting area are provided with a number calling broadcasting system and an information issuing screen; and the passenger waiting area is also provided with a queuing sign-in machine and an applet display device. According to the invention, passengers are liberated from a physical queue, and efficient and accurate matching of taxis and passengers is realized.

At the very least, it provides a taxi navigation method that eliminates the need for communication between the user and the driver of the vehicle (including taxis) regarding the destination address. [Solution] A method for navigating a taxi comprises the steps of: a user terminal transmitting destination information relating to the destination address; and a navigation device installed in the taxi receiving the destination information.

An object of the system according to the embodiment is to instantaneously compare various elements of a plurality of vehicle types.SOLUTION: A system according to an embodiment includes a price comparator, a fuel economy comparator, a quietness comparator, an acceleration feeling comparator, a tax comparator, an insurance comparator, and a travel fee comparator. The price comparison unit uses the generated AI to compare the prices of the plurality of vehicle types that the user is considering. The fuel efficiency comparison unit compares the fuel efficiencies of a plurality of vehicle types that the user is considering. The quietness comparator compares quietness of a plurality of vehicle types that the user is considering. The acceleration feeling comparison unit compares acceleration feelings of a plurality of vehicle types that the user is considering. The tax comparison unit compares taxes of a plurality of vehicle types that the user is considering. The insurance comparison unit compares insurances of a plurality of vehicle types that the user is considering. The travel fee comparator compares the travel fees of the plurality of vehicle types that the user is considering.SELECTED DRAWING: Figure 1