Yang Xi

TL;DR: In this article, a road network traffic signal iterative learning control method based on MFD is proposed to enable the overall structure of the road network to achieve relative balance, can improve the outflow vehicles of the sub-area so as to improve the traffic volume of road network, provides an effective urban road network control means for the traffic manager, and improves the traffic service level of the urban road networks.

TL;DR: In this paper, an adaptive ILC (iterative learning control) based coordinated control method for urban main roads comprises: a. determining a key intersection: for the controlled main roads, the intersection with the largest traffic demand is determined as the key intersection; b, initializing the common signal period, green time ratio and phase difference; c, optimizing the green time ratios at key intersections; d, optimising the green times ratio at noncritical intersections; e, performing circulation, and repeating steps c and d every 3-5 signal cycles.

TL;DR: In this paper, a micro-blog short text classification method based on lexical chain feature extension and an LDA (latent Dirichlet allocation) model was proposed, and the method was evaluated in terms of short length, less content, sparse feature and the like of a microblog text.

TL;DR: In this article, the authors proposed an optimization method for road network traffic flow based on an improved TASEP model, which comprises the following steps of: firstly, setting the edges of the network to have different hopping rates and conform to the Poisson distribution; combining the real-time flow values and the number of particles on the corresponding sides at each intersection of the road network to design path strategies of the particles and control the motions of particles; and finally, selecting a reasonable path of particles according to the roulette algorithm.