一个统一的数据集用于20个美国城市的城市规模交通分配模型
Xiaotong Xu1, Zhenjie Zheng2, Zijian Hu1
1Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China.
Scientific data
|March 30, 2024
概括
这项研究引入了一种新方法来生成城市规模的交通数据,克服传感器限制和隐私问题. 该方法为多个城市的城市研究提供了全面的,验证的交通数据.
科学领域:
- 城市研究 城市研究
- 运输科学 运输科学
- 数据科学数据科学数据科学
背景情况:
- 城市规模的交通数据对于城市研究至关重要,但受到传感器可用性和隐私问题的限制.
- 现有的数据集往往不完整,局部化,缺乏全面的数据类型,阻碍了城市研究.
- 需要一个公开可用的,城市规模的交通数据集,具有完整的数据类型和高质量的数据.
研究的目的:
- 开发一种统一的方法来生成全面的,城市规模的交通数据.
- 在规模,完整性和可访问性方面解决当前交通数据集的局限性.
- 为城市交通,环境和能源研究创建一个验证的,公开可用的资源.
主要方法:
- 使用了来自运输研究的经典交通分配模型.
- 来自开放的公共数据库的输入数据,包括道路网络,交通需求和旅行时间.
- 应用了这种方法来生成20个美国城市的全市交通数据.
主要成果:
- 平均旅行时间达到0.79的平均相关系数.
- 对于平均行驶速度,获得了5.16%的平均相对误差.
- 证明了该方法能够产生验证的,城市规模的交通数据的能力.
结论:
- 拟议的统一方法有效地产生了全面和验证的城市规模的交通数据.
- 这种方法克服了现有数据集的局限性,使得更广泛的城市研究成为可能.
- 产生的数据具有促进交通,环境和能源研究的巨大潜力.
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