Generalized multipath planning model for ride-sharing systems

作     者：Jamal YOUSAF JuanziLI Lu CHEN Jie TANG Xiaowen DAI 

作者机构：Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China. General Motors China Science Lab Shanghai 201303 China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2014年第8卷第1期

页      面：100-118页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081201[工学-计算机系统结构] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the General Motors  China Science Lab  Shanghai 

主　　题：ride-sharing path planning dynamic optimiza-tion 

摘      要：Ride-sharing systems should combine environ- mental protection （through a reduction of fossil fuel usage）, socialization, and security. Encouraging people to use ride- sharing systems by satisfying their demands for safety, pri- vacy and convenience is challenging. Most previous works on this topic have focused on finding a fixed path between the driver and the riders either based solely on their loca- tions or using social information. The drivers＇ and riders＇ lack of options to change or compute the path according to their own preferences and requirements is problematic. With the advancement of mobile social networking technologies, it is necessary to reconsider the principles and desired character- istics of ride-sharing systems. In this paper, we formalized the ride-sharing problem as a multi source-destination path plan- ning problem. An objective function that models different ob- jectives in a unified framework was developed. Moreover, we provide a similarity model, which can reflect the personal preferences of the rides and utilize social media to obtain the current interests of the riders and drivers. The model also al- lows each driver to generate sub-optimal paths according to his own requirements by suitably adjusting the weights. Two case studies have shown that our system has the potential to find the best possible match and computes the multiple opti- mal paths against different user-defined objective functions.