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最小的出租车车队算法考虑人类的时空行为
Zhi-Dan Zhao1,2, Yu Wang1,2, Wei-Peng Nie3,4,5
1Complexity Computation Lab, Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China.
Chaos (Woodbury, N.Y.)
|July 1, 2023
概括
本研究介绍了用于城市旅行计划的空间时间霍普克罗夫特-卡普 (STHK) 算法. 该算法优化了车队规模,减少了旅行时间和距离,从而降低了排放.
科学领域:
- 城市规划是城市规划.
- 运输科学 运输科学
- 计算机科学 计算机科学
背景情况:
- 越来越多的旅行数据有助于研究用户行为.
- 用户旅行计划对于理论和实际应用至关重要.
研究的目的:
- 开发一个高效的城市旅行安排解决方案.
- 尽量减少车队规模,同时考虑行程时间和距离.
主要方法:
- 提出了空间时间霍普克罗夫特-卡普 (STHK) 算法.
- 开发了一个考虑时间和空间成本的旅行安排解决方案.
主要成果:
- STHK算法减少了81%的车队卸载时间和58%的卸载距离.
- 算法保留了人类旅行行为的异质特征.
- 优化的车队规模满足了城市旅行的需求.
结论:
- 该STHK算法有效地减少了旅行时间,距离,能源消耗和碳排放.
- 旅行计划结果与人类旅行特征保持一致.
- 该算法在城市移动方面具有重要的理论和实践价值.
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