Project: #182 Actuation System For City-Wide Sensing And Ride Distribution Using Managed Vehicular Fleets Progress Report - Reporting Period Ending: March 30, 2019 Principal Investigator: Pei Zhang Status: Active Start Date: July 1, 2018 End Date: June 30, 2019 Research Type: Applied Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2018 Mobility21 UTC Progress Report (Last Updated: March 31, 2019, 1:22 p.m.) % Project Completed to Date: 40 % Grant Award Expended: 40 % Match Expended & Document: 40 USDOT Requirements Accomplishments • What are the major goals and objectives of the project? The major goals of the project is to answers the question: how can we achieve optimal city-wide sensing coverage quality through collaboration while also matching ride requests with taxis? • What was accomplished under these goals? In the first part of the project, we focused on two goals: 1. Develop initial brute force algorithm for incorporate the mobility model and incentive model base incorporation. Develop the theoretical problem framework. 2. Implement algorithm Simulate results from existing NYC and Beijing taxi traces. • What opportunities for training and professional development has the project provided? This funding is providing for one Ph.D. student. The post-doc and another student is on the matching funding. • How have the results been disseminated? If so, in what way/s? We submitted a journal paper and is preparing for a sensys publication. One SPIE paper was submitted and was presented at SPIE in Denver CO. • What do you plan to do during the next reporting period to accomplish the goals and objectives? Next reporting period we plan to 1) Develop near-optimal non-exhaustive results to improve scalability and trace and actuation generation speed. 2) Deploy new algorithm on physical taxi hardware in Shenzhen. Utilizing air pollution as the motivating application Impacts • The effectiveness of the transportation system Our results have shown the possibility of using the transportation system as an effective city status montoriting platform that benefit citizens of all economic levels. At the same time, it will also increase the effectiveness of the transportation system to reduce bias of the pickups. • Technology transfer (include transfer results to entities in government or industry, adoption of new practices, or instances where research outcomes have led to the initiation of a start-up company) None • The increase in the body of scientific knowledge We made the following main contribution in scientific knowledge - A novel modeling of the incentivizing problem: To our best knowledge, we are the first to model the quality of sensing coverage as the KL-divergence between the target and sensed data distributions and formulate the sensing coverage optimization problem. We further prove that this formulation is a non-linear multiple-choice knapsack problem, which is NP-complete and impossible to solve in polynomial time. - A novel hybrid incentive design to reduce the incentivizing cost: We design a hybrid incentive for the vehicle agents, which combines the non-monetary incentive of potential task requests at the vehicle agent destination (we call this a ``hidden incentive'') and the monetary incentive. This combination of incentives allows us to better utilize the budget by decreasing the average cost of incentivizing one agent. - A novel and efficient algorithm to compute optimal the incentivizing strategy: We introduce an algorithm, which incentivizes vehicles to optimize the sensing distribution in a crowd sensing system. The algorithm finds the solution to reduce the dissimilarity in a more efficient way than exhaustive search by a two-stage optimization method. Other none Outcomes New Partners none Issues Some delays occurred for the later part of the project due to the Post-doc ability to work outside of the US for a real deployment within the Chinese testbed. We are leveraging relationship in china to fix the issue and allow deployment remotely with the help of our partners at Tsinghua University.