Introduction to the Special Issue on Intelligent Trajectory Analytics: Part II

Abstract

We are delighted to present this special issue on Intelligent Trajectory Analytics. Over the past decades, a broad range of techniques have been proposed for processing, managing, and mining trajectory data. It has enabled and helped government agencies and businesses to better understand the mobility behavior of their citizens and customers, which is crucial for a variety of applications such as smart city and transportation, public health and safety, environmental management, and location-based services. The purpose of this special issue is to provide a forum for researchers and practitioners in academia and industry to present their latest research findings and engineering experiences in developing cutting-edge techniques for intelligent trajectory data analytics. This special issue consists of two parts. In Part II, the guest editors selected 10 contributions that cover varying topics within this theme, such as trajectory quality management, trajectory search and mining, trajectory privacy protection, and novel trajectory-based applications. Zhao et al. in “Efficient and Effective Similar Subtrajectory Search: A Spatial-aware Comprehension Approach” address the similar subtrajectory search problem with the Graph Neural Network framework, which contains four modules including a spatial-aware grid embedding module, a trajectory embedding module, a query-context trajectory fusion module, and a span prediction module. Sharma et al. in “Analyzing Trajectory Gaps to Find Possible Rendezvous Region” propose a refined algorithm to find a potential rendezvous region and an optimal temporal range to improve computational efficiency. Theoretical evaluation of the algorithm’s correctness and completeness along with a time complexity analysis is also provided. Zheng et al. in “Supply-demand-aware Deep Reinforcement Learning for Dynamic Fleet Management” use a deep Q-network with action sampling policy, called AS-DQN, to learn an optimal dispatching policy and further utilize a dueling network architecture to improve the performance of AS-DQN. Wang et al. in “Multivariate Correlation-aware Spatio-temporal Graph Convolutional Networks for Multi-scale Traffic Prediction” study the problem of multivariate correlation-aware multi-scale traffic flow prediction and propose a feature correlation-aware spatio-temporal graph convolutional network to effectively address it. Wang et al. in “Integrate Algorithmic Sampling-based Motion Planning with Learning in Autonomous Driving” integrate algorithmic motion planning with learning models to improve the performance of sampling-basedmotion planning (SBMP) for autonomous driving in highway traffic scenarios.