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.

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: 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: The disclosure relates to a method of predicting one or more road attributes. The method may include providing trajectory data of a geographical area. The method may further include providing map data, wherein the map data may include image data of the geographical area. The method may further include extracting trajectory features from the trajectory data and extracting map features from the map data. The method may further include using at least one processor to predict road attributes by inputting the trajectory features and the map features in a neural network and by classifying an output of the neural network into prediction probabilities of the road attributes. The disclosure also relates to a data processing system; to a non-transitory computer-readable medium storing computer executable code; and to a method of training an automated predictor.

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.

Abstract: Method and device for real-time error bounded and relay bounded map matching. The method for map matching comprises of modelling each road arc as a hidden state and each location measurement as an observation emitted by the hidden state using a Hidden Markov Model, decoding each road arc and each location measurement using a Viterbi algorithm and outputting a matching road arc, wherein the outputting is delayed by a delay time in response to an optimal trade-off between selection accuracy and selection latency.

Abstract: There is provided a bandwidth prediction apparatus. An acquisition unit is configured to acquire position information indicating a first position at which a bandwidth is to be predicted. A prediction unit is configured to predict a bandwidth at the first position by using respective measured values of bandwidths measured in advance at a plurality of second positions.

Abstract: Embodiments of methods for multimedia annotation with sensor data (referred to herein as Sensor-rich video) includes acquisition, management, storage, indexing, transmission, search, and display of video, images, or sound, that has been recorded in conjunction with additional sensor information (such as, but not limited to, global positioning system information (latitude, longitude, altitude), compass directions, WiFi fingerprints, ambient lighting conditions, etc.). The collection of sensor information is acquired on a continuous basis during recording. For example, the GPS information may be continuously acquired from a corresponding sensor at every second during the recording of a video. Therefore, the acquisition apparatus generates a continuous stream of video frames and a continuous stream of sensor meta-data values. The two streams are correlated in that every video frame is associated with a set of sensor values. Note that the sampling frequency (i.e.

Abstract: The present invention provides a bacillus sp. having a deposit access number of CGMCC NO. 5744 and a hyaluronidase produced by the bacillus and the amino acid sequence of the hyaluronidase is shown in SEQ ID NO: 2. The present invention further relates to a process for preparing oligomeric hyaluronic acid or salts thereof or low-molecular-weight hyaluronic acid or salts thereof by using the bacillus or the hyaluronidase produced thereby. The produced oligomeric hyaluronates or low-molecular-weight hyaluronates have advantages such as good transdermal absorption ability, high purity, no cytotoxicity, potent antioxidant ability. The present invention also provides use of the bacillus having a deposit access number of CGMCC NO. 5744, or the hyaluronidase, oligomeric hyaluronates or salts thereof, low-molecular-weight hyaluronates or salts thereof produced by the bacillus in the fields of osmetics, food products and medicines.

Abstract: There is provided a bandwidth prediction apparatus. An acquisition unit is configured to acquire position information indicating a first position at which a bandwidth is to be predicted. A prediction unit is configured to predict a bandwidth at the first position by using respective measured values of bandwidths measured in advance at a plurality of second positions.

Abstract: The present invention provides a bacillus sp. having a deposit access number of CGMCC NO. 5744 and a hyaluronidase produced by the bacillus and the amino acid sequence of the hyaluronidase is shown in SEQ ID NO: 2. The present invention further relates to a process for preparing oligomeric hyaluronic acid or salts thereof or low-molecular-weight hyaluronic acid or salts thereof by using the bacillus or the hyaluronidase produced thereby. The produced oligomeric hyaluronates or low-molecular-weight hyaluronates have advantages such as good transdermal absorption ability, high purity, no cytotoxicity, potent antioxidant ability. The present invention also provides use of the bacillus having a deposit access number of CGMCC NO. 5744, or the hyaluronidase, oligomeric hyaluronates or salts thereof, low-molecular-weight hyaluronates or salts thereof produced by the bacillus in the fields of osmetics, food products and medicines.