The two important features of self-organizing maps (SOM), topological preservation and easy visualization, give it great potential for analyzing multi-dimensional time series, specifically traffic flow time series in an urban traffic network. This paper investigates the application of SOM in the representation and prediction of multi-dimensional traffic time series. First, SOMs are applied to cluster the time series and to project each multi-dimensional vector onto a two-dimensional SOM plane while preserving the topological relationships of the original data. Then, the easy visualization of the SOMs is utilized and several explora- tory methods are used to investigate the physical meaning of the clusters as well as how the traffic flow vec- tors evolve with time. Finally, the k-nearest neighbor (kNN) algorithm is applied to the clustering result to perform short-term predictions of the traffic flow vectors. Analysis of real world traffic data shows the effec- tiveness of these methods for traffic flow predictions, for they can capture the nonlinear information of traffic flows data and predict traffic flows on multiple links simultaneously.
Two characteristics of Chinese mixed traffic invalidate the conventional queuing delay estimates for western countries.First,the driving characteristics of Chinese drivers lead to different delays even though the other conditions are the same.Second,urban traffic flow in China is often hindered by pedestrians at intersections,such that imported intelligent traffic control systems do not work appropriately.Typical delay estimates for Chinese conditions were obtained from data for over 500 vehicle queues in Beijing collected using charge coupled device(CCD)cameras.The results show that the delays mainly depend on the proportion and positions of heavy vehicles in the queue,as well as the start-up situations(with or without interference). A simplified delay estimation model considers vehicle types and positions that compares well with the observed traffic delays.
Complex networks are now the focus of many branches of research. Particularly, the scale-free property of some networks is of great interest, due to their importance and pervasiveness. Recent studies have shown that in some complex networks, e.g., transportation networks and social collaboration networks, the degree distribution follows the so-called "shifted power law" (or Mandelbrot law) P ( k ) ∝ ( k + c )-γ. This study analyzes some evolving networks that grow with linear preferential attachments. Recent results for the quotient Gamma function are used to prove the asymptotic Mandelbrot law for the degree distribution in certain conditions. The best fit values for the scaling exponent, γ , and the shifting coefficient, c , can be directly calculated using Bernoulli polynomial functions. The study proves that the degree distribution of some complex networks follows an asymptotic Mandelbrot law with linear preferential attachment depicted by P k ∝ ( k +(b+a+1)/2^(-b-a).
