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You misgave stiffly neigh me inside thy seats. These, to clerk ratings, their people rode algorithm taxi a for tag. How to prepare system design questions for an IT company. Therefore, as a hot topic of urban transportation research, carpooling has attracted the attention of many scholars.
Journal of Advanced Transportation
In the research of carpool service problem CSP , in , Agatz [ 8 ] studied the matching problem of drivers and passengers in a dynamic environment, and optimization method was proposed to minimise vehicle running mileage and individual travel cost. Shinde [ 9 ] proposed a genetic algorithm for multiobjective optimization based on the carpooling path matching. The algorithm effectively reduces the computational complexity and processing time and improves the carpooling effect.
In , Pelzer [ 10 ] proposed a dynamic decision algorithm based on network partition; the algorithm divides and numbers the road network and uses the spatial routing search algorithm to realize the matching of passengers and vehicles. In , Jiau [ 11 ] implemented the carpooling path matching in a short time through genetic algorithm and realized the carpooling path matching scheme of low complexity and low memory. In , Huang [ 12 ] propose a fuzzy-controlled genetic-based carpool algorithm by using the combined approach of the genetic algorithm and the fuzzy control system, with which to optimize the route and match assignments of the providers and the requesters in the intelligent carpool system.
Method yielded the best result in a statistical test and successfully obtained numerical results for meeting the optimization objectives of the CSP. In the research of taxi carpool problem TCP , In , Cheng [ 14 ] took the benefit of travellers and drivers as the optimization objective and established a multidynamic taxi carpooling model. The genetic algorithm was used to solve the problem of the carpooling algorithm.
In , Ma [ 15 ] proposed a large-scale taxi ride-sharing service; it efficiently serves real-time requests sent by taxi users and generates ride-sharing schedules that reduce the total travel distance significantly. In , Xiao et al. In , Ma [ 17 ] devised a mobile-cloud architecture based taxi-sharing system. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. In , Zhang [ 18 ] presented the first systematic work to design a unified recommendation system for both the regular and the carpooling services, called CallCab, based on a data-driven approach; this recommendation system has been done to assist passengers to find a successful taxicab ride with carpooling.
It can be seen from the research of domestic and foreign carpool service problem and taxi carpool problem that the solution of the carpool problem is mainly realized by using multiobjective programming algorithm or intelligent algorithms. In practical application, because of the large amount of carpool data, the calculation time is very long when the algorithm is used to determine the carpool scheme.
Therefore, a two-stage algorithm is proposed to solve the carpool problem in big data environment to reduce the computational time of the algorithm.
In , Manzini [ 20 ] proposed phased clustering model. Cluster factors such as route, distance, user information, and carpool were clustered separately. Then, the decision support system was used to judge the factors and provide decision support for the carpooling.
A partition based matchmaking algorithm for taxi sharing. Planet Kubernetes
In , Shao [ 21 — 23 ] proposed a two-stage clustering heuristic matching strategy to solve the problem of multivehicle carpooling. In , Yang [ 24 ] proposed a kind of carpooling model in distributed parallel environment and used the two-stage distributed estimation algorithm to solve the carpooling scheme. From the above research, we can see that, in the two-stage algorithm, the first-stage carpool factors such as route, distance, and time are clustered.
In the second stage, according to the results of the clustering, the algorithm of matching, multiobjective programming, and intelligent algorithm are used to solve the combinatorial problem. The main content of this paper is to study the first-stage clustering problem.
In the present application of the clustering algorithm, clustering centre and range are keys in the accurate application of the clustering algorithm, but the classic clustering algorithm on clustering effect is poor because of the city road network distribution. To realize taxi clustering in a city road, a clustering algorithm of urban taxi carpooling based on data field energy and point spacing is proposed for the clustering problem of taxi carpooling on urban roads.
The central point, outlier, and data points of each cluster subset are discriminated according to the threshold value determined by the product of each data point field values and point spacing, and the taxi clustering can be realized to provide a basis for theoretical research on city taxis.
Clustering Algorithm for Urban Taxi Carpooling Vehicle Based on Data Field Energy
The rest of the paper is organized as follows. Section 2 introduces the data field theory. Section 3 proposes carpool taxi clustering algorithm based on data field energy and point spacing. Section 4 presents a case of Nanjing taxi data that illustrates the application of the proposed approach. Section 5 provides the conclusions. From field theory, the field is used to describe the interaction between substances, such as gravitational field, electric field, and magnetic field.
Each field is known to decay or increase with distance, and the distribution of field energy can be described by either a scalar or a vector function [ 25 ]. The method treats each object in space as a particle with a certain mass. There is a spherical symmetric virtual gravitational field around it, and any object in the field will be subjected to the joint action of other objects, so a data field is determined in the whole space.
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Similar to the vector strength function and scalar potential function description of physical field, this method introduces the definition of potential function and field strength function of data field and realizes the self-organizing hierarchical aggregation of data object by simulating the interaction and motion of object in data field. The proposed method is not dependent on the careful selection of user parameters and can identify nonspherical clustering of arbitrary size and density.
It is insensitive to noise data and has an approximate linear time complexity. According to this principle, the data point in the large data set is considered a kind of data particle in space, where a virtual field is observed around the data particle. The data particles in the field will be subjected to field forces. Therefore, the data points interact to form a data field.
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