No abstract

The comfort of rides has been identified as one of the top criteria that affects passengers' satisfaction with public transportation systems. Conventional comfort measurement approaches rely on professional measure tools or interviews from passengers, which are costly, time-consuming, and not yet feasible. The concept of Internet of Things (IoT) is a new solution to answer this problem. The idea of IoT is to interconnect state-of-the-art digital products in physical world to provide more powerful applications. Vehicles equipped with GPS devices and wireless access technologies are parts of the IoT elements in traffic networks. We use the GPS data to measure the comfort level of vehicle rides, and provide a detailed comfort statistics as a value added service. Using real data collected from one of the Taipei taxi service providers, we show that over 95% taxi trajectories are viewed as comfortable. In addition, rides without passengers get higher comfort scores than with passengers. We also give the rankings of all taxi drivers according to a number of criteria, such as the comfort score and the number of loads. With the ranking results, we can track back to the trajectories and infer drives' driving behaviors, road conditions, and traffic conditions. We believe that the proposed solution has the potential to provide a representative comfort measurement service for taxi services and additional value-added services for public transportation systems.

Abstract-Cooperative spectrum sensing (CSS) in cognitive radio networks conducts cooperation among sensing users to jointly sense the sparse spectrum and utilize available spectrums. Greedy multiple measurement vectors (MMVs) algorithm in the context of compressed sensing can ideally model the wideband CSS scenario to efficiently solve the support detection problem for identification of occupied channels. Actually, the number of sparsity is unknown, and most of greedy algorithms for MMVs lack for a (robust) stopping criterion of determining when the greedy algorithm should terminate. In this paper, we analyze and derive oracle stopping bounds for greedy MMVs algorithms without depending on prior information such as sparsity. Moreover, we introduce a practical subspace MMVs greedy algorithm that extends from a subspace-based sparse recovery method to a more practical setting, in which no prior information are required. Extensive simulations confirm the feasibility of the proposed stopping criteria and our sparse recovery algorithm.