Let Trajectories Speak Out the Traffic Bottlenecks

نویسندگان

چکیده

Traffic bottlenecks are a set of road segments that have an unacceptable level traffic caused by poor balance between capacity and volume. A huge volume trajectory data which captures realtime conditions in networks provides promising new opportunities to identify the bottlenecks. In this paper, we define problem as trajectory-driven bottleneck identification : Given network R , database T find representative seed edges size K influence highest number not set. We show is NP-hard propose framework follows. First, spread model defined represents changes for each segment over time. Then, diffusion probability two connected residual ratio can be computed using historical data. then different algorithmic approaches solve problem. The first one best-first algorithm BF with approximation 1-1/ e . To further accelerate process larger datasets, also sampling-based greedy SG Finally, comprehensive experiments three datasets compare contrast various solutions, provide insights into important efficiency effectiveness trade-offs among respective methods.

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ژورنال

عنوان ژورنال: ACM Transactions on Intelligent Systems and Technology

سال: 2021

ISSN: ['2157-6904', '2157-6912']

DOI: https://doi.org/10.1145/3465058