I-Corps: Prediction Services for a Shared Economy

I-Corps：共享经济的预测服务

• 批准号: 1445718
• 负责人: Robert Hampshire
• 金额: $ 5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1445718&HistoricalAwards=false
• 关键词: Corps; Prediction; Services; Shared; Economy

The management of shared services such as parking and ride sharing can be challenging in urban areas. However, the efficient management of shared services, particularly parking, has been shown to dramatically reduce congestion and pollution. In this proposal, the team proposes exploring the commercial viability of a methodology to predict the demand for shared services found in cities. The product being proposed is a predictive parking application with both long and short term components. Not only will the product assist individuals in finding a parking space but also it will influence their travel demand patterns. Drivers will be able to proactively plan their trips and explore other options available to them based on the information obtained from the predictive model.The benefits of the predictive model go beyond the demand side. On the supply side, garage operators could use the predictive information to better manage their facilities. For example, a predicted higher than normal demand for parking spots could allow a garage operator to artificially increase the facility?s capacity by making provisions for valet parking. Further downstream, the information provided by the predictive module will be used to dynamically price parking spaces, a functionality that will help address some of the rigidities in the automobile parking market. The proposed innovation, if successful, will improve the management of shared services in urban areas. Additionally, the proposed innovation has the potential increases in small business activity and entrepreneurship in the areas of shared services such as carsharing, homesharing, and ridesharing

在城市地区，停车和拼车等共享服务的管理可能具有挑战性。然而，共享服务的有效管理，特别是停车，已被证明可以大大减少拥堵和污染。在这份提案中，该团队提议探索一种方法的商业可行性，以预测城市中对共享服务的需求。 正在提出的产品是一个预测停车应用程序，具有长期和短期组件。该产品不仅可以帮助人们找到停车位，还可以影响他们的出行需求模式。驾驶员将能够根据从预测模型中获得的信息主动规划行程，并探索其他可用的选项。预测模型的好处超出了需求方。在供应方面，车库运营商可以使用预测信息来更好地管理他们的设施。例如，对停车位的预测高于正常需求可能会允许车库运营商人为地增加设施？通过提供代客泊车服务来提高停车场的容量。在更下游的地方，预测模块提供的信息将用于动态定价停车位，这一功能将有助于解决汽车停车市场的一些刚性问题。拟议的创新如果成功，将改善城市地区共享服务的管理。此外，拟议的创新有可能增加小企业活动和共享服务领域的创业精神，如汽车共享，家庭共享和乘车共享