Anomaly detection is one of the most significant tasks in multivariate time series analysis, while it remains challenging to model complex patterns for improving detection accuracy and to interpret the root causes of anomalies. However, existing studies either consider only the temporal dependencies, or simply reconstruct the original input for detection, both neglecting the hidden relationships among multivariate. We propose an adversarial graph neural network based anomaly detection model, called SGAT-AE, which consists of aSelf-learningGraphATtention network (SGAT), anAuto-Encoder (AE), and an adversarial training component. Specifically, SGAT is a prediction model that discovers the graph dependency relationships among multivariate and acts as a sample generator to confuse AE, while AE reconstructs the samples and acts as a discriminator that distinguishes a real sample from a generated one. A novel adversarial training between SGAT and AE is applied to amplify the errors of anomalies such that the prediction performance of SGAT is improved and the overfitting of AE is avoided. In addition, we aggregate the prediction error, the reconstruction error, and the adversarial error for anomaly detection, and develop a graph based anomaly interpretation method that locates the root causes from both local and global perspectives. Extensive experiments with five real-world data offer evidence that the proposed solution SGAT-AE is capable of achieving better performance when compared with the state-of-the-art proposals.

We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming passenger requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) model that predicts the requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (DROP) algorithm that considers both the spatial-temporal predictions and the supply-demand state. We report on extensive experiments with real-world data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.

Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are idle and that passengers spend on waiting. As a key component of these platforms, the fleet management problem can be naturally modeled as a Markov Decision Process, which enables us to use the deep reinforcement learning. However, existing studies are proposed based on simplified problem settings that fail to model the complicated supply-dynamics and restrict the performance in the real traffic environment. In this article, we propose a supply-demand-aware deep reinforcement learning algorithm for taxi dispatching, where we use a deep Q-network with action sampling policy, called AS-DQN, to learn an optimal dispatching policy. Furthermore, we utilize a dueling network architecture, called AS-DDQN, to improve the performance of AS-DQN. Extensive experiments on real-world datasets offer insight into the performance of our model and show that it is capable of outperforming the baseline approaches.

Online car-hailing services have gained substantial popularity. An effective taxi fleet management strategy should not only increase taxi utilization by reducing taxi idle time, but should also improve passenger satisfaction by minimizing passenger waiting time. We demonstrate a fleet management system called SOUP that aims at minimizing taxi idle time and that monitors the fleet movement status. SOUP includes a passenger request prediction model called ST-GCSL that predicts the number of requests in the near future, and it includes a demand-aware route planning algorithm called DROP that provides idle taxis with search routes to serve potential requests. In addition, SOUP supports visualizing and analyzing historical passenger requests, simulating fleet movement, and computing evaluation metrics. We demonstrate how SOUP accurately predicts passenger demand and significantly reduces taxi idle time.

The ACM SIGSPATIAL GIS Cup 2020 focuses on the Collaborative Spatio-Temporal Searching (CSTS) problem, in which a fleet of mobile agents search for stationary resources on a road network. While each resource can be obtained by exactly one agent, agents can collaborate to obtain resources as quickly as possible. The key of solving CSTS is to guide agents to "hotspot" areas and to avoid the competition by considering agent collaboration. We propose a fleet management method by formulating CSTS as a minimum cost flow problem, called MCF-FM. In addition, we develop a continuous order dispatch strategy. Our submission is the top performer in the agent utilization scenario and runner-up in the customer experience scenario. Our source code is available at: https://github.com/Chriszblong/MCF-FM.

The competitive spatial-temporal searching (CSTS) problem finds many applications in the daily life, such as ridesharing, car parking, and EV charging, where the mobile agents search for stationary resources on a road network. A key issue of CSTS is how agents should choose their search path such that the search time is minimized. To solve CSTS, we are faced with two main challenges: (1) how to find an optimal space partitioning granularity such that the resource availability pattern of each region is well modeled; (2) how to design a route search algorithm to avoid the "herding" effect as the agents tend to adopt a common search strategy. In this paper, we propose a Spatial-Temporal Partitioning (STP) approach to cope with these two issues. First, we partition the search space into regions by considering both spatial and temporal information of the historical resource records, and compute a weight for each region. Second, we assign a shortest-travel-time path to each agent from its current location to a relatively popular region according to the current time. Extensive experiments are conducted on a real dataset, which show that STP outperforms the baseline algorithm about 24 to 29 seconds in terms of average search time, and about 38% to 54% in terms of average wait time. The source code is available at: https://github.com/Chriszblong/STP.