让轨迹说出交通瓶颈
Let Trajectories Speak Out the Traffic Bottlenecks
摘要：
交通瓶颈是一组路段，由于道路容量和交通量之间的不平衡而导致交通量达到不可接受的水平。大量的轨迹数据捕捉道路网络中的实时交通状况，为识别交通瓶颈提供了有前途的新机会。在本文中，我们定义这个问题为随机驱动的交通瓶颈识别：给定一个道路网络R，一个轨迹数据库T，找到一个代表性的种子边缘的大小K的交通瓶颈，影响最高数量的路段不在种子集中。我们表明，这个问题是NP-难的，并提出了一个框架，找到交通瓶颈如下。首先，定义了一个交通扩散模型，该模型表示每个路段的交通量随时间的变化。然后，两个连接的路段之间的交通扩散概率和交通量的剩余比例为每个路段可以使用历史轨迹数据计算。然后，我们提出了两种不同的算法来解决这个问题。第一种算法是最佳优先算法BF，近似比为1-1/e。为了进一步加快大数据集的识别过程，我们还提出了一种基于采样的贪心算法SG。最后，使用三个不同的数据集进行综合实验，比较和对比各种解决方案，并深入了解各种方法之间的重要效率和有效性权衡。
英文摘要：
Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures real-time traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification: Given a road network R, a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF, with an approximation ratio of 1-1/e. To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG. Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
