ACM Transactions on Intelligent Systems and Technology
About: ACM Transactions on Intelligent Systems and Technology is an academic journal. The journal publishes majorly in the area(s): Recommender system & Deep learning. It has an ISSN identifier of 2157-6904. Over the lifetime, 765 publication(s) have been published receiving 66330 citation(s).
Topics: Recommender system, Deep learning, Cluster analysis, Graph (abstract data type), Crowdsourcing
Abstract: 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.
Abstract: The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as durat...
Abstract: Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next locati...
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