78 results about "Travel time" patented technology

The invention provides a method for quickly establishing a three-dimensional near-seafloor speed model in a shallow sea area; the method comprises the following steps of extracting initial informationand extracting a common-center-point gather; calculating the slope of the initial travel time in the common-center-point gather; judging whether a shot point is located in the seawater layer or not,and if yes, correcting the shot point to the seafloor; calculating the speed and the depth of inflection points corresponding to respective offset distances; performing linear interpolation on the speed at each depth to obtain a one-dimensional speed model of the common center point; after all the common-center point domain gathers are processed, adopting the linear interpolation method, calculating the speed values of the rest grid points by utilizing the one-dimensional speed model of all common center points, and a speed model is output. By adoption of the method for quickly establishing the three-dimensional near-seafloor speed model in the shallow sea area, the influence of the seawater layer on ray propagation is overcome, and the influence of multiple excitation receiving modes on an inversion result is eliminated, and the problem that the conventional method needs to carry out multi-ray tracing forward modeling is avoided.

The present application provides a traveling route prediction method, an electronic device, and a storage medium. The method comprises: acquiring a plurality of historical traveling trajectories of a target vehicle in historical traveling; filtering the plurality of historical traveling trajectories on the basis of a preset filtering rule to obtain target historical traveling trajectories; acquiring trip information of the target historical traveling trajectories; calculating route probabilities of respective target historical traveling trajectories having a same destination position and a same vehicle travel time period among the target historical traveling trajectories; establishing a habitual route database of the target vehicle on the basis of the route probabilities and the trip information; and upon detecting that the target vehicle is started, determining a first final predicted traveling route of the target vehicle on the basis of the habitual route database. In the present application, a future trip of a user can be predicted when the user does not use an automotive navigation system, thereby improving travel experience of the user.

This application provides a method for recommending travel routes, an in-vehicle system, and a storage medium, relating to the field of in-vehicle tourism service technology. The method can obtain the route length, travel time, required travel cost, and average experience score of all attractions for each travel route. Then, for each travel route, it determines a first sub-rating corresponding to the route length, a second sub-rating corresponding to the travel time, a third sub-rating corresponding to the travel cost, and a fourth sub-rating corresponding to the average experience score. A weighted average of the first, second, third, and fourth sub-ratings for each travel route is taken to obtain a recommended rating for each travel route. Based on the top N% of travel routes ranked by their recommended ratings, recommended routes are output, which can balance short route length, short travel time, low travel cost, and high average experience score, achieving a balance between the user's itinerary, travel time, cost, and overall experience.

During operation, a computer system may intuitively and interactively (or dynamically) present a travel option. Notably, the computer system may receive, from an electronic device, information specifying a desired travel itinerary. In response, the computer system may determine the travel option based at least in part on the information. Then, the computer system may provide, addressed to the electronic device, second information specifying the travel option, where the travel option is configured for display in a calendar format. In some embodiments, the travel option includes an associated total cost. Note that the travel option may be based at least in part on a predefined user preference, such as a budget or preferred travel time.

A kind of method, system, medium and product of data processing of duty of wisdom camp, in the method, system first obtains the attribute information of current execution task and to-be-executed task, generates the vector reflecting the completion state of current task and the vector of next task admission requirement. By comparing the two vectors, determine the conflict items that need to be adjusted, and find the corresponding state recovery action and its required duration. Combined with the physical travel time between the two task locations, the total transition time is calculated. According to the latest start time of the second task and the transition time, the system judges the key end time point of the first task, and decides whether to generate a scheduling instruction containing state recovery action according to the key end time point. The present application is used to improve the prediction accuracy of the start time of subsequent task, reduce the service response delay risk caused by the personnel unable to arrive at the designated point in time.

This invention provides a sample entry / exit method and system. Upon receiving sample entry / exit instructions, it automatically determines the task sequence and generates a task scheduling strategy based on the current number of idle robots. This achieves a fully automated process from task reception to execution, significantly reducing manual intervention and improving work efficiency. It also ensures the rationality and timeliness of task allocation, demonstrating a high level of intelligence. By comprehensively considering the task sequence and the number of idle robots, task allocation can be dynamically adjusted to ensure optimal resource allocation, thereby improving overall entry / exit efficiency. Furthermore, based on warehouse layout, shelf location, sample type and specifications, and task allocation results, precise movement paths can be planned for each target robot. This not only shortens robot travel time and reduces energy consumption but also avoids collisions and path conflicts between robots, ensuring the safety and smoothness of entry / exit operations.

The application provides a road section travel time prediction method and system, the method comprising: obtaining two continuous data points in vehicle trajectory data, and determining path travel time based on the two continuous data points; determining path free flow travel time and path delay time based on the path travel time, wherein the path free flow travel time comprises at least one road section free flow travel time corresponding to at least one road section; determining at least one road section static delay time and at least one road section dynamic delay time corresponding to the at least one road section in the path delay time based on the scene where the two continuous data points are located; determining road section delay time of each road section based on the at least one road section static delay time and the at least one road section dynamic delay time; and determining road section travel time based on the road section delay time and the road section free flow travel time. The application achieves the purpose of obtaining more accurate road section travel time of each road section through low sampling frequency vehicle trajectory data.

The application discloses a bus riding route planning method based on human-vehicle-road cooperation and belongs to the cross-application field of intelligent networked public transport system design. The method comprises the following steps: collecting passenger external origin-destination point demand data, real-time road condition data, bus station GIS data and road network GIS data; associating the external origin-destination point with the nearby bus station according to the passenger external origin-destination point demand data and the bus station GIS data; estimating the walking transfer time between the external origin-destination point and the corresponding bus station; sequentially connecting the bus stations associated with the external origin-destination point based on the shortest path principle to obtain the bus driving path; constructing an optimization model, with the target being the minimum total passenger travel time and the constraint condition being that the passengers only board from the bus stations associated with the external origin-destination point; and solving the optimization model to obtain the time and position information of the boarding and alighting bus stations.

A system and method for individualized airline passenger disembarkment integrates with Passenger Service Systems (PSS) and Airport Operational Databases (AODB) using IATA New Distribution Capability (NDC) standards for real-time data exchange. Passengers book priority disembarkment options during reservations, with availability determined via priority queues in distributed software modules interacting with cloud-based hardware to allocate limited slots efficiently. Upon detecting delays, the system identifies at-risk passengers for missed connections by computing gate-to-gate travel times using graph data structures, assigns priority disembarkment passes (physical or virtual) with verification elements such as QR codes, NFC, or RFID, and deactivates passes post-disembarkment via geo-fencing or hash-based structures. This reduces airline costs, enhances operational efficiency by minimizing latency and optimizing resources, and complies with aviation regulations across multiple jurisdictions while improving passenger satisfaction.

The application provides a ride-hailing carpool dynamic matching method based on travel time prediction. The method comprises the following steps: obtaining a data set of ride-hailing orders; constructing a travel time prediction model based on interpretable machine learning; training the travel time prediction model using the data set and the interpretable machine learning model SHAP method to obtain a trained travel time prediction model; constructing a shared network; predicting the travel time of each order by using the trained travel time prediction model; matching carpool orders according to the on-demand carpool dynamic matching algorithm; and dynamically dispatching the matched carpool orders in the direction of carbon emission reduction. The method uses the demand information and location data of passengers, matches the qualified passenger orders through reasonable algorithms and optimization strategies, reduces the empty running and repeated running of vehicles through carpooling, improves the service efficiency of ride-hailing and reduces carbon emissions.

The application relates to a bus visualization scheduling method and system based on bus passenger flow and turnover time, which comprises the following steps: S1, checking the rationality and feasibility of relevant parameters, and taking the parameters passing the check as input parameters; S2, generating a timetable based on a space-time network optimization algorithm or a non-standardized file analysis of a regular expression; S3, obtaining basic information data of the timetable, and displaying the departure time, travel time and route sign number of all shifts in the timetable, namely Gantt chart visualization; S4, calculating the route sign and shift evaluation indexes of the timetable and comprehensively evaluating the generated timetable, and if the actual demand is met, the process is ended; if the actual demand is not met, step S5 is executed; S5, changing the input parameters in the timetable or selecting a scene to automatically optimize, adjusting the timetable and repeating steps S3 and S4. Compared with the prior art, the application improves the bus timetable scheduling efficiency and reduces the bus operation cost.

The application discloses a train operation table compiling method based on Morlet parameter adaptive differential evolution algorithm, and the method is characterized in that: according to a preset weight coefficient, three indexes of 'line capacity maximization', 'departure frequency minimization' and 'total passenger travel time minimization' are weighted and synthesized to construct a comprehensive objective function, so as to establish a single-objective optimization model, and the decision variables include the departure frequency of large and small inter-route trains and the operation interval of small inter-route trains. The application adopts an improved differential evolution algorithm for solving: through a two-stage adaptive mechanism based on a Morlet function, a scaling factor and a crossover probability are dynamically adjusted, the sensitivity of the algorithm to parameters is reduced, and an opposite disturbance strategy is introduced in the crossover operation to enhance the population diversity. Finally, the large and small inter-route train operation timetables are automatically generated according to the optimal solution. The application can flexibly adapt to different operation scenes and dynamic changes.

This application discloses a train timetable optimization method for urban rail transit networks considering transfer coordination, belonging to the field of urban rail transit technology. The method includes: acquiring network topology, transfer station information, train operation parameters, and passenger flow data; constructing a timetable optimization model and generating an initial timetable, considering constraints such as departure intervals, station dwell times, travel times, and turnaround times, with the objective of minimizing the total travel time and operating costs of passengers on the line; identifying the set of transfer stations and direction mappings based on the network topology, and extracting train arrival and departure events from the timetable; optimizing the train time windows at each transfer connection point with the objective of minimizing the total waiting time for transfers across the entire network, forming a time window contract; adding this contract to the optimization model and resolving it to obtain a new timetable; and outputting the final timetable by judging whether the improvement in transfer waiting time has converged. This method effectively improves the overall service level and economic benefits of multi-line networks while reducing computational complexity.

The application belongs to the technical field of intelligent traffic operation control, and particularly relates to a traffic intelligent operation management and control and cross-region linkage command system, which comprises a congestion pre-judgment module, an inflow evaluation module, an undertaking and shunting module, a recursive splitting module and a linkage command module. The congestion early warning value is calculated based on the speed difference of the boundary road section and the falling duration, the abnormal boundary road section is identified, the propagation timing relationship is determined in combination with the historical propagation time difference, and the potential congestion road section is locked in advance. The accumulated difference of the traffic flow within the preset duration is calculated, compared with the remaining storage capacity, and used to determine whether the congestion is intensified. When the shunting is triggered, the step-by-step distribution is performed in combination with the shunting priority value and the additional travel time, so as to avoid the overload of the alternative path or the insufficient shunting. Finally, the coordinated release period is determined according to the first-stage shunting and the step-by-step distribution result and in combination with the predicted arrival time, so that the dredging efficiency of the cross-region collaborative control is effectively exerted.

A corrosion prediction method includes: acquiring corrosion environment data and corrosion amount data of a vehicle, the corrosion environment data including at least temperature, humidity, salt concentration, and traveling time for each road surface state; constructing a corrosion prediction model using the corrosion environment data as an explanatory variable and the corrosion amount data as an objective variable; defining an explanatory variable having a high correlation with the corrosion amount as a corrosion promoting factor; acquiring vehicle data and meteorological data; constructing a corrosion promoting factor selection model using the vehicle data and the meteorological data as explanatory variables and the corrosion promoting factor as an objective variable; inputting the vehicle data and the meteorological data of a vehicle to be predicted to the corrosion promoting factor selection model and obtaining the corrosion promoting factor; and predicting a corrosion amount of the vehicle based on the obtained corrosion promoting factor.

Detecting network anomalies is provided. An amount of change between aggregate representational error data and number of hops data and average travel time data is determined based on comparing the aggregate representational error data that indicate expected network behavior for a network regarding data packet number of hops and average travel time in the network with the number of hops data and the average travel time data that indicate changes in the data packet number of hops and average travel time in the network. It is determined whether the amount of change between the aggregate representational error data and at least one of the number of hops data and the average travel time data is greater than a maximum amount of change threshold level. In response to determining that the amount of change is greater than the maximum amount of change threshold level, an anomaly is detected in the network.

A logistics return trip capacity intelligent matching system and method based on travel prediction, including, for each vehicle current collection period of multi-source heterogeneous data, capacity departure time window prediction, travel time estimation, space correction estimation, sudden factor correction estimation and bearable weight prediction, obtaining the capacity departure time window, the optimal return path, the return time and the estimated bearable weight of the target vehicle; based on the capacity departure time window, the optimal return path, the return time and the estimated bearable weight of the target vehicle, generate the capacity label, adapt the capacity label with the order information in multiple dimensions, obtain the comprehensive score of the capacity label, based on the comprehensive score of the capacity label, the order matching and state updating operation of the target vehicle, realize the accurate prediction of the capacity state, greatly reduce the invalid matching rate.

Method for controlling a mobility concept for traffic relief in an agglomeration by means of at least one driving unit (1, 1') operating autonomously with an electric drive unit and an energy distribution system with at least one electrical energy storage device (3) and for controlling an energy distribution by means of the energy distribution system, characterized in that - when a vehicle (2) requests a ride with a driving unit (1, 1'), the vehicle (2) transmits its planned route, an estimated charge level of its electrical energy storage upon arrival of the vehicle (2) at a pick-up point for a driving unit (1, 1'), an estimated energy requirement for carrying out an individual journey of the vehicle (2) from a starting point to a destination point, and an amount of electrical energy that the vehicle (2) may make available to the energy distribution system to an application program (A), - using the application program (A) depending on traffic data and / or weather data and / or energy costs relating to the route, a fare (M) for the vehicle (2) as well as a possible pick-up location and a predicted travel time are determined and sent to the vehicle (2) and to the driving unit (1, 1'), wherein - if the vehicle (2) is confirmed as having been taken along, a driving unit (1, 1') is assigned to the vehicle (2), whereby if the vehicle (2) uses the electrical energy provided by the energy distribution system during the transport of the vehicle (2) with the driving unit (1, 1') or if the vehicle (2) feeds energy into the energy distribution system of the driving unit (1, 1'), the determined transport price (M) for the vehicle (2) is adjusted accordingly.

This invention discloses an automatic task allocation method for AGVs based on distance and idle status, belonging to the field of automated guided vehicle (AGV) scheduling technology. It includes: a status perception module that collects the AGV's location, operating status, remaining power, and load status in real time and uploads it to an RDS scheduling system; the RDS scheduling system introduces a dynamic task priority quantification evaluation mechanism, calculating task priority scores and generating task sequences through a weighted scoring model; filtering idle AGVs, obtaining real-time road condition information and combining it with historical driving data to calculate the estimated travel time and estimated energy consumption cost of each idle AGV to the task starting point, constructing a comprehensive dynamic cost value; prioritizing the allocation of tasks to AGVs with the optimal comprehensive dynamic cost value, sufficient remaining power to meet task requirements, and matching load; locking AGV resources after task allocation, releasing them after task completion. This invention achieves dynamic intelligent allocation of AGV tasks, improving resource utilization and task response speed, and is suitable for multi-AGV collaborative operation scenarios.

This application provides a method, apparatus, device, and vehicle for predicting vehicle travel time, relating to the field of vehicle traffic technology. The method includes: acquiring the vehicle's historical travel times and historical travel trajectories; selecting multiple first moments where the dispersion of the vehicle's travel times meets preset conditions based on the historical travel times; selecting multiple second moments where there is overlap among the multiple travel trajectories based on the historical travel trajectories; calculating the similarity between the first and second moments and third moments in a preset dataset to obtain multiple fourth moments with a similarity greater than or equal to a preset value; and predicting the vehicle's target travel time based on the multiple fourth moments. This application can reduce the deviation between the predicted travel time and the user's actual travel time, making the predicted travel time closer to the user's actual travel time, and improving the accuracy of predicting vehicle travel time.

This invention belongs to the field of intelligent driving technology, specifically disclosing a method, system, and computer-readable storage medium for behavior planning at road ramps. Addressing the challenge of balancing traffic efficiency and safety in existing mixed traffic ramp scenarios for connected autonomous vehicles, the invention first establishes a communication model and a multi-view safety prediction model, generating a multi-view graph structure and quantified values ​​of driving behavior safety risks. Then, the behavior planning problem is constructed as an optimization problem with the objective of minimizing total travel time, constrained by collision-free operation and communication quality, and transformed into a graph Markov decision process. Finally, a graph reinforcement learning network is used to solve the problem, outputting the optimal lane-changing operation. This invention effectively balances traffic efficiency and driving safety while ensuring zero collisions and reliable communication, significantly improving decision convergence speed and driving stability.

The application relates to a vehicle travel information prediction method, device, equipment, medium and program product. The method comprises the following steps: firstly, obtaining multi-dimensional data of a vehicle, wherein the multi-dimensional data comprises historical travel data, schedule data, user habit data, environment data and user input data, the user habit data comprises charging habits and driving habits; then, determining a travel type of the vehicle in a prediction period according to the multi-dimensional data and a preset classification model; then, determining at least one candidate travel information of the vehicle in the prediction period according to the multi-dimensional data, the travel type and a preset time sequence prediction model, wherein the candidate travel information comprises candidate travel time, candidate travel mileage, candidate travel energy consumption and confidence; finally, correcting the at least one candidate travel information according to a preset correction model to determine at least one target travel information of the vehicle in the prediction period. The method can improve the accuracy of vehicle travel information prediction.

This application provides a method, apparatus, medium, and equipment for calculating the traffic flow of bauxite port access roads. The method involves constructing a geofence centered on a point of interest; acquiring trajectory data of heavy-duty trucks within the geofence; determining the starting and ending points; calculating the actual mileage and travel time of the heavy-duty trucks based on temporary stopping points; calculating the single-trip transport volume of the heavy-duty trucks based on their rated tonnage, actual mileage, and actual travel time; determining the geofence based on the port and corresponding interested companies; acquiring the trajectory data of heavy-duty trucks within the geofence; selecting transport routes with the target port as the starting point and the interested companies as the ending point; and calculating the single-trip transport volume of the heavy-duty trucks based on their rated tonnage, actual mileage, and actual travel time. This allows for accurate calculation of the heavy-duty truck traffic flow and transport volume of the bauxite port access roads corresponding to the target port while controlling the computational load.

A device may receive location data identifying a current location of a user equipment (UE) and traffic data associated with routes from the current location to a destination, and may retrieve historical data associated with travel times of the UE to the destination. The device may process the location data, the traffic data, and the historical data, with a model, to predict a time of arrival of the UE at the destination, and may determine that the UE is within a proximate distance of the destination based on the time of arrival. The device may provide, to a network device, an alert indicating that the UE is within the proximate distance and to cause the network device to generate a radio frequency signal for the UE.

The application discloses a method and system for vehicle path optimization in a single-vehicle channel under dynamic demand, acquires a path point request and adds the path point request to a request queue; a vehicle path optimization model is constructed, and according to a current position of the vehicle and the dynamic request queue, an optimal order of the request queue is dynamically calculated within a certain time limit after the request is sent, a next request is dynamically selected to minimize total travel time, and the next path point is determined; a priority queue is divided from the request queue according to a time when the request is sent, for the priority queue, a current target, a next target and a second next target are determined in sequence; when the vehicle reaches the next target, the next target is updated to the current target, and the second next target is updated to the next target, and the process is sequentially and circularly promoted to form a processing order until the requests in the priority queue are all responded.

The application provides an energy management optimization method and device, equipment and storage medium, relates to the technical field of hybrid vehicles, and the method comprises the following steps: when a hybrid vehicle is not started in navigation, it is determined that the owner is oil preference, a cloud server is triggered to determine a target travel route from historical repeated travel routes according to the current travel position and the current travel time of the hybrid vehicle, the oil saving amount corresponding to the historical repeated travel route is greater than the sum of the oil saving amounts of single routes obtained by splitting the historical repeated travel route into separate travel routes; the cloud server performs predicted energy management planning on the hybrid vehicle according to the target travel route, obtains a target battery power required by the target travel route, and sends the target battery power to the hybrid vehicle; and the hybrid vehicle performs a predicted energy management function according to the target battery power. The application can better exert the energy-saving potential of the powertrain of the hybrid vehicle and improve fuel efficiency.

A method and device for optimizing train departure path and timetable cooperatively, the method comprising: constructing a space-time state network for describing the operation of a train bottom; based on the space-time state network, constructing a train departure path and timetable cooperative optimization model; wherein the train departure path and timetable cooperative optimization model comprises a model for minimizing the space-time state path cost of the train bottom leaving a section and corresponding constraint conditions; using a Lagrange relaxation algorithm to transform the train departure path and timetable cooperative optimization model, and using a dynamic programming algorithm to solve the model to obtain an optimal train departure path and timetable. Through the method and device provided in the embodiment of the application, the train operation cost can be effectively reduced while the travel time of passengers is shortened, and the method can be used for assisting actual transportation production and improving the overall quality of train operation plans in departure time periods.

The application provides a rail transit abnormal state detection method, system, device and storage medium. The method comprises the following steps: acquiring AFC entry and exit, line network topology, train working diagram and other multi-source data, and preprocessing the multi-source data; extracting passenger riding total travel time from the preprocessed data and splitting and decoupling the passenger riding total travel time into real-time values of entry walking, exit walking, platform waiting, riding and transfer walking; and finally analyzing each time component real-time value, identifying and quantifying abnormal events, and forming an abnormal judgment result set. The application solves the problem that existing AFC data analysis cannot accurately locate the cause of travel time increase by mixing and overlapping each travel time component, realizes accurate differentiation and attribution of train operation, station passenger flow organization and other different dimension abnormalities through fine splitting and decoupling of total travel time, makes the formulation of operation scheduling strategy more targeted, and provides accurate and specific decision basis for rail transit operation and scheduling.

This application relates to a method, apparatus, equipment, and medium for scheduling engineering vehicles in complex road network environments. The method includes: acquiring static geographic data of physical nodes in the construction road network, engineering vehicle operating status data, and construction node operational status data to construct an initial dynamic spatiotemporal map for the current simulated scheduling cycle; obtaining the vehicle's global perception latent state vector through spatiotemporal map interactive attention network encoding; inputting the graph attention game network to complete game decision-making, generating the optimal response action command for the engineering vehicles, and aggregating the optimal response action commands into a joint scheduling strategy; iteratively optimizing the network model parameters based on the data updated in the simulated scheduling, and repeating the above steps in subsequent simulated scheduling cycles until the joint scheduling strategy reaches a Nash equilibrium state, outputting the joint scheduling strategy as an engineering vehicle scheduling scheme. This method can dynamically adjust the vehicle paths during the process, achieving the optimal scheduling goal of low energy consumption and short travel time.