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首页> 外文会议>International conference on very large data bases >Path Cost Distribution Estimation Using Trajectory Data
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Path Cost Distribution Estimation Using Trajectory Data

机译：使用轨迹数据的路径成本分布估计

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With the growing volumes of vehicle trajectory data, it becomes increasingly possible to capture time-varying and uncertain travel costs in a road network, including travel time and fuel consumption. The current paradigm represents a road network as a weighted graph; it blasts trajectories into small fragments that fit the underlying edges to assign weights to edges; and it then applies a routing algorithm to the resulting graph. We propose a new paradigm, the hybrid graph, that targets more accurate and more efficient path cost distribution estimation. The new paradigm avoids blasting trajectories into small fragments and instead assigns weights to paths rather than simply to the edges. We show how to compute path weights using trajectory data while taking into account the travel cost dependencies among the edges in the paths. Given a departure time and a query path, we show how to select an optimal set of weights with associated paths that cover the query path and such that the weights enable the most accurate joint cost distribution estimation for the query path. The cost distribution of the query path is then computed accurately using the joint distribution. Finally, we show how the resulting method for computing cost distributions of paths can be integrated into existing routing algorithms. Empirical studies with substantial trajectory data from two different cities offer insight into the design properties of the proposed method and confirm that the method is effective in real-world settings.
机译：随着车辆轨迹数据量的增长，越来越有可能捕获道路网络中随时间变化和不确定的行驶成本，包括行驶时间和燃料消耗。当前范例将道路网络表示为加权图;它将轨迹炸裂成小碎片，使其适合下面的边缘以为边缘分配权重;然后将路由算法应用于结果图。我们提出了一种新的范式，即混合图，其目标是更准确，更有效的路径成本分布估算。新的范式避免了将轨迹炸裂成小碎片，而是将权重分配给路径，而不是简单地分配给边缘。我们展示了如何使用轨迹数据计算路径权重，同时考虑到路径边缘之间的旅行成本依赖性。给定出发时间和查询路径，我们展示了如何选择最佳的权重集以及相关联的路径来覆盖查询路径，从而使权重能够为查询路径提供最准确的联合成本分布估算。然后，使用联合分布精确计算查询路径的成本分布。最后，我们展示了如何将计算路径成本分布的结果方法集成到现有的路由算法中。来自两个不同城市的大量轨迹数据的实证研究提供了对所提出方法的设计特性的洞察力，并确认了该方法在实际环境中是有效的。

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