Clustering Algorithm for Urban Taxi Carpooling Vehicle Based on Data Field Energy

Abstract

A clustering algorithm for urban taxi carpooling based on data field energy and point spacing is proposed to solve the clustering problem of taxi carpooling on urban roads. The data field energy function is used to calculate the field energy of each data point in the passenger taxi offpoint dataset. To realize the clustering of taxis, 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. The classical algorithm and proposed algorithm are compared and analyzed by using the compactness, separation, and Dunn validity index. The clustering results of the proposed algorithm are better than those of the classical clustering algorithm. In the case of cluster numbers 25, 249, 409, and 599, the algorithm has good clustering results for the taxi trajectory dataset with certain regularity in space distribution and irregular distribution in time distribution. This algorithm is suitable for the clustering of vehicles in urban traffic roads, which can provide new ideas and methods for the cluster study of urban traffic vehicles.

An Improved Mixed Distribution Model of Time Headway for Urban Roads Based on a New Traffic State Classification Method

“…Clustering algorithms have been widely applied to discover homogeneous and heterogeneous characteristics of traffic flow [39], [40]. Some researchers found that K-means clustering is more suitable for classifying traffic state than Fuzzy clustering, clustering large-scale application, 2 and 3 nearest neighbor algorithm, and artificial neural network [41], [42].…”

Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

“…The clustering methods group road links according to their similarity in terms of traffic properties. The clustering methods have wide applications in traffic pattern extraction as they can consider various traffic features [14][15][16]. To obtain more accurate results, several NN methods are applied in traffic data analysis, such as local artificial neural networks [20], fuzzy neural networks [8], and sequence graph neural networks [21].…”

“…Several transport attributes have been recognized from various data sources, for example, road network [6], travel speed [7], travel volume [8], and traffic congestion [9]. A set of effective approaches has been developed for analyzing road traffic, which includes statistics-based methods [10][11][12][13] and clustering-based methods [14][15][16]. For example, Zou et al [17] examined road traffic using long-term vehicle trajectories and identified the spatial dependency of the traffic state via spatial autocorrelation.…”

A New Model of Comparative Directions and Profile Matching in the Carpooling Computational Systems

“…In this approach every participated motor vehicle solve its own DQN separately having proper coordination. Xiao Qiang et al, [5] suggested the carpooling using taxi in urban a clustering algorithm which rely on data pasture energy along with position spacing is prepared to explicate the difficultness of clustering in taxi carpooling which runs on urban roads. The basic purpose of using data pasture energy is to calculate the energy at the ground level for every data point of traveler taxi off point dataset.…”