Intensive Scheduling Method of Ride-Sharing Based on Demand Density Prediction

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更新时间：2024-05-16

- Intensive Scheduling Method of Ride-Sharing Based on Demand Density Prediction
- “科技新闻记者报道，网约车领域迎来新突破。研究团队提出基于需求密度预测的集约化调度方法，通过深度时空残差感知网络准确预测需求密度，并结合经济效益设计调度模型。实验验证，预测模型精度高达97%，调度算法质量接近最优解，有望显著提升网约车接单率和利润率，实现全局供需平衡，为交通系统稳定提供有力支持。”
  
  ”
- Software Guide Vol. 23, Issue 4, Pages: 21-30(2024)
- 作者机构：
  
  1.辽宁工程技术大学软件学院，辽宁葫芦岛 125105
  2.浙江科技学院理学院，浙江杭州 310023
- 作者简介：
- 基金信息：
- DOI：10.11907/rjdk.231463
  CLC： TP302
- Published：15 April 2024，
  
  Received：04 May 2023，
扫描看全文
郭羽含,丁文婧.基于需求密度预测的网约车集约化调度方法[J].软件导刊,2024,23(04):21-30.

GUO Yuhan,DING Wenjing.Intensive Scheduling Method of Ride-Sharing Based on Demand Density Prediction[J].Software Guide,2024,23(04):21-30.
郭羽含,丁文婧.基于需求密度预测的网约车集约化调度方法[J].软件导刊,2024,23(04):21-30. DOI： 10.11907/rjdk.231463.

GUO Yuhan,DING Wenjing.Intensive Scheduling Method of Ride-Sharing Based on Demand Density Prediction[J].Software Guide,2024,23(04):21-30. DOI： 10.11907/rjdk.231463.

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