Trip Planning Queries in Road Networks

机动性是人类的基本特征。作为一个物种，我们总是为了太多的原因而迁徙。随着当今移动通信设备的丰富，有关移动性的数据可以相对容易地收集，从而提供大量的时空数据。智能地使用这些数据可以使人们在几个方面更有效率地出行，例如，通过更有效和高效地拼车来消耗更少的燃料，减少交通时间等等，从而走向所谓的智能城市。大量的流量监控应用、社交网络等可以利用这些数据源，并为其聚集更多的价值。此外，通过考虑非时空数据，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，拟议的研究计划将集中在公路网中的出行计划查询这一主题。旅行计划查询考虑道路网络、起点和终点以及旅行者(或其组)想要访问的一组兴趣点。旅行计划查询可以有几种变体：兴趣点可以属于不同类别的兴趣点(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、兴趣点之间或从预定路径到潜在兴趣点的最大距离等。

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.