...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Transportation research >A distributionally robust optimization approach to reconstructing missing locations and paths using high-frequency trajectory data
【24h】

A distributionally robust optimization approach to reconstructing missing locations and paths using high-frequency trajectory data

机译：使用高频轨迹数据重构缺失位置和路径的分布式鲁棒优化方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Daily high-frequency trajectory data (e.g., 0.1-s connected vehicle data) provide a promising foundation to improve the observability of travel demand dynamics. However, the raw trajectories are not always accurate and complete due to technical and privacy issues. This paper proposes a data-driven optimization modeling framework to reconstruct the location-duration-path choices for the missing observations from the incomplete trajectories. By processing many-day raw trajectories, we observe a set of historical choices of location-duration-path and identify missing observations in space and time dimensions. To improve computational efficiency, we apply data-driven network-time prisms that reduce the search space for the missing choices. Then, we formulate Distributionally Robust Optimization (DRO) models with likelihood bounds, a special case of data-driven optimization models using phi-divergences (i.e., chi(2) distance), to reconstruct the missing choices. To solve the minimax programs of the DRO models while maintaining tractability, we reformulate and solve the equivalent dual problems of the DRO models based on the strong duality theory. To demonstrate and validate the proposed models, we use a real-world connected vehicle dataset containing around 2,800 connected vehicles over two separate months in Southeast Michigan from the Safety Pilot Model Deployment (SPMD) project and a transportation network from OpenStreetMap.
机译：每日高频轨迹数据（例如，连接的0.1秒车辆数据）为改善旅行需求动态的可观察性提供了有希望的基础。但是，由于技术和隐私问题，原始轨迹并不总是准确和完整的。本文提出了一种数据驱动的优化建模框架，以从不完整的轨迹中重建缺失观测值的位置持续时间路径选择。通过处理许多天的原始轨迹，我们观察了位置-持续时间-路径的一组历史选择，并确定了在时空维度上缺少的观察结果。为了提高计算效率，我们应用了数据驱动的网络时间棱镜，以减少缺少选择的搜索空间。然后，我们用似然范围来公式化分布式鲁棒优化（DRO）模型，这是使用phi散度（即chi（2）距离）进行数据驱动的优化模型的特殊情况，以重构缺失的选择。为了在保持可处理性的同时解决DRO模型的minimax程序，我们基于强对偶理论重新制定并解决了DRO模型的等效对偶问题。为了演示和验证所提议的模型，我们使用了一个现实世界的联网车辆数据集，其中包含来自安全试点模型部署（SPMD）项目的两个单独月份在密歇根州东南部的约2,800辆联网车辆，以及来自OpenStreetMap的交通网络。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号