Route planning satisfied multiple requests is an emerging branch in the route planning field and has attracted significant attention from the research community in recent years. The prevailing studies focus only on seeking a route by minimizing a single kind of Travel Cost, such as trip time or distance, among others. In reality, most users would like to choose an appropriate route, neither fastest nor shortest route. Usually, a user may have multiple requirements, and an appropriate route would satisfy all requirements requested by the user. In fact, planning an appropriate route could be formulated as a problem of Multi-weight Multi-destination Route Planning with Deadlines Constraints (MWMDRP-DC). In this article, we propose a framework, namely, MWMD-Router, which addresses the MWMDRP-DC problem comprehensively. To consider the travel costs with time-variation, we propose not only four novel dynamic graph miner to extract travel costs that reveal users’ requirements but also two new algorithms, namely, Basic MWMD Route Planning and Advanced MWMD Route Planning , to plan a route that satisfies deadline requirements and optimizes another criterion like travel cost with time-variation efficiently. To the best of our knowledge, this is the first work on route planning that considers handling multiple deadlines for multi-destination planning as well as optimizing multiple travel costs with time-variation simultaneously. Experimental results demonstrate that our proposed algorithms deliver excellent performance with respect to efficiency and effectiveness.

中文翻译：

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具有期限约束的多权重多目的地路线规划的动态图挖掘

满足多个请求的路线规划是路线规划领域的一个新兴分支，近年来引起了研究界的极大关注。流行的研究仅关注通过最小化单一类型的旅行成本（例如旅行时间或距离等）来寻找路线。实际上，大多数用户都希望选择一条合适的路线，既不是最快也不是最短路线。通常，一个用户可能有多个需求，一条合适的路由可以满足用户提出的所有需求。事实上，规划一条合适的路线可以被表述为一个问题具有期限约束的多权重多目的地路线规划(MWMDRP-DC)。在本文中，我们提出了一个框架，即 MWMD-Router，它全面解决了 MWMDRP-DC 问题。为了考虑随时间变化的旅行成本，我们不仅提出了四种新颖的动态图挖掘器来提取揭示用户需求的旅行成本，而且还提出了两种新算法，即，基本 MWMD 路线规划和先进的 MWMD 路线规划，规划一条满足最后期限要求的路线，并有效地优化另一个标准，例如随时间变化的旅行成本。据我们所知，这是第一个考虑处理多目的地规划的多个截止日期以及同时优化随时间变化的多个旅行成本的路线规划工作。实验结果表明，我们提出的算法在效率和有效性方面表现出色。