Reinforcement Learning (RL), trained via trial and error in simulators, has been proven to be an effective approach for addressing task assignment problems in spatial crowdsourcing. However, a performance gap still exists when transferring the simulator-trained RL Models (RLMs) to real-world settings due to the misalignment of travel time. Existing works mostly focus on using data-driven and learning-based methods to predict travel time; unfortunately, these approaches are limited in achieving accurate predictions by requiring a large amount of real-world data covering the entire state distribution. In this paper, we propose a Sim-to-Real Transfer with Human-guided Language Models framework called HLMTrans, which comprises three core modules: RLMs decision for task assignment, sim-to-real transfer with Large Language Models (LLMs), and preference learning from human feedback. HLMTrans first leverages the zero-shot chain-of-thought reasoning capability of LLMs to estimate travel time by capturing the real-world dynamics. This estimation is then input as domain knowledge into the forward model of Grounded Action Transformation (GAT) to enhance the action transformation of RLMs. Further, we design a human preference learning mechanism to fine-tune LLMs, improving their generation quality and enabling RLMs learn a more realistic policy. We evaluate the proposed HLMTrans on two real-world datasets, and the experimental results demonstrate that HLMTrans outperforms the SOTA methods in terms of effectiveness and efficiency.

In recent years, adaptive task assignment has been explored in spatial crowdsourcing. The challenge lies in how to adaptively partition the task stream to achieve the best utility for task assignment. A number of existing works have attempted to solve this challenge and achieve better performance by utilizing learning-based methods. Specifically, they mainly employ reinforcement learning to divide the task stream into a series of suitable batches and then perform task assignment in a batch fashion. Drawing inspiration from the effectiveness of human-machine collaborative decision-making, we aim to investigate human-in-the-loop methods to further enhance the performance of adaptive task assignment. In this paper, we propose a novel framework called Human-in-the-Loop Adaptive Partition (HLAP), which consists of two primary modules: Reinforcement Learning Partition Decision (RL-PD) and Human Supervision and Guidance (HSG). In the RL-PD module, we develop an RL agent, referred to as the decision-maker, by integrating the dual attention network into the Deep Q-Network (DQN) algorithm to capture cross-dimensional contextual information and long-range dependencies for a better understanding of the environment. In the HSG module, we design a human-in-the-loop mechanism to optimize the performance of the decision-maker, focusing on addressing two key issues: when and how humans interact with the decision-maker. Furthermore, to alleviate the heavy workload on humans, we construct a supervisor based on RL to oversee the decision-maker's partition process and adaptively determine when human intervention is necessary. We conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency and effectiveness of the HLAP framework.

The adoption of unmanned vehicles in urban logistics has gradually become a trend. It can effectively lower carbon emissions, reduce labor costs, and improve logistics efficiency. In this paper, we investigate a novel problem of unmanned vehicle scheduling (UVS) for large-scale urban logistics, where the logistics platform assigns unmanned vehicles to deliver parcels among stations under the constraints of time, capacity, and electricity to maximize the overall revenue of the logistics platform. Although the UVS problem is of practical usefulness, solving it requires non-trivial efforts, because we have proved that the UVS problem is NP-hard. To solve the UVS problem efficiently, we propose an efficient two-stage processing framework, including task assignment and vehicle reposition. Specifically, in the first stage, we propose an effective preference-aware matching (PAM) algorithm to deal with task assignments between unmanned vehicles and delivery tasks, which considers not only the electricity consumption of unmanned vehicles but also the supply-demand balance between delivery tasks and unmanned vehicles. In the second stage, we propose two vehicle repositioning algorithms based on deep reinforcement learning, termed restricted DQN repositioning algorithm (RDR) and restricted A2C repositioning algorithm (RAR), which can effectively refine the vehicle’s reposition stations based on vehicle supply and demand, electricity supply and demand, charging pile availability and collision avoidance restriction rules at current and neighbor stations, so that the vehicles can be efficiently relocated to stations with over-delivery tasks. Finally, extensive experiments have demonstrated that our proposed algorithms can achieve desirable efficiency and effectiveness.

With the rapid development of wireless networks and smart devices, spatial crowdsourcing (SC) has become increasingly prevalent. The key issue in SC is efficiently assigning spatial tasks, such as parcel and food delivery, to mobile workers in order to maximize platform utility. Existing works mainly focus on task assignment based on real-time spatio-temporal constraints of workers and tasks, neglecting the influence of future spatio-temporal distributions of tasks on current assignments. In this paper, we propose a novel problem in SC calledPrediction-aware Task Assignment (PTA), where the platform adaptively assigns spatial tasks to workers by considering their current and future spatio-temporal constraints to maximize overall platform revenue. To address this problem, we introduce a two-stage framework composed of task prediction and task assignment. In the task prediction stage, we develop a powerfulBilateral Spatial-Temporal Graph Convolutional Network (BSTGCNet)to predict the time and location where potential tasks may appear in the future. In the task assignment stage, we present aDeep Reinforcement Learning (DRL)approach to dynamically partition tasks into batches based on the current and future status of tasks, and conduct bipartite graph matching for spatial tasks and workers in a batch-wise manner. Finally, extensive experiments on real-world datasets validate the effectiveness and efficiency of our proposed solution.

Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain.

City express delivery services (a.k.a.~last-mile delivery) have become more prominent in recent years. Many logistics giants, such as Amazon, JD, and Cainiao, have deployed intelligent express delivery systems to deal with the growing demand for parcel delivery. Existing works adopt queuing or batch processing approaches to assign parcels to couriers. However, these approaches do not fully consider the distribution of parcels and couriers, leading to poor quality of task assignment. In this paper, we investigate a problem of delivery matching based on revenue maximization in real-time city express delivery services. Given a set of couriers and a stream of parcel collection tasks, our problem aims to assign each collection task to a suitable courier to maximize the overall revenue of the platform. The problem is shown to be NP-hard. To tackle the problem efficiently, we present a time-aware batch matching algorithm to offer high-quality courier-task matching in each sliding window. We further theoretically analyze the matching approximation bound. In addition, we propose an efficient deep reinforcement learning-based approach to adaptively determine the sliding window size for better matching results. Finally, extensive experiments demonstrate that our proposed algorithms can achieve desirable effectiveness and efficiency under a wide range of parameter settings.

As a convenient, economical and low carbon transport mode, ride hitching has gained tremendously prevalent in the globe. This paper presents PRID that offers an efficient ride hitching service with sufficient results analysis. In general, PRID is implemented by a publish/subscribe service, which provides a user-friendly web browser client that allows users (e.g., company managers and data analysts) to perform the performance analysis for ride hitching through simple interactive operations. Moreover, the server side of the PRID system consists of three modules including a top-k initialization module, an order dispatch module, and a top-k maintenance module, which can process the matching between drivers and riders efficiently. PRID system aims at continuously maintaining the update-to-date k best matched ride orders for each driver with less time cost, and monitoring their matching efficiency and effectiveness meanwhile. Finally, we demonstrate the PRID system on a real-world dataset, showing its good performance. The source code of the demonstration has been shared on https://github.com/wuqingshun/PRID.

The wide adoption of GPS-enabled smart devices has greatly promoted spatial crowdsourcing, where the core issue is how to assign tasks to workers efficiently and with high quality. In this paper, we build a novel visual analysis system for spatial crowdsourcing, namely AMRAS, which can not only intuitively present the task allocation for workers under different time window scales to users (e.g., data analysts and managers) in real-time, but also help users analyze task assignment decision model and its learning process. AMRAS has the following novel features. First, AMRAS provides two user-friendly interfaces that allow users to employ simple and easy-to-use console to perform statistical analysis. Secondly, AMRAS provides three powerful visualization tools, such as the visualization of assignment results, assignment process, and assignment decision model, which not only allow users to intuitively analyze the whole process of task assignment, but also help users discover the computational bottleneck of their task assignment solution. Finally, AMRAS enables online access to real-time data, providing users with instant assignment and instant analysis. We have implemented and deployed AMRAS on Alibaba Cloud and demonstrated its usability and efficiency in real-world datasets. The demonstration video of AMRAS has been uploaded to Google Drive.