Trip Planning Queries in Road Networks

道路网络中的行程计划查询

• 批准号: RGPIN-2017-05061
• 负责人: Nascimento, Mario
• 金额: $ 2.48万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634791
• 关键词: Trip; Planning; Queries; Road; Networks

Mobility is a basic human characteristic. As a species we have always been moving about for a plethora of reasons. With today’s abundance of mobile communication devices, data about mobility can be collected relatively easily, thus providing large amounts of spatio-temporal data. Using such data intelligently can enable people to move more efficiently in several aspects, e.g., consuming less fuel by sharing rides more effectively and efficiently, spending less time in traffic, etc., thus moving towards so-called smart cities. A large number of traffic monitoring applications, social networks, etc., can leverage such data sources and aggregate further value to it. Moreover, this data can become rather complex and richer by considering non-spatio-temporal data, e.g., public events, weather patterns and path-popularity. Given this overarching theme, the proposed research program will focus on the topic of trip planning queries in road networks. A trip planning query considers a road network, a starting and a destination point, and a set of points-of-interest a traveller (or group thereof) wants to visit. There can be several variations of trip planning queries: points-of-interest may belong to different categories-of-interest (which may be needed to be traversed in an arbitrary, full or partial order) or one may have to account for opening hours of points-of-interest along with traffic conditions or public transportation schedules, maximum distance between point-of-interest or from pre-determined paths to potential points-of-interest, etc.

能动性是人类的基本特征。作为一个物种，我们总是因为各种各样的原因而四处迁徙。在移动通信设备丰富的今天，移动数据的收集相对容易，从而提供了大量的时空数据。智能地使用这些数据可以使人们在几个方面更有效地移动，例如通过更有效和高效地拼车来减少燃料消耗，减少交通时间等，从而走向所谓的智慧城市。大量的流量监控应用、社交网络等，都可以利用这些数据源，进一步聚合其价值。此外，考虑到非时空数据，例如公共事件、天气模式和路径受欢迎程度，这些数据可能变得相当复杂和丰富。鉴于这一总体主题，提出的研究计划将集中在道路网络中的旅行规划查询主题上。旅行计划查询考虑一个道路网络、一个起点和一个终点，以及旅行者（或其团队）想要访问的一组兴趣点。旅行计划查询可能有几种变体：兴趣点可能属于不同的兴趣类别（可能需要以任意、全部或部分顺序遍历），或者可能必须考虑兴趣点的开放时间以及交通状况或公共交通时间表、兴趣点之间的最大距离或从预定路径到潜在兴趣点的最大距离，等等。