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

该项目解决了与管理大型车队部署相关的物流问题，这些车队包括：Uber、Lyft和Ola等出租车共享服务；Hub Grub等送餐服务；以及谷歌Shopping和亚马逊等零售配送服务，涉及管理和优化数十万辆汽车的运营。快递网络曾经由少数大公司和许多小公司主导。然而，新的模式正在发展。例如，在纽约市的街道上有14k辆出租车，优步现在是纽约市最大的出租车服务。这些公司必须进行近乎实时的操作，并依赖于对道路网络上车辆的历史分析，以保持其高效运行。特别是，公司在进行供需之间的实时匹配时，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数十万个网络距离。这个项目的技术不同于谷歌Maps、Here Maps和其他在线地图服务，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。该项目的技术只专注于在数据库中以非常高的吞吐量计算网络距离，这使得这项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。这种方法将对食品配送公司、包裹递送公司（UPS、FedEx、USPS）、出租车/豪华轿车服务以及拼车服务（Uber、Lyft、Ola Cabs）具有吸引力。提供地图服务的公司，如b谷歌、微软或ESRI，可能也会发现这项技术很有吸引力，因为使用它，他们可以通过提供网络距离来改进他们的本地搜索服务，而不是像现在这样依靠“直线飞行”的距离。该团队当前的业务模式是为每个客户定制系统，要求在每台机器或每个站点的基础上购买许可证。这个团队的计划是专注于几个垂直市场（例如，具有特定的、引人注目的需求的利基市场），并了解他们的需求。该团队将开发应用程序来满足每个垂直领域的需求。

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.