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

Xiao Qiang,Yao Shuang-Shuang

doi:10.1155/2018/3853012

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.

Highlights

The acceleration of urbanization and the rapid increase in the number of travel vehicles have caused traffic congestion on urban roads to become a major problem that must be solved in urban development
In 2017, 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 datadriven 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
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

Summary

Introduction

The acceleration of urbanization and the rapid increase in the number of travel vehicles have caused traffic congestion on urban roads to become a major problem that must be solved in urban development. In 2017, 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 datadriven 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. 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.

Data Field Energy

Carpool Taxi Clustering Algorithm

Case Study

Conclusion