An exact single-agent task selection algorithm for the crowdsourced logistics

Agency for Science, Technology and Research (A*STAR); Fujitsu; National Research Foundation (NRF) Singapor

e-Uber\textit{e-Uber}: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing

The sharing-economy-based business model has recently seen success in the transportation and accommodation sectors with companies like Uber and Airbnb. There is growing interest in applying this model to energy systems, with modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). In this work, we exploit the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits spatial crowdsourcing, reinforcement learning, and reverse auction theory. Specifically, the platform uses reinforcement learning to understand the drivers' preferences towards different ride-sharing and energy-sharing tasks. Based on these preferences, a personalized list is recommended to each driver through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid on their preferred tasks in their list in a reverse auction fashion. Then e-Uber solves the task assignment optimization problem that minimizes cost and guarantees V2G energy requirement. We prove that this problem is NP-hard and introduce a bipartite matching-inspired heuristic, Bipartite Matching-based Winner selection (BMW), that has polynomial time complexity. Results from experiments using real data from NYC taxi trips and energy consumption show that e-Uber performs close to the optimum and finds better solutions compared to a state-of-the-art approachComment: Preprint, under revie

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Optimal Order Assignment with Minimum Wage Consideration (OOAMWC)

While the application of crowdsourcing has increased over the years, the technology experiences various issues during implementation. Examples of some of the issues that affect crowdsourcing include task assignment, profit maximizations, as well as time window issues. In some instances addressing some of the issues results in the other issues being overlooked. An example is when assigning tasks to workers, the profits of the workers might not be considered and this ends up affecting the profit maximization aspect. Various algorithms have been proposed to address the task assignment, profit maximizations, and time window issues. However, these algorithms address the issues individually and this results in the occurrence of the other noted issues. Therefore, this calls for the definition of a solution to address the task assignment issue while taking into consideration the time window issue and the minimum wage constraint. Additionally, the solution should address the profit maximization of not only the workers but also the platform and the clients of the platform. To evaluate the efficiency of the proposed solution, a comparison with the different implemented solutions to address individual issues is recommended. Comparing such solutions can provide insight into the efficiency of the proposed approach when addressing multiple issues affecting crowdsourcing