The invention provides an intelligent matching and route optimization-base carpooling method and system. The method includes the following steps that: a passenger travel information set and a vehicle state information set are acquired; spatial-temporal distances between passengers and vehicles are calculated, a passenger and vehicle matching candidate set is created according to the calculated spatial-temporal distances, the travel time of the passengers and the number of the passengers; an integer planning model is created, and passenger and vehicle matching results are obtained according to the created integer planning model; and based on the combination of the passenger and vehicle matching results and real-time traffic states, time-varying Dijkstra's algorithm or a time-varying A star algorithm is utilized to obtain optimal vehicle driving paths. According to the intelligent matching and route optimization-base carpooling method, dynamic traffic information is utilized to measure the spatial-temporal proximity of the travel demands of individuals; spatial-temporal locality is adopted to guide group intelligent carpooling matching; and dynamic vehicle paths with minimum cost are designed; and therefore, spatial-temporal precise matching of massive travel demand services and efficient path planning can be realized.

Intelligent matching and route optimization-base carpooling method and system

The invention discloses an initial user selection method in social network influence spreading Firstly k-shell decomposition is performed on a social network; then the number of initial users required to be selected by each shell is calculated, and the shells of which the number of initial users is not zero are selected to act as candidate shells; and the initial users are selected for each candidate shell, and the initial users form the initial user sets of the social network, wherein the user of the largest degree in the candidate shells is firstly selected to act as the first initial user in initial user selection, then the mean shortest path MSP from the initial users to the corresponding activation set is acquired according to an influence spreading model, and the next initial user is selected according to the MSP step of neighbor sets and the activation user sets of the non-activated users The initial user selection method is improved based on k-shell decomposition so that the efficiency and the effectiveness of initial user selection in the large-scale social network can be effectively enhanced

Initial user selection method in social network influence spreading

The invention discloses a cooperative car-pooling route selection method with uncertain demands in an Internet of Vehicles environment. The cooperative car-pooling route selection method is characterized by comprising the steps of: firstly, reading relevant data such as distance and traffic flow of each route within a car-pooling service range in real time by adopting the Internet of Vehicles technology; importing the obtained data into a cooperative car-pooling route selection model with uncertain demands in the Internet of Vehicles environment, wherein the construction steps of the model mainly comprises calculation of car-pooling cooperative route fee, calculation of cooperative route time, calculation of the optimal route under a single index by adopting a distance matrix method, and calculation of the optimal route under comprehensive index by adopting a coefficient method; and finally recommending the optimal driving route for a driver in real time. The cooperative car-pooling route selection method aims at different demands of different car-pooling users, makes the selection of car-pooling routes of a private car more user-friendly, reduces the total distance, total fee and total time of travel by means of cooperative car-pooling, and realizes economized traveling.

Cooperative car-pooling route selection method with uncertain demand in Internet of Vehicles environment

The invention discloses an attribute graph model based neighbor search method The method comprises the steps of defining a vertex set search question as an attribute graph model; solving a top-k vertex set with a minimum diameter in the attribute graph from a global perspective; and obtaining a feasible solution space through policies such as progressive search, a rarest first algorithm and the like According to the method, the scheme for solving the top-k vertex set with the minimum diameter in the attribute graph in the global condition can be formed, so that the problem in solving the minimum vertex set in the attribute graph is optimized in time and space complexity during a solving process and the premature convergence is avoided

Attribute graph model based neighbor search method

A computing method for ridesharing paths, a computing apparatus and a recording medium using the same are provided. This invention finds the ridesharing paths out from the target trajectory which have ridesharing relationship with a plurality of candidate trajectories. People on the ridesharing paths can share a ride on a part of or whole target trajectory. In the computing method, a trajectory is transformed into a sequence of blocks. After target trajectory and a plurality of the candidate trajectories are transformed, it is determined whether each of the blocks of the candidate trajectories overlaps with at least one of the blocks of the target trajectory. Then, ridesharing paths are calculated according the screened candidate trajectories. This method is many-to-one method which compares a plurality of the candidate trajectories to one target trajectory at one time.

Computing method for ridesharing paths, computing apparatus and recording medium using the same

The invention provides an optimal multi-meeting-point route searching method and apparatus applied to real-time ridesharing. The method comprises acquiring a route searching preset information including a graph G=(V, E and W), a set of points U, [alpha], a starting point s, and a terminal point t; initializing a queue Q and a set D, and adding an initial state into the queue Q; repeating the following steps when the queue is not empty: A. popping up a first element ((v,X), cost) in the queue Q, B. returning cost if v is equal to t and X is equal to U, C. adding a state (v,X) into the set D, D. circulating all (v,u) edges in a set E, and updating (Q, D, (v,X), cost + [alpha] *w(v,u)), and E. circulating all x points in a set U-X, and updating (Q, D, (v, X union of {x}), cost +(1-[alpha])*dist (x,v)); and obtaining the optimal route after circulation. The optimal multi-meeting-point route searching method can efficiently solve the technical issue not yet solved in present real time ridesharing applications, that is a driver can quickly determine the optimal route to take all coupling passengers after the driver and the passengers are coupled, so the invention fill in gaps in correlation technology of present real time ridesharing applications.

Optimal multi-meeting-point route searching method and apparatus applied to real-time ridesharing

The invention discloses a community search algorithm based on a k-kernel The community search algorithm includes the steps of growing a maximum spanning tree (MST) for an image, preprocessing the MST, finding a subtree for connecting all search nodes from the MST, finding a subtree with the search nodes, and returning the maximum K-kernel By means of the community search algorithm, the k-kernel with a designated node can be found in the time complexity O(T), the value of k is the maximum, and T represents the size of a community where search needs to be conducted

Community search algorithm based on k-kernel

The invention discloses a generalized maximum degree random walk graph sampling algorithm. The generalized maximum degree random walk graph sampling algorithm comprises the following steps of enabling a sample to walk on a graph randomly; and performing unbiased estimation according to the sample. A 'large deviation problem' of an RW algorithm and a 'repeated sample problem' of an MD algorithm can be solved effectively, so that the overall efficiency on sample points acquired from the internet is improved.

Generalized maximum degree random walk graph sampling algorithm

The invention provides a method and device for estimating user influences in social networking services based on the graph simplification technology. The method includes the steps: (1) a probability graph G of the social networking services with the user influences to be estimated is obtained, and the number N, the node U and parameters r and t of possibly-extracted graphs are preset; (2) a recursion stratified sampling algorithm and the graph simplification technology are used for estimating the influences of the node u in the probability graph G. According to the embodiment, the graph simplification technology is integrated into a recursion stratified sampling method based on the graph simplification technology; on one hand, the nodes and the edges irrelevant to user influence estimation can be rapidly cut off, so that rapid influence estimation can be achieved; on the other hand, in the graph simplification process, the edges irrelevant to node influence calculation are prevented from being selected in the recursion stratified sampling process for stratifying, and therefore the accuracy of the algorithm is improved. Generally, compared with an existing method, the recursion stratified sampling method based on the graph simplification technology has the higher speed and the higher accuracy.

Method and device for estimating user influences in social networking services based on graph simplification technology

The invention discloses a novel distance-based algorithm for solving a representative node set in a two-dimensional space. The novel distance-based algorithm comprises the steps of inputting a data set; calculating the Skyline point set Q in the data set through the BNL algorithm; sequencing the point set Q; solving the manhattan distance value from the initial point to any other Skyline point and storing; solving k representative Skyline points in the Skyline point set; returning the k representative Skyline points. According to the algorithm, the time complexity is shown as O (K2LOG3m) which is far less than that of the DRS algorithm in the prior art.

Distance-based algorithm for solving representative node set in two-dimensional space

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