53 results about "Shared mobility" patented technology

The invention provides an implementation method of a shared bicycle real-time tracking and positioning system. The system comprises a controller, a plurality of tracking devices, a plurality of shared bicycles and a plurality of operation devices. And the operation equipment monitors and positions the shared bicycles in real time through the controller and the tracking equipment. Through the system, an operator can obtain accurate position information of the monitored shared bicycle in real time, and all-weather and high-precision bicycle tracking and positioning are realized. The system remarkably improves the operation and maintenance management efficiency of the shared bicycles, reduces the operation cost, and has a wide application prospect in the fields of smart transportation, urban shared travel and the like.

An embodiment of the present disclosure provides a method for managing a safety solution in a shared mobility apparatus system, wherein the method may include identifying whether the safety kit is stored in the safety kit storage case, identifying whether an applicant of the mobility apparatus withdraws and wears the safety kit identified as being stored in the safety kit storage case, in response to the applicant being identified as wearing the safety kit, the management server unlocking a starting mode of the mobility apparatus, and in response to confirming that an engine of the mobility apparatus with the unlocked starting mode is turned on, the management server notifying that charging starts according to use of the mobility apparatus to a mobility apparatus APP of the applicant.

The present invention relates to a ride-sharing system which includes two mobile applications: a driver application and a rider application, operating in conjunction with a central server system. The driver application allows drivers to create detailed profiles including credentials, vehicle information, and payment options, and generates a scannable QR code for secure and cashless payments. The rider application enables riders to search for drivers, view profiles, select a preferred driver, book rides, and process payments using the driver's QR code. The server system comprises a driver profile database, a rider profile database, a payment module, and a location module for real-time tracking and matching of drivers and riders. The method of ride booking includes steps for profile setup, ride booking, real-time ride management, and secure payment processing, providing an efficient, safe, and personalized ride-sharing experience.

The application discloses a kind of shared electric scooter intelligent scheduling method and system, it is related to shared travel field, the method comprises: obtaining the vehicle request of user;Real-time monitoring the state of each shared electric scooter;According to the vehicle request and the state of each shared electric scooter, generate scheduling instruction, to control corresponding shared electric scooter to complete the vehicle request;Wherein, the shared electric scooter is under the control of the scheduling instruction, based on auxiliary driving technology and AI intelligent navigation technology completes the vehicle request.The application improves the scheduling response speed of shared electric scooter, operation intelligent level and resource utilization, promotes shared travel industry to the direction of automation, intelligent development.

The present disclosure provides systems and methods for systems and methods for generating potential flight plans to be used by a ride sharing network, including dynamic and / or automated changes to flight plans that have been engaged with passengers based on real-time information. In particular, the systems and methods of the present disclosure can operate to generate a fleet-level set of potential flight plans which comply with one or more constraints for a fleet of aircraft. The potential flight plans can be exposed into and used by a ride sharing network to provide transportation to users.

A mobility service providing method and apparatus are provided. The method includes: moving, by a vehicle, to a customer when the customer requests the vehicle to provide a mobility service and forming, by one or more anchors mounted on the vehicle, an Ultra-Wide Band (UWB) link with an information technology (IT) device carried by the customer when a determination is made that a distance between the vehicle and the customer is within a predetermined distance; performing a smart anchor positioning (SAP) algorithm to estimate a position of the IT device; performing position-based security authentication with the customer; and arriving, by the vehicle, at the customer, wherein the one or more anchors and the IT device transmit or receive UWB pulse waves.

The invention discloses a regional battery warehouse management method and system and a storage medium thereof, and relates to the field of shared electric vehicle battery management, and the method comprises the steps: constructing a service area and a hierarchical parking point network through data driving, precisely defining a trunk area based on multi-source people flow data, customizing a minimum electric quantity threshold value, and intelligently analyzing the urgency of a battery replacement request. And implementing a cross-hub battery borrowing mechanism, and dynamically optimizing global battery resource scheduling. Through data-driven refined operation, intelligence and high efficiency of battery scheduling in the battery warehouse of the shared electric vehicle are realized, the half-way drop risk of a user is remarkably reduced, global battery resources are converted into a value chain of dynamic circulation, coexistence of resource idleness and shortage is avoided, and the service life of the battery warehouse is prolonged. The service range and the threshold value are automatically optimized by continuously analyzing mobile data, sustainable infrastructure support is provided for urban shared travel, and meanwhile operation of batteries in a whole warehouse is maximized.

This invention discloses a method and device for detecting and reassigning violations in multi-tenant chauffeur services, relating to the field of intelligent transportation and shared mobility service technology. The method includes: real-time collection and storage of driver endpoint location data; synchronization of channel order and multi-tenant capacity data, calculating grid supply-demand heat value and channel capacity balance value; responding to order status changes, extracting location data reported by the driver's end bound to the current order within a preset time period before and after the order status change; quantifying multiple risk factors, dynamically adjusting factor weights based on supply-demand heat value and channel capacity balance value, calculating a weighted risk score and determining the risk level; obtaining the real-time capacity status of the grid, and deciding on the final reassignment action based on the risk level. This invention, through a hierarchical reassignment mechanism that integrates multi-dimensional factors, dynamic weights, and capacity linkage, accurately identifies violations such as drivers quickly canceling or picking and choosing orders, improving completion rates and capacity efficiency, and reducing misjudgments and complaints.

The present invention belongs to the field of supply and demand dispatching for online car-hailing, taxi, and ride-sharing services. It specifically discloses a driver-passenger dispatching optimization method based on a multi-objective genetic algorithm, including S1, optimization problem modeling; S2, generating an initial population; S3, initializing the number of iterations, t=0; copying w solutions 1 into 2w solutions; S4, multi-objective calculation of shared fitness for the 2w solutions, using the NSGA algorithm for the multi-objective genetic algorithm; S5, selecting and calculating the selection probability; S6, crossover; S7, mutation; S8, increasing the number of iterations; S9, selecting the solution with the largest shared fitness among S1…Sw as the optimal solution. The present invention solves the multi-objective optimization problem of driver-passenger dispatching in shared travel, while simultaneously achieving the optimal solution for the interests of drivers, passengers, and the platform.

An electric vehicle charging and discharging device that connects a ride-sharing service to a vehicle-to-grid system may include a processor and memory that stores software which, when executed by the processor, causes the processor to collect energy data from buildings, predict the energy consumption of the buildings based on the energy data, calculate a time zone in which additional energy is needed for each of the buildings, calculate a required amount of electrical energy based on the collected energy data and the predicted energy consumption, estimate a travel demand of a region in which the buildings are located, and determine a route for a ride-sharing vehicle for each time zone within the region based on the time zone that needs additional energy, the required amount of energy, and the travel demand.

The invention relates to a seat adjusting method and device, a travel order processing method, a terminal, a vehicle, a medium and a program product, and relates to the technical field of intelligent sensing and intelligent cabins. The method comprises the steps of judging whether a riding object moves towards a vehicle or not and whether the distance between the riding object and the vehicle is smaller than a distance threshold value or not by obtaining movement data of the riding object, collecting an image of the riding object before the riding object gets on the vehicle, and adjusting a target seat of the vehicle in advance in combination with multi-source information of the riding object. The method is especially suitable for scenes of online car hailing, shared travel and the like, and intelligent and personalized seat control and management are realized.

A computer-implemented method that, in an embodiment, includes receiving a travel request from a traveler that includes a pickup and destination location and a number of travelers and analyzing the travel request and calculating routes for vehicles that are partially based on the pickup and destination location, the number of travelers, destination locations of travelers located in the vehicles, traffic conditions, minimizing a waiting time for the traveler, minimizing a travel time for the traveler, minimizing a travel time for the travelers located in the vehicles, and optimizing an aggregation of travelers on each of the plurality vehicles. In an embodiment, the plurality of vehicles includes 5 vehicles or more and based on the calculating, the method includes supplying a selected route to a selected vehicle and supplying to the traveler, identification information related to the selected vehicle thereby resulting in transportation of the traveler to the destination location. A systems and methods for ridesharing are provided. The systems and method can include splitting a plurality of GPS locations for a given vehicle into segments, determining a most probable location for each GPS location, and reconstructing the route, for a fleet of ridesharing vehicles.

The invention discloses a car-pooling dynamic scheduling method and system for online car-hailing, and relates to the technical field of dynamic scheduling of order dispatching tasks, and the method comprises the steps: collecting real-time order information and working state information of online car-hailing, and constructing a city supply and demand distribution diagram; the order and the online car-hailing car are used as two intelligent agents respectively, and after the two intelligent agents are subjected to one-to-one matching, the fitness of each pair of matched intelligent agents is calculated; performing global stability analysis according to the fitness; and according to an analysis result of the stability, performing global adjustment on a matching result to generate a final scheduling result. According to the overall scheme, the intelligent level and user experience of car sharing service are improved, and efficient, safe and sustainable development of urban shared travel service is promoted.

The invention discloses a sun protection demand-based distribution and route lane planning system for an automatic driving vehicle in shared travel, and the system comprises a user vehicle matching module which calculates the matching degree score of a current user and a candidate vehicle according to the sun protection sensitivity grade of the current user and the vehicle comprehensive sun protection index of the candidate vehicle; therefore, a vehicle matching result is obtained; the route and lane planning module constructs a multi-target route planning model through an optimization algorithm, and the multi-target route planning model calculates the comprehensive cost of all candidate driving routes and lane sequences through user basic information, historical travel data, sunscreen sensitivity levels, vehicle matching results and real-time traffic environment data. And selecting the candidate driving route and the lane sequence with the minimum comprehensive cost as the optimal driving route and the optimal lane sequence. According to the invention, personalized service quality and user comfort of shared travel are improved.

A computer-implemented method for receiving electric vehicle (EV) data describing at least one connected electric vehicle and at least one portable electric vehicle charging (PEVC) unit and analyzing the received data to determine at least one PEVC demand requirement and at least one PEVC ride-sharing supply. The method may further include assigning an identified PEVC carrier to deliver a PEVC unit to a target PEVC end user and transmitting at least one delivery instruction to the assigned PEVC carrier and the target PEVC end user. The method may further include receiving a confirmation message from the assigned PEVC carrier that the PEVC unit was delivered to the target PEVC end user.

The invention discloses a shared trip-oriented priority-based multi-task scheduling method and system, and the method comprises the steps: constructing a node set containing supply nodes and demand nodes and a task set of multiple types of tasks, configuring truck parameters, calculating the driving time between the nodes, defining the net demand quantity of the nodes, and judging the types of the nodes. The method comprises the following steps of: calculating the net demand quantity, distributing task priorities, setting a unit reward value, constructing a mixed integer programming model integrating multiple constraints, solving and outputting a scheduling scheme by adopting a customized operator algorithm, and refining sub-steps of calculating the net demand quantity, distributing the priorities and constructing the model so as to optimize a process; and a system matching method function module supports ordered operation of each link. According to the method and the system, accurate adaptation of multiple tasks and priorities is realized, the task response efficiency and the scheme feasibility are improved, and the resource allocation rationality and the overall efficiency of shared travel operation are further improved.

The invention belongs to the technical field of intelligent traffic and shared travel, and particularly relates to a car-pooling optimization method considering demand adaptation and balanced pricing, and the method comprises the steps: obtaining passenger information; a demand adaptation graph is constructed based on passenger information, the passenger information comprises a binary feature demand, a double-binary feature demand and a quantitative feature demand, and the binary feature demand represents the binarization tendency of the passenger to the first type of behaviors; the dual-binary feature demand represents the binarization tendency of the passenger to the second type of behaviors and the acceptance degree of the passenger to the behaviors of other people; the quantitative characteristic demand represents an acceptable range of the passenger for the third type of behaviors; determining adaptation results of individual demands of different passengers based on the adaptation function, and forming connection edges based on the adaptation results; forming a demand adaptation graph based on the passenger information and the connecting edges; forming an alliance structure based on the demand adaptation graph, and searching in the alliance structure to obtain an optimal alliance structure meeting constraints; and based on the optimal alliance structure, calculating a payment result meeting an equilibrium condition through surplus calculation.

To provide a technique for an appropriate use charge corresponding to a disturbance degree and collection easiness of returned shared mobility.SOLUTION: A shared mobility use charge calculation method acquires return position information showing a return position of a shared mobility rented to a user, calculates disturbance degree of a returned shared mobility or collection easiness of the shared mobility on the basis of the return position information, and calculates a use charge of the shared mobility on the basis of the disturbance degree or the collection easiness.SELECTED DRAWING: Figure 1

The present invention discloses an optimized system for group pick-up and drop-off of autonomous vehicles based on group behavior prediction, including a passenger group data collection module, a group behavior prediction and pattern analysis module, a multi-modal large model optimization module, a group pick-up and drop-off point priority ranking and dynamic adjustment module, and a real-time group interaction and feedback module; By analyzing the behaviors, needs, locations, and future movement trajectories of multiple passengers in real time and combining group behavior prediction technology, the autonomous vehicle can dynamically adjust pick-up and drop-off points and driving routes to maximize pick-up and drop-off efficiency and passenger experience. The system of the present invention is particularly suitable for scenarios such as shared travel, multi-passenger carpooling, and dynamic multi-person pick-up and drop-off scenarios.

Methods, computer-readable media, software, and apparatuses include collecting, via one or more sensors and during a first window of time, sensor data associated with an acceleration of the vehicle, processing the sensor data to obtain frequency domain sensor data, analyzing the frequency domain sensor data to identify one or more occurrences of a vehicle mass change event, classifying a use of the vehicle for a shared mobility service during one or more time periods of the first window of time based on the one or more occurrences of a vehicle mass change, and transmitting, to a remote computing device, a notification relating to use of the vehicle for the shared mobility service.

Systems, methods, and computer-readable media for constraint-based itinerary generation in a ride-sharing service are disclosed. Drivers in the ride-sharing service may execute itineraries that include legs, where each leg is defined by a start point and a stop point. The itineraries may be generated in real time in response to ride requests, traffic conditions, and the like. A number of itinerary variations may be generated. Ride requests may include ride tags upon which a ride constraint may be defined. Vehicles may be compared to the ride constraint to determine whether the vehicle satisfies the ride constraint. Vehicles that do not satisfy the ride constraint may be removed from consideration when generating the itineraries, thereby constraining the search space in determining an optimal itinerary.

A system and method for enhancing safety in rideshare services through live video and audio streaming, AI-based behavioral anomaly detection, GPStracking, smart device sensor input, and real-time remote monitoring via safety codes. The system provides both internal and external live feeds from the driver's mobile device, along with alternative monitoring tools such as audio analysis, motion sensing, facial verification, panic detection, and dynamic safety scoring. The system enables trusted individuals (Guardians) to monitor rides in real-time and receive alerts upon detection of irregular or unsafe activity.

The invention relates to the technical field of shared travel and intelligent traffic, and particularly discloses a data processing method and device for vehicle driver and passenger cooperative driving, and the method comprises the steps: receiving a hitchhiking order issued by a passenger terminal and travel information issued by a vehicle owner terminal; matching the passenger with the vehicle owner based on the hitchhiking order and the travel information; for the successfully matched passenger and owner, generating an initial cooperative driving scheme based on the hitchhiking order and travel information; providing a negotiation interface for the passenger terminal and the vehicle owner terminal based on the initial cooperative driving scheme, and generating a target cooperative driving scheme according to feedback data of the passenger terminal and / or the vehicle owner terminal on the initial cooperative driving scheme in the negotiation interface; and in the hitchhiking journey, driver switching reminding is carried out based on the monitoring data, the road section division scheme and the driving intersection point information so as to carry out cooperative driving. According to the scheme, the driver and passenger cooperative driving of the hitchhiking journey can be realized, and the safety and reliability of the journey are remarkably improved.

The disclosure includes embodiments of personalizing a shared ride in a mobility-on-demand service using a response matrix. A method includes receiving feedback from a first set of users that share a first shared ride, wherein the feedback describes their individual satisfaction with the first shared ride. The method includes updating, by the processor, a response matrix to include the feedback, wherein the response matrix includes digital data describing historical user satisfaction with a plurality of shared rides over time. The method includes matching, by the processor, a second set of users to a second shared ride based on service profile data for the users, vehicle data for vehicles, and the response matrix so that the satisfaction of the second set of users with the second shared ride is improved based on the response matrix.

System (10) for providing journeys (F) through shared ride services (FD), wherein the system (10) comprises: a platform (12) designed to manage user data (ND) assigned to the respective users (N) of the shared ride services (FD), the platform (12) comprising a processing unit (14) designed to process the user data (ND) assigned to a given user (N) in order to suggest a ride (F) to the given user (N) and / or create a ride (F) for the given user (N), the system (10) is further configured to depending on the user data (ND) assigned to the user (N) in question, to selectively merge the user (N) in question with other users (N) and / or to avoid merging the user (N) in question with other users (N), the processing unit (14) being further equipped to: from the user data (ND) to determine at least one marker (M) for each user (N), wherein the at least one marker (M) indicates a compatibility of a given user (N) with at least one other user (N) and / or a preference of a given user (N). to compare at least one marker (M) assigned to the user (N) with the trips (F) and / or the markers (M) of the other users (N), whereby, in case of a match, a suggestion for a trip (F) is triggered and / or another user (N) is suggested as a passenger, wherein the platform (12) of the system (10) further comprises an interface (17) through which users (N) can interact with the system (10), characterized by the fact that In the event that a match is determined, a first user (A) can send a request to a second user (B) via the platform (12) of the system (10), or that, in the event that a match is determined, the processing unit (14) is set up to suggest the journey (F) from the first user (A) to the second user (B), who can then send a request to the first user (A) via the platform (12) of the system (10).

This will enable regular and affordable ride-sharing. [Solution] The dispatch management device comprises one or more processors, which set up a plurality of candidate trips 210, each associated with one of a plurality of predetermined travel routes and a transport period, receive a dispatch request (rideshare dispatch request 240) from a user terminal, and assign the dispatch request in parallel to one or more candidate trips 210, where the pick-up and drop-off selection points received from the user terminal 20 that made the dispatch request are included in the travel route and the desired transport time received from the user terminal 20 that made the dispatch request is included in the transport period, and at a predetermined timing before the transport period, one of the candidate trips 210 to which the dispatch request has been assigned is designated as a confirmed trip 212, and a vehicle is assigned to the confirmed trip 212.

This invention relates to the field of shared mobility scheduling and charging service management technology, and particularly to a dynamic charging decision-making system and method that links shared mobility and charging services. The system includes: collecting multi-source data on vehicle battery level, charging pile status, and regional demand; analyzing historical data using a particle swarm optimization algorithm to generate scenario-based emergency charging thresholds and preventative charging thresholds; and performing noise reduction and standardization processing on real-time data. This invention effectively breaks the rigid limitation of traditional fixed battery thresholds on charging timing by constructing a deep matching mechanism that links offline parameter optimization with real-time data. It achieves precise linkage between the battery status of shared vehicles, travel demand, and charging resources, as well as a global optimization closed loop for the scheduling system, solving the dual balance problem of ensuring battery availability and fulfilling orders during operation.

This APP is about live streaming inside and outside of a rideshare to authinticate and determine the saftey of everyone inside of the ride share. This APP will also determine the drivers identity to the passanger so they do not enter the wrong vehicle. This APP will store all video on the cloud. Aslo a code will be generated for each ride so it may be shared. All this is for saftey.

The invention provides an electric bicycle helmet management method and device, and the method comprises the steps: carrying out the image recognition of a helmet image under the condition that an electric bicycle is unlocked, and obtaining a recognition result of dirt on a helmet; generating and displaying helmet prompt information under the condition that the dirt identification result is that the foreign matter exists; in the electric bicycle returning process, the scene image of the helmet storage frame is subjected to image recognition, and a foreign matter recognition result is obtained; and when the foreign matter identification result is that the foreign matter exists, generating and displaying foreign matter cleaning prompt information. According to the method provided by the invention, the computer vision identification technology is introduced to two key nodes of unlocking and returning, so that intelligent closed-loop management of the whole life cycle of the helmet is realized, the cleaning state of the helmet and the basket can be automatically maintained without depending on high-frequency manual inspection, the operation and maintenance cost is remarkably reduced, and the working efficiency is improved. And the service quality and the operation efficiency of shared travel are improved.

Ride-sharing system, comprehensive: a memory configured to contain one or more executable instructions; a controller configured to execute the executable instructions; one or more mobile computing devices (57) configured to transmit pickup location data (303) and drop-off location data (306) to the controller; a ride-sharing vehicle (12) configured to transmit vehicle location data to the controller, wherein the ride-sharing vehicle (12) is an autonomous vehicle configured to drive autonomously from one location to another; and the executable instructions of the controller enable the following: Receiving the pickup location data (303) transmitted by the mobile computing device (57); Receiving the drop location data (306) from the one or more mobile computing devices (57); Receiving vehicle location data from the rideshare vehicle (12); based on the pick-up location data (303), drop-off location data (306) and vehicle location data, calculating a ride-sharing priority number (304) for each of the one or more mobile computing devices (57), wherein the ride-sharing priority number (304) is calculated from the following equation: VT = α (RT) − β 1 (PT) − β 2 (JT) − γ (CT) + δ 1 (WT) + δ 2 (WT 2) + ε (DT), where the ride-hailing priority number (304) is calculated from the following weighted variables: expected revenue from serving the customer (RT), customer pick-up time (PT), customer travel time (JT), fee spent serving the customer (CT), customer current wait time (WT), and customer destination suitability (DT), and where α, β1, β2, γ, δ1, δ2, and ε are weighted factors having a default weight value; Transmitting the ride-sharing priority number (304), pick-up location data (303) and drop-off location data (306) to the ride-sharing vehicle (12) for at least one of the one or more mobile computing devices (57); and where the ride-sharing vehicle (12) is configured to: Receiving the ride-sharing priority number (304), pick-up location data (303) and drop-off location data (306) for each of the one or more mobile computing devices (57) from the controller; Select the rideshare priority number (304) with the highest value; and Transfer to a location that matches the pickup location data (303) for the selected rideshare priority number (304).