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I-Corps: RoadsInDB: Customer Discovery in the Logistics, Delivery, Ride Sharing, Location-based Services and Analytics Verticals

I-Corps：RoadsInDB：物流、交付、乘车共享、基于位置的服务和分析垂直领域的客户发现

基本信息

批准号：
1634753
负责人：
Hanan Samet
金额：
$ 5万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-03-15 至 2017-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634753&HistoricalAwards=false
关键词：
Corps RoadsInDB Customer Discovery Logistics

项目摘要

This project addresses logistics issues related to managing the deployment of large fleets of vehicles ranging from: Cab sharing services like Uber, Lyft, and Ola; food delivery services like Hub Grub; and retail delivery services like Google Shopping and Amazon, involving managing and optimizing the operation of hundreds and thousands of vehicles. Delivery networks were once dominated by a few large players and many small players. However, new models are evolving. For example, with 14k taxis on the streets of New York City (NYC), Uber is now is the single largest taxi service in NYC. Such companies must perform near real-time and rely on historical analytics on vehicles on road networks to keep their operations running efficiently. In particular, companies constantly compute network distances on road networks as they perform real time matchmaking between supply and demand. The technology involved in this project enables computing hundreds of thousands of network distances on road network at a very high throughput rate. This project's technology is different from Google Maps, Here Maps, and other online map services in the sense that they provide APIs to compute the shortest path between a source and destination vertex. This project's technology only focuses on computing network distances at a very high throughput inside a database, which makes this technology essential for companies that perform extensive analytical queries on road networks. This approach will be attractive to food delivery companies, package delivery companies (UPS, FedEx, USPS), taxi/limousine services, and ride sharing services (Uber, Lyft, Ola Cabs). Companies that provide map services such as Google, Microsoft, or ESRI may also find this technology attractive since using it they can improve their local search service by providing network distances instead of resorting to "as the crow flies" distances which is currently the case. The team's current business model would be to customize the system for each customer, requiring the purchase of licenses on a per-machine or per-site basis. This team's plan is to concentrate on several verticals (i.e., niche markets with specific, compelling needs) and understand their requirements. The team will develop applications to cater to each of the verticals.

该项目解决了与管理大型车队部署相关的物流问题，包括：Uber，Lyft和奥拉等出租车共享服务; Hub Grub等食品配送服务;以及Google Shopping和Amazon等零售配送服务，涉及管理和优化数百辆和数千辆汽车的运营。交付网络曾经由少数大型参与者和许多小型参与者主导。然而，新的模式正在发展。例如，在纽约市（NYC）的街道上有14 k辆出租车，Uber现在是纽约市最大的出租车服务。这些公司必须近乎实时地执行，并依赖于对道路网络上车辆的历史分析，以保持其运营效率。特别是，公司在执行供需之间的真实的时间匹配时，不断计算道路网络上的网络距离。该项目所涉及的技术能够以非常高的吞吐率计算道路网络上的数十万个网络距离。该项目的技术与Google Maps、Here Maps和其他在线地图服务不同，因为它们提供API来计算源顶点和目标顶点之间的最短路径。该项目的技术仅专注于在数据库内以非常高的吞吐量计算网络距离，这使得该技术对于在道路网络上执行广泛分析查询的公司至关重要。这种方法对食品配送公司、包裹配送公司（UPS、FedEx、USPS）、出租车/豪华轿车服务和拼车服务（Uber、Lyft、奥拉出租车）具有吸引力。提供地图服务的公司，如谷歌、微软或ESRI，也可能会发现这项技术的吸引力，因为使用它，他们可以通过提供网络距离来改善他们的本地搜索服务，而不是像目前这样依靠“乌鸦飞”的距离。该团队目前的业务模式是为每个客户定制系统，需要在每台机器或每个站点的基础上购买许可证。该团队的计划是专注于几个垂直领域（即，具有特定迫切需求的利基市场），并了解他们的要求。该团队将开发应用程序，以满足每个垂直领域的需求。