使用机器学习预测星桥腾飞的停车场容量
公司规模
Large Corporate
地区
- Asia
国家
- Singapore
产品
- DataRobot’s automated machine learning platform
- DataRobot’s Time Series functionality
技术栈
- Machine Learning
- Time Series Analysis
实施规模
- Enterprise-wide Deployment
影响指标
- Revenue Growth
- Customer Satisfaction
技术
- 分析与建模 - 预测分析
- 分析与建模 - 机器学习
适用功能
- 设施管理
用例
- 预测性维护
服务
- 数据科学服务
关于客户
星桥腾飞集团 (ASG) 是亚洲领先的可持续城市和商业空间解决方案提供商，其全球管理资产总额超过 200 亿美元。该集团总部位于新加坡，业务遍及亚洲、澳大利亚、欧洲和美国的 11 个国家。他们专注于提高新加坡和整个亚洲众多物业的停车场效率。该公司在澳大利亚、中国、印度、印度尼西亚和新加坡等 11 个国家的 28 个城市拥有价值超过 200 亿美元的管理资产 (AUM)。
挑战
星桥腾飞集团 (ASG) 是亚洲领先的可持续城市和商业空间解决方案提供商，其物业面临着停车容量方面的挑战。在新加坡等人口密集的城市，停车容量是一个主要问题。尽管高层建筑设有停车场或车库，但停车容量仍然是物业经理和司机面临的挑战。ASG 希望预测停车场容量，以优化停车服务，改善游客和司机的体验，并可能增加收入。他们之前曾使用过不同的平台来构建模型，但成本高昂，而且无法提供他们所需的准确预测。
解决方案
ASG 转向 DataRobot 的自动化机器学习平台，特别是其时间序列功能，以准确预测其停车场的容量。他们之前曾使用过不同的平台来构建模型，但成本高昂，而且无法提供所需的准确预测。在成功验证了 DataRobot 的概念后，他们发现该平台可以生成更准确的预测，而且更易于使用。最终目标是让司机通过应用程序获取停车场可用性信息。这将使司机能够找到周围哪些建筑物有按小时计费的停车位，为 ASG 提供新的收入来源，并为司机提供更好的停车体验。
运营影响
数量效益
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