基于iTransformer的增强早期预警系统,用于预测违反汽车租合同的情况
Ming Jiang1, Dongpeng Peng1, Haihan Yu1
1School of Internet Economics and Business, Fujian University of Technology, Fuzhou, China.
PloS one
|March 20, 2025
概括
本研究引入了汽车租行业的早期预警系统,以应对客户违约造成的经济损失. 通过预测车辆轨迹,它最大限度地降低了财务风险,提高了运营效率.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 运输系统 运输系统
背景情况:
- 客户违约在汽车租行业中构成重大经济风险,导致财务损失和运营效率低下.
- 车辆租市场的不断扩张加剧了人们对经济损失,车辆置以及由于客户不合规而导致的服务质量下降的担忧.
研究的目的:
- 为汽车租行业开发创新的早期预警系统,以减轻客户违约造成的经济损失.
- 实现实时监控潜在客户违约情况,并预测车辆轨迹,以防止租公司的财务损失.
主要方法:
- 采用了改进的DBSCAN集群技术,优化了除雪优化器 (SAO) 算法,用于建立电子屏障.
- 集成iTransformer模型用于准确预测车辆轨迹,利用车辆驾驶数据.
- 该系统分析车辆的驾驶数据,以确定居民位置和预测未来的移动,有效地防止客户的默认.
主要成果:
- 开发的系统在预测车辆居住位置和未来轨迹方面表现出高效.
- 实现了0.001的平均平方误差 (MSE),0.003的平均绝对误差 (MAE) 和0.08公里的位置误差,证实了模型的准确性.
- 该系统成功地识别并防止了潜在的违约风险,减少了汽车租企业的经济损失.
结论:
- 提议的早期预警系统通过主动解决客户违约问题,有效地减少了汽车租行业的经济损失.
- 该系统提高了运营效率,并为租公司提供了财务保障,支持汽车租部门的可持续发展.
- 为客户提供关于潜在违约行为的及时警告,进一步保护企业利益并改善行业实践.
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