Shoufeng Lu

  • Citations Per Year
Learn More
Incremental multistep Q learning (Q( lambda )) combines Q learning and TD(lambda ). Theoretically, Q(lambda ) has better performance than Q learning. The goal of the paper is to test the performance of Q(lambda ) for adaptive traffic signal control. For Q(lambda ), the state is total delay of the intersection, and the action is phase green time change. The(More)
Adaptive and coordinated signal setting has been research emphasis. Cycle, split, and offset are three important parameters. Lessons and experiments about adaptive signal control have shown that cycle and phase sequence renewal interval should be less than 20 minutes. So frequently optimized parameters are split and offset. For Webster signal setting(More)
Multiband model of bandwidth optimization assumes that all the vehicles have the same speed. But there is traffic flow dispersion because of different vehicle performance. Traffic flow dispersion has been described by normal distribution and geometric distribution. The paper integrates traffic flow dispersion model into multiband model to optimize bandwidth(More)
This paper applies the Macroscopic Fundamental Diagram (MFD) and the Generalized Macroscopic Fundamental (GMFD) Diagram to estimate traffic characters for an urban area. We used traffic flow data manually counted from intersection traffic video and taxi GPS data to derive MFD and GMFD. The method is suitable to recognize traffic situation. These figures(More)
  • 1