使用大数据分析中型和重型车辆的长途与区域短途旅行
Kimberly Collins1, Raffi Der Wartanian2, Yunfei Hou3
1William and Barbara Leonard Transportation Center & Department of Public Administration, California State University, San Bernardino, 5500 University Parkway, San Bernardino CA 92407-2393, United States.
Data in brief
|March 5, 2025
概括
这一数据集区分了南加州中型和重型车辆 (MDHDV) 的区域短距离 (RSH) 和长距离 (LH) 旅行. 它支持了解车辆操作,以便过渡到零排放车辆.
科学领域:
- 运输科学 运输科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 中型和重型运输车辆 (MDHDV) 是城市排放的重要贡献者.
- 了解旅行模式对于有效的车队管理和减排战略至关重要.
- 过渡到零排放汽车 (ZEV) 需要详细的操作数据.
研究的目的:
- 创建一个数据集,区分区域短距离 (RSH) 和长距离 (LH) 旅行的MDHDVs.
- 提供MDHDV在内陆帝国,南加州的旅行模式的细粒度分析.
- 支持对ZEV采用和可持续运输的研究和政策制定.
主要方法:
- 通过与大数据供应商合作获取数据.
- 采用标准化的流量数据采集方法,包括旅行的起源,目的地和时间.
- 根据车辆分类和路线,将行程战略分类为RSH和LH.
主要成果:
- 一个全面的数据集,详细介绍了MDHDV的RSH和LH行程.
- 能够有效地区分短距离和长距离旅行模式.
- 为分析MDHDV运营动态提供了一个框架.
结论:
- 该数据集是分析MDHDV运营动态的关键资源.
- 促进知情的环境政策制定和城市/交通规划.
- 支持物流管理和战略过渡到零排放的MDHDVs.
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