Route Recommendations for Intelligent Transportation Services
Route Recommendations for Intelligent Transportation Services
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DOI：
10.1109/tkde.2019.2937864
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发表时间：
2021-03
影响因子：
8.9
通讯作者：
Yong Ge;Huayu Li;Alexander Tuzhilin
中科院分区：
文献类型：
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作者：
Yong Ge;Huayu Li;Alexander Tuzhilin
积累的大量移动数据以及跟踪移动的人或物体的能力使我们能够开发先进的移动推荐，这对于向移动中的个人用户推荐一系列位置至关重要。在本文中，我们研究了移动推荐的特定案例，即利用车辆 GPS 数据向驾驶员推荐路线。具体来说，我们制定了一个新的宽松假设路线推荐（RR-RA）问题，其目标是根据司机当前的位置向他推荐一系列位置，以最大限度地提高他的商业成功。为了使我们的推荐实用且可用于实际实践，我们需要在请求出现后及时生成推荐结果。因此，我们提出了一种有效的算法来有效地生成推荐。此外，我们识别并解决了面向目的地的路线推荐（DORR）问题。在不解决 DORR 问题的情况下，单独使用 RR-RA 在实践中效果不佳，因为驾驶员每天都可能会遇到目的地限制。我们开发了一种专用且高效的算法来解决 DORR 问题。 RR-RA 和 DORR 问题的一揽子解决方案为向驾驶员提供路线建议提供了全面的方法。我们使用真实世界的 GPS 数据和合成数据来评估我们的方法，并使用不同的评估指标证明所提出方法的有效性和效率。
The accumulated large amount of mobility data and the ability to track moving people or objects have enabled us to develop advanced mobile recommendations, which are essential to recommend a sequence of locations to an individual user on the move. In this paper, we study a particular case of mobile recommendations, route recommendations to drivers, by utilizing vehicle GPS data. Specifically, we formulate a new Route Recommendation with Relaxed Assumptions (RR-RA) problem, the goal of which is to recommend a sequence of locations to a driver based on his current location in order to maximize his business success. To make our recommendation practical and scalable for real practice, we need to produce recommendation results in a timely fashion once a request emerges. Therefore, we propose an efficient algorithm to efficiently generate recommendations. Furthermore, we identify and address a destination-oriented route recommendation (DORR) problem. Without solving DORR problem, RR-RA alone does not work well in practice because drivers may encounter the destination constraint on a daily basis. We develop a dedicated and efficient algorithm for solving DORR problem. The package of solutions for both RR-RA and DORR problems provide a comprehensive approach for route recommendations to drivers. We evaluate our methods using both real-world GPS data and synthetic data, and demonstrate the effectiveness and efficiency of proposed methods with different evaluation metrics.