The prevalence of region-based urban data has opened new possibilities for exploring correlations among regions to improve urban planning and smart-city solutions. Region embedding, which plays a critical role in this endeavor, faces significant challenges related to the varying nature of city data and the effectiveness of downstream applications. In this paper, we propose a novel framework, HREP (Heterogeneous Region Embedding with Prompt learning), which addresses both intra-region and inter-region correlations through two key modules: Heterogeneous Region Embedding (HRE) and prompt learning for different downstream tasks. The HRE module constructs a heterogeneous region graph based on three categories of data, capturing inter-region contexts such as human mobility and geographic neighbors, and intraregion contexts such as POI (Point-of-Interest) information. We use relation-aware graph embedding to learn region and relation embeddings of edge types, and introduce selfattention to capture global correlations among regions. Additionally, we develop an attention-based fusion module to integrate shared information among different types of correlations. To enhance the effectiveness of region embedding in downstream tasks, we incorporate prompt learning, specifically prefix-tuning, which guides the learning of downstream tasks and results in better prediction performance. Our experiment results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods.

In this paper, we study the Time-Dependent k Nearest Neighbor (TD-kNN) query on moving objects that aims to return k objects arriving at the query location with the least traveling cost departing at a given time t. Although the kNN query on moving objects has been widely studied in the scenario of the static road network, the TD-kNN query tends to be more complicated and challenging because under the time-dependent road network, the cost of each edge is measured by a cost function rather than a fixed distance value. To tackle such difficulty, we adopt the framework of GLAD and develop an advanced index structure to support efficient fastest travel cost query on time-dependent road network. In particular, we propose the Time-Dependent H2H (TD-H2H) index, which pre-computes the aggregated weight functions between each node to some specific nodes in the decomposition tree derived from the road network. Additionally, we establish a grid index on moving objects for candidate object retrieval and location update. To further accelerate the TD-kNN query, two pruning strategies are proposed in our solution. Apart from that, we extend our framework to tackle the time-dependent approachable kNN (TD-AkNN) query on moving objects targeting for the application of taxi-hailing service, where the moving object might have been occupied. Extensive experiments with different parameter settings on real-world road network show that our solutions for both TD-kNN and TD-AkNN queries are superior to the competitors in orders of magnitude.

The intelligent transportation systems, e.g., DiDi, Uber, have served as essential travel tools for customers, which foster plenty of studies for the location-based queries on the road network. In particular, given a set O of objects and a query point q on a road network, the k Nearest Neighbor (kNN) query returns the k nearest objects in O with the shortest road network distance to q. In literature, most existing solutions for kNN queries tend to reduce the query time, indexing storage, or throughput of the kNN queries while overlooking the correctness of the queries caused by query-query and update-query conflicts. In our work, we propose a grid-based framework on conflict-aware kNN queries on moving objects which aims to optimize system throughput while guaranteeing query correctness. In particular, we first propose efficient index structures and new query algorithms that significantly improve the throughput. We further present novel scheduling algorithms that aim to avoid conflicts and improve system throughput. Moreover, we devise approximate solutions that provide a controllable trade-off between the conflict of kNN queries and system throughput. Finally, we propose a cost-based dispatching strategy to assign the kNN results to the corresponding queries. Extensive experiments on real-world data demonstrate the effectiveness and efficiency of our proposed solutions over alternatives.

In the domain of route planning, the critical concern for travel efficiency has shifted towards optimizing travel time over distance due to the rise in congestion and the dynamic nature of modern road networks. Addressing this shift, we introduce Dynamic Route Optimization with Multi-Category Constraints (DROMC) for Point-of-Interest (POI) visits, which seeks to find the most time-efficient path that satisfies a user-defined list of requirements, considering both the spatial and temporal dimensions. This paper proposes a novel approach that leverages a path enumeration algorithm, which iteratively validates the k-fastest paths until all user queries are satisfied, ensuring adherence to time constraints and POI availability. To enhance the algorithm’s efficiency, we employ several key methodologies. First, we adapt the kSP algorithm to account for POI-focused path enumeration. We also introduce a Shared Prefix Tree (SPFT) with binary encoding, which significantly improves the storage and retrieval of path information. Moreover, we integrate a grid-based heuristic for quicker computation and implement strategic pruning methods to circumvent redundant calculations and manage POI business hours effectively. Our extensive experiments on real-world networks demonstrate the algorithm’s superiority in finding more efficient paths in shorter time frames compared to existing methods.

In this paper, we study the $k$ Maximum Trajectory Coverage Query, which aims to find $k$ routes in a public transport system that can serve the maximum number of users with given journey trajectories. In existing studies, they only consider independent service that includes no transfers, but overlooks the aggregative service that includes transfer of multiple routes, resulting in inferior results. In our study, we consider both independent and aggregative services, which can help provide more meaningful results. However, the problem is NP-hard and non-submodular. To address this problem, we propose a greedy algorithm that iteratively selects the route with the maximum marginal gain considering both independent and aggregative services, and show that it outperforms the competitor by up to 60% in terms of accuracy. Since the problem is non-submodular, the greedy algorithm typically does not provide any approximation guarantee. By a mild assumption, we show that our proposed solution provides a constant approximation with respect to the optimal one. Moreover, since we need to consider both the independent and aggregative service, our greedy algorithm becomes more complicated and brings additional time overheads. To overcome such a deficiency, we further accelerate our solution using several heuristics and present an efficient method for spatially associating trajectories and routes. Extensive experiments on real-world datasets demonstrate that our optimisation brings up to an order of magnitude speedup and even outperforms existing solutions (that consider no aggregative service) by 2-3 times.