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Generalized multipath planning model for ride-sharing systems

机译:乘车共享系统的通用多路径规划模型

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摘要

Ride-sharing systems should combine environmental protection (through a reduction of fossil fuel usage), socialization, and security. Encouraging people to use ride-sharing systems by satisfying their demands for safety, privacy and convenience is challenging. Most previous works on this topic have focused on rinding a fixed path between the driver and the riders either based solely on their locations 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 characteristics of ride-sharing systems. In this paper, we formalized the ride-sharing problem as a multi source-destination path planning problem. An objective function that models different objectives 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 allows 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 optimal paths against different user-defined objective functions.
机译:乘车共享系统应结合环境保护(减少化石燃料的使用),社会化和安全性。通过满足人们对安全性,隐私性和便利性的需求来鼓励人们使用乘车共享系统具有挑战性。以前有关该主题的大多数工作都集中于仅基于驾驶员的位置或使用社交信息在驾驶员和骑手之间固定路径。驾驶员和骑手缺乏根据自己的偏好和要求来改变或计算路径的选择是有问题的。随着移动社交网络技术的进步,有必要重新考虑乘车共享系统的原理和期望的特性。在本文中,我们将乘车共享问题形式化为多源-目的地路径规划问题。开发了在统一框架中为不同目标建模的目标功能。此外,我们提供了一个相似性模型,该模型可以反映出游乐设施的个人喜好,并利用社交媒体来获取车手和驾驶员的当前利益。该模型还允许每个驾驶员通过适当调整权重,根据自己的需求生成次优路径。两项案例研究表明,我们的系统有可能找到最佳匹配,并针对不同的用户定义目标函数计算多个最佳路径。

著录项

  • 来源
    《Frontiers of computer science in China》 |2014年第1期|100-118|共19页
  • 作者单位

    Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;

    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;

    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;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ride-sharing; path planning; dynamic optimization;

    机译:拼车;路径规划;动态优化;

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