About the Event
One of the most prevalent trends in personal communication and computation in the past few years has been the tremendous growth of the smart device adoption. For example, in most developed countries, the smartphone penetrations have already crossed 40% in 2013, with the top 4 countries over 70%. With such high market penetrations and increasingly sophisticated sensors installed in these smart devices, it's becoming possible to provide highly personal and context-aware services. In this talk, two innovative urban solutions are introduced to allow urban dwellers to request for mobility service and to sell mobility as a service.
In the former case, we focus on the last-mile transportation service, which connects travelers from public transport hubs to their final destinations. In particular, we investigate the use of ride-sharing services on a non-dedicated commercial fleet (such as taxis or passenger vans). Our approach has the benefits of being dynamic, flexible, and with low setup cost. A critical issue in such ride-sharing service is how riders should be grouped and serviced, and how fares should be split. To facilitate our designed approach, we propose two auction designs which are used to solicit individual rider's willing payment rate and compensation rate (for extra travel, if any). We demonstrate that these two auctions are budget balanced, individually rational, and incentive compatible.
In the latter case, we investigate the problem of large-scale mobile crowd-tasking, where a large pool of citizen crowd-workers are compensated to voluntarily perform a variety of location-specific urban logistics tasks. We propose TRACCS, a coordinated task assignment approach (as opposed to the decentralized approach that is universally adopted at the moment), where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time horizon, as opposed to just instantaneous location. We formulate such task assignment as an optimization problem, that seeks to maximize the total payoff from all assigned tasks, subject to a maximum bound on the detour (from the expected path) that a worker will experience to complete her assigned tasks. We develop credible computationally-efficient heuristics to address this optimization problem, and show, via simulations with realistic topologies and commuting patterns, that our approach increases the fraction of assigned tasks by more than 20%, and reduces the average detour overhead by more than 60%, compared to the current decentralized approach.
Shih-Fen Cheng received his B.S.E. degree in mechanical engineering from National Taiwan University and Ph.D. degree in industrial and operations engineering from the University of Michigan, Ann Arbor.
His research focuses on the modeling and optimization of complex systems in engineering and business domains. He is particularly interested in the application areas of transportation, manufacturing, and computational markets.