• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Processing & Management >Demand cycles and market segmentation in bicycle sharing
【24h】

Demand cycles and market segmentation in bicycle sharing

机译：自行车分享中需求周期和市场分割

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Consumers often display unique habitual behaviors, and knowledge of these behaviors is of great value in prediction of future demand. We investigated consumer behavior in bicycle sharing in Beijing, where demand prediction is critical for cost-effective rebalancing of bicycle locations (putting bikes where and when they will be rented) and supply (number of bicycles). We created baseline statistical demand models, borrowing methods from economics, signal processing and animal tracking to find consumption cycles of 7, 12, 24 h and 7-days. Lorenz curves of bicycle demand revealed significant stratification of consumer behavior and a long-tail of infrequent demand. To overcome the limits of traditional statistical models, we developed a deep-learning model to incorporate (1) weather and air quality, (2) time-series of demand, and (3) geographical location of demand. Customer segmentation was added at a later stage, to explore potential for improvement with customer demographics. Our final machine learning model with tuned hyper parameters yielded around 50% improvement in predictions over a discrete wavelet transform model, and 80-90% improvement in predictions over a naive model the reflects some current industry practice. We assessed causality in the deep-learning model, finding that location and air quality had the strongest causal impact on demand. The extreme market segmentation of customer demand, and our relatively short time span of data combined to make it difficult to find sufficient data on all customers for a model fit based on segmentation. We reduced our model data to only the 10 most frequent to see whether such segmentation improves our model's predictive success. These results, though limited, suggest that customer behavior within market segments is more stable than across all customers, as was expected.
机译：消费者经常展示独特的习惯行为，知识这些行为在预测未来需求方面具有很大的价值。我们调查了北京自行车分享中的消费者行为，需求预测对于自行车地点的成本效益重新平衡（将自行车租赁）和供应（自行车数量）来说至关重要。我们创建了基线统计需求模型，借用经济学，信号处理和动物跟踪的方法，以找到7,12,24小时和7天的消耗周期。自行车需求的Lorenz曲线揭示了消费者行为的显着分层和不常见的需求的长尾。为了克服传统统计模型的限制，我们开发了一种深入学习模型，包括（1）天气和空气质量，（2）时间系列的需求，（3）地理位置。在稍后阶段增加了客户细分，以探讨顾客人口统计数据改善的潜力。我们的最终机器学习模型具有调谐的超参数，在离散小波变换模型的预测中产生了大约50％的提高，并且在天真的模型上的预测提高了80-90％，反映了一些当前的行业实践。我们评估了深度学习模式的因果关系，发现位置和空气质量对需求的最强烈的因果影响。客户需求的极端市场分割，以及我们相对较短的数据的时间跨度组合，使得基于分割的模型适合的所有客户难以找到足够的数据。我们将模型数据减少到最常见的10次以查看此类分割是否提高了我们模型的预测成功。这些结果虽然有限，表明市场部分内的客户行为比预期的所有客户都更加稳定。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号