ABSTRACTEffective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management. 相似文献
The spatial and temporal variability of tidal mixing in Bohai Sea is studied using a numerical approach. In calculating tidal mixing, accurate barotropic tidal current is obtained via a harmonic analysis package utilizing the simulated current output from a high-resolution regional ocean model. And a “small-scale” roughness map is adopted to describe the detailed topographic features of Bohai Sea. It is shown that the tidal mixing estimated in Bohai Sea is much higher than the level of global background, and fluctuates considerably at some regions within a single day. In Liaodong Bay, Bohai Bay and Bohai Strait, the mixing varies greatly, with the peak value of O (10?2) m2 s?1. The order of magnitude of mixing in Laizhou Bay is about O (10?5~10?3) m2 s?1. Mixing with background level of O (10?5) m2 s?1 only appears in central area. Result also shows that rough topography plays relatively a more important role than tidal current in enhancing diapycnal mixing in Bohai Sea. The distributions of tidal mixing in selected sections reveal that the vertical stratification in Bohai Sea is not obvious, generally renders a barotropic structure. 相似文献
Wind turbine technology is well known around the globe as an eco-friendly and effective renewable power source. However, this technology often faces reliability problems due to structural vibration. This study proposes a smart semi-active vibration control system using Magnetorheological (MR) dampers where feedback controllers are optimized with nature-inspired algorithms. Proportional integral derivative (PID) and Proportional integral (PI) controllers are designed to achieve the optimal desired force and current input for MR the damper. PID control parameters are optimized using an Ant colony optimization (ACO) algorithm. The effectiveness of the ACO algorithm is validated by comparing its performance with Ziegler-Nichols (Z-N) and particle swarm optimization (PSO). The placement of the MR damper on the tower is also investigated to ensure structural balance and optimal desired force from the MR damper. The simulation results show that the proposed semi-active PID-ACO control strategy can significantly reduce vibration on the wind turbine tower under different frequencies (i.e., 67%, 73%, 79% and 34.4% at 2 Hz, 3 Hz, 4.6 Hz and 6 Hz, respectively) and amplitudes (i.e. 50%, 58% and 67% for 50 N, 80 N, and 100 N, respectively). In this study, the simulation model is validated with an experimental study in terms of natural frequency, mode shape and uncontrolled response at the 1st mode. The proposed PID-ACO control strategy and optimal MR damper position is also implemented on a lab-scaled wind turbine tower model. The results show that the vibration reduction rate is 66% and 73% in the experimental and simulation study, respectively, at the 1st mode.