Abstract: The present application provides hyaluronic acid particles and a preparation method therefor. The preparation method comprises the following steps: carrying out airflow suspension on a hyaluronic acid raw material, and spraying an adhesive to the suspended hyaluronic acid raw material for granulation to obtain the hyaluronic acid particles, wherein the adhesive is an aqueous solution of water or hyaluronic acid. According to the preparation method of the present application, the dissolution rate of the HA can be significantly increased, the fluidity of the HA can also be improved, thereby avoiding caking, and reducing dust, waste and pollution, and it is more convenient to add the HA into a formula.

Abstract: Aspects concern a method for identifying a list of nearest vehicles including receiving a ride request comprising a request position; obtaining vehicle information of a plurality of vehicles, wherein the vehicle information comprises a vehicle position; translating the vehicle position information of each of the plurality of vehicles to positions on an edge based graph, wherein the edge based graph represents a road system; and determining a driving distance between at least one of the vehicle positions of the plurality of vehicles and the request position, wherein the driving distance comprises a distance the at least one vehicle travels along the road system to reach the request position.

Abstract: A communications server apparatus for a distributed sharding database related to a transportation service comprising a processor and a memory, the communications server apparatus being configured, under control of the processor, to execute instructions stored in the memory to: determine whether rebalancing of a plurality of shard replicas stored on a plurality of server instances is required based on two or more quantitative metrics; if rebalancing is required, select a plurality of candidate rebalancing plans; and rank the candidates, and execute a selected rebalancing plan from the ranking if two or more quantitative metrics criteria are met for the selection.

Abstract: Aspects concern a method for correcting errors in map data comprising obtaining map data specifying travel possibilities between locations, generating a routing graph from the map data by assigning a vertex to each location and an edge from one vertex to another vertex if the map data specifies that it is possible to travel from the location to which the first vertex is assigned to the location to which the second vertex is assigned, identifying the largest strongly connected component of the routing graph, identifying one or more further strongly connected components of the routing graph, detecting errors in the map data by identifying travel possibilities that are not in the map data which lead to missing connections between the one or more further strongly connected components of the routing graph and the largest strongly connected component of the routing graph and amending the map data by the identified travel possibilities.

Abstract: A method for generating map data comprising training a generator neural network by acquiring training data elements and training a generative adversarial network, comprising training a generator neural network to generate, for a satellite image and a road usage image of a training data element, the map data image of the training data element and comprising generating map data for a geographical region by acquiring road usage information specifying which parts of a geographical area have been used for driving a vehicle, acquiring a satellite image of the geographical area, forming a road usage image of the geographical area which has pixels, each pixel corresponding to a respective part of the geographical area and having a value indicating whether its part of the geographical area is specified by the road usage information to have been used; and feeding the satellite image and the road usage image to the trained generator neural network.

Abstract: A computer-implemented method of adaptively providing an estimated time of arrival and predicting a route likely to be taken by a ride for a ride by providing a personalized map for a driver for the ride. The method comprises: retrieving a recommended route for the ride: identifying historical ride information relating to the driver, the historical ride information identifying a driving behaviour of the driver based on rides that have been taken; and providing a personalized map for the driver based on the retrieved recommended route and the identified historical ride information so as adaptively provide an estimate time of arrival for a ride.

Abstract: A computer-implemented apparatus, and associated method, for predicting traffic conditions in respect of a specified route selected from within a road network representative of a geographical region.

Abstract: Aspects concern a method for correcting position estimates of a movable object. According to various embodiments, the method comprises establishing (1001) a hidden Markov model HMM, instance for a movable object and, for positioning times of a sequence of positioning times, receiving (1002) a position estimate from a positioning device of the movable object for a respective positioning time, determining (1003) a set of candidate path segments for the positioning time, determining (1004) likelihoods for the candidate path segments to correspond to the position estimate by application of the Viterbi algorithm to the HMM instance, expanding (1005) the HMM instance by the determined likelihoods for the candidate path segments for the positioning time and determining (1006) a corrected position estimate from a candidate path segment of the set of candidate path segments with the highest likelihood.

Abstract: A system configured for managing orders is disclosed. The system may include one or more processor(s) which may collect first 2D training image data; collect second 2D training image data; construct a 3D map for the geographical area based on the first training image data and the second training image data; determine a likelihood of a potential missing feature in the 3D map based on the first 2D training image data and the second 2D training image data; collect third 2D training image data comprising third map images of the geographical area acquired by the one or more image acquisition apparatus if the likelihood of the potential missing feature is above a predetermined threshold; and generate the road map based on the first 2D training image data, the second 2D training image data and the third 2D training image data.

Abstract: A communications server apparatus for a distributed sharding database related to a transportation service comprising a processor and a memory, the communications server apparatus being configured, under control of the processor, to execute instructions stored in the memory to: determine whether rebalancing of a plurality of shard replicas stored on a plurality of server instances is required based on two or more quantitative metrics; if rebalancing is required, select a plurality of candidate rebalancing plans; and rank the candidates, and execute a selected rebalancing plan from the ranking if two or more quantitative metrics criteria are met for the selection.

Abstract: Aspects concern a method for identifying a list of nearest vehicles including receiving a ride request comprising a request position; obtaining vehicle information of a plurality of vehicles, wherein the vehicle information comprises a vehicle position; translating the vehicle position information of each of the plurality of vehicles to positions on an edge based graph, wherein the edge based graph represents a road system; and determining a driving distance between at least one of the vehicle positions of the plurality of vehicles and the request position, wherein the driving distance comprises a distance the at least one vehicle travels along the road system to reach the request position.

Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classi-fying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabil-ities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Abstract: Aspects concern a method for correcting errors in map data comprising obtaining map data specifying travel possibilities between locations, generating a routing graph from the map data by assigning a vertex to each location and an edge from one vertex to another vertex if the map data specifies that it is possible to travel from the location to which the first vertex is assigned to the location to which the second vertex is assigned, identifying the largest strongly connected component of the routing graph, identifying one or more further strongly connected components of the routing graph, detecting errors in the map data by identifying travel possibilities that are not in the map data which lead to missing connections between the one or more further strongly connected components of the routing graph and the largest strongly connected component of the routing graph and amending the map data by the identified travel possibilities.

Abstract: Aspects concern a method for correcting position estimates of a movable object. According to various embodiments, the method comprises establishing (1001) a hidden Markov model HMM, instance for a movable object and, for positioning times of a sequence of positioning times, receiving (1002) a position estimate from a positioning device of the movable object for a respective positioning time, determining (1003) a set of candidate path segments for the positioning time, determining (1004) likelihoods for the candidate path segments to correspond to the position estimate by application of the Viterbi algorithm to the HMM instance, expanding (1005) the HMM instance by the determined likelihoods for the candidate path segments for the positioning time and determining (1006) a corrected position estimate from a candidate path segment of the set of candidate path segments with the highest likelihood.

Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classifying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabilities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Abstract: Aspects concern a method for correcting errors in map data comprising obtaining map data specifying travel possibilities between locations, generating a routing graph from the map data by assigning a vertex to each location and an edge from one vertex to another vertex if the map data specifies that it is possible to travel from the location to which the first vertex is assigned to the location to which the second vertex is assigned, identifying the largest strongly connected component of the routing graph, identifying one or more further strongly connected components of the routing graph, detecting errors in the map data by identifying travel possibilities that are not in the map data which lead to missing connections between the one or more further strongly connected components of the routing graph and the largest strongly connected component of the routing graph and amending the map data by the identified travel possibilities.

Abstract: Aspects concern a method for correcting position estimates of a movable object. According to various embodiments, the method comprises establishing (1001) a hidden Markov model, HMM, instance for a movable object and, for positioning times of a sequence of positioning times, receiving (1002) a position estimate from a positioning device of the movable object for a respective positioning time, determining (1003) a set of candidate path segments for the positioning time, determining (1004) likelihoods for the candidate path segments to correspond to the position estimate by application of the Viterbi algorithm to the HMM instance, expanding (1005) the HMM instance by the determined likelihoods for the candidate path segments for the positioning time and determining (1006) a corrected position estimate from a candidate path segment of the set of candidate path segments with the highest likelihood.

Abstract: A method for generating map data comprising training a generator neural network by acquiring training data elements and training a generative adversarial network, comprising training a generator neural network to generate, for a satellite image and a road usage image of a training data element, the map data image of the training data element and comprising generating map data for a geographical region by acquiring road usage information specifying which parts of a geographical area have been used for driving a vehicle, acquiring a satellite image of the geographical area, forming a road usage image of the geographical area which has pixels, each pixel corresponding to a respective part of the geographical area and having a value indicating whether its part of the geographical area is specified by the road usage information to have been used; and feeding the satellite image and the road usage image to the trained generator neural network.

Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classifying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabilities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Abstract: A communications server apparatus for managing a request for transport-related services, which is configured to, in response to receiving user request data, generate a data record having a plurality of transit point data fields having data for a corresponding plurality of transit points from an origin to a destination, including a variable transit point data field having data that is determined based on data associated with at least one transportation network-related parameter, and having a plurality of trip section data fields for a corresponding plurality of trip sections defining navigation directions from the origin to the destination, and, for each trip section data field, to associate the trip section data field with a respective transit point data field, and, based on the data of the associated transit point data field, to determine a respective transportation mode and to generate transit data in respect of the respective transportation mode.