Distance-Aware Competitive Spatiotemporal Searching Using Spatiotemporal Resource Matrix Factorization (GIS Cup)
Distance-Aware Competitive Spatiotemporal Searching Using Spatiotemporal Resource Matrix Factorization (GIS Cup)
复制标题
使用时空资源矩阵分解的距离感知竞争性时空搜索（GIS Cup）
DOI：
10.1145/3347146.3363350
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Züfle, Andreas
中科院分区：
文献类型：
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作者：
Kim, Joon-Seok;Pfoser, Dieter;Züfle, Andreas
拥挤的交通浪费了数十亿升的燃料，是绿色家用气体（GHG）排放的重要贡献者。虽然方便，但Uber和Lyft等拼车服务正在成为这些排放的重要贡献者，这不仅是因为增加了交通流量，还因为在路上等待乘客的时间。为了帮助改善乘车共享的影响，我们提出了一种算法来优化司机搜索客户的效率。在我们的模型中，主要目标是引导表示为空闲代理的驱动程序，即，当前未分配客户或资源的位置，我们预测会出现新资源。我们的方法使用非负矩阵分解（NMF）建模和预测资源的时空分布。为了选择空闲代理的目的地，我们采用了一种贪婪的启发式算法，该算法在距离贪婪之间取得了平衡，即，为了避免没有资源和资源贪婪的长途旅行，即，移动到一个位置，资源预计将出现以下NMF模型。为了确保代理不过度供应的资源预测和供应其他地区的地区，我们随机使用预测的资源分布在代理的本地邻域内的代理的目的地。我们的实验评估表明，我们的方法减少了搜索时间的代理和等待时间的资源使用真实世界的数据从曼哈顿，纽约，美国。
Congested traffic wastes billions of liters of fuel and is a significant contributor to Green House Gas (GHG) emissions. Although convenient, ride sharing services such as Uber and Lyft are becoming a significant contributor to these emissions not only because of added traffic but by spending time on the road while waiting for passengers. To help improve the impact of ride sharing, we propose an algorithm to optimize the efficiency of drivers searching for customers. In our model, the main goal is to direct drivers represented as idle agents, i.e., not currently assigned a customer or resource, to locations where we predict new resources to appear. Our approach uses non-negative matrix factorization (NMF) to model and predict the spatio-temporal distributions of resources. To choose destinations for idle agents, we employ a greedy heuristic that strikes a balance between distance greed, i.e., to avoid long trips without resources and resource greed, i.e., to move to a location where resources are expected to appear following the NMF model. To ensure that agents do not oversupply areas for which resources are predicted and under supply other areas, we randomize the destinations of agents using the predicted resource distribution within the local neighborhood of an agent. Our experimental evaluation shows that our approach reduces the search time of agents and the wait time of resources using real-world data from Manhattan, New York, USA.
管理规划和控制的管理：纽约市出租车和豪华轿车委员会
DOI：
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发表时间：
1979
期刊：
影响因子：
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作者：
Louis C Calcagno
通讯作者：
Louis C Calcagno