多式联运集装箱在途定位与跟踪数据集

集装箱的定位和跟踪正成为集装箱运输的迫切需求运输。地图匹配算法已被广泛应用于校正定位误差。因为集装箱轨迹具有采样率低和GPS点缺失的特点,基于最短路径原理的地图匹配算法不适用于集装箱拍卖和跟踪。采用一种基于历史轨迹的地图匹配算法挖掘历史轨迹中的旅行时间和频率，确定给定集装箱的位置。通过调研深圳的货车、集装箱、物流园区、港口等实际数据，采集可靠的基础数据，设计高效的集装箱定位与追踪算法，采用Python优化生成完整与精准的定位数据，数据容量大约1.45GB。

Positioning and tracking of containers have become an urgent demand in container transportation. Map matching algorithms have been widely used to correct positioning errors. As container trajectories are characterized by low sampling rates and missing GPS points, map matching algorithms based on the shortest path principle are not applicable to container auction and tracking. This study adopts a historical trajectory-based map matching algorithm to mine travel time and frequency information from historical trajectories, so as to determine the position of a given container. By collecting reliable basic data through investigating actual scenarios including trucks, containers, logistics parks, ports and other related entities in Shenzhen, an efficient container positioning and tracking algorithm is designed, and Python is utilized for optimization to generate complete and accurate positioning data, with a total data capacity of approximately 1.45 GB.

搜集汇总

背景与挑战

背景概述

该数据集是一个关于多式联运集装箱在途定位与跟踪的数据集，容量约为1.45GB，包含31846个文件，数据格式多样。数据集基于深圳的货车、集装箱、物流园区、港口等实际数据，采用基于历史轨迹的地图匹配算法和Python优化生成，旨在解决集装箱轨迹采样率低和GPS点缺失的问题。