Traffic information is so far less than the number of OD variables, that it is difficult to obtain the satisfactory solution. In this paper, a method based on Quantum behaved Particle Swarm Optimization (QPSO) algorit...Traffic information is so far less than the number of OD variables, that it is difficult to obtain the satisfactory solution. In this paper, a method based on Quantum behaved Particle Swarm Optimization (QPSO) algorithm is developed to obtain the global optimal solution. It designs the method based on QPSO algorithm to solve the OD matrix prediction model, lists the detailed steps and points out how to choose the PSO operator. Moreover, it uses MATLAB program-ming language to carry out the simulation test. The simulation results show that the method has higher efficiency and accuracy.展开更多
Currently, there are kinds of algorithms in order to detect real-time urban traffic condition. Most of these approaches consider speed of vehicles as a main metric to describe traffic situation. In this paper, we find...Currently, there are kinds of algorithms in order to detect real-time urban traffic condition. Most of these approaches consider speed of vehicles as a main metric to describe traffic situation. In this paper, we find out two important observations through several experiments. (1) In urban city, the speed of vehicles is influenced significantly by some factors such as traffic lights delay and road condition. The actual situation rarely satisfy hypothesis required for these solutions. Therefore, these traditional algorithms do not work well in practical environment. (2) Traffic volume on a road segment shows strong pattern and changes smoothly at adjacent time. This feature of traffic volume inspires us to define a metric: traffic-rate, which is used to detect traffic condition in real time. In our solution, we develop a novel traffic-detection algorithm based on original- destination (OD) matrix. We illustrate our approach and measure its performance in real environment. The performance evaluations confirm the effectiveness of our algorithm.展开更多
旅客出行 OD 矩阵是旅客列车开行重要的基础数据。本文主要介绍了一种利用上下车的客流推算路段流量,然后由路段流量反推导旅客出行 OD 矩阵的方法。这个方法建立了多路径交通分配和反推模型,设计了反推模型的迭代算法,并进行了算法分...旅客出行 OD 矩阵是旅客列车开行重要的基础数据。本文主要介绍了一种利用上下车的客流推算路段流量,然后由路段流量反推导旅客出行 OD 矩阵的方法。这个方法建立了多路径交通分配和反推模型,设计了反推模型的迭代算法,并进行了算法分析。文中的算例给出了该方法的合理性与精确性。展开更多
文摘Traffic information is so far less than the number of OD variables, that it is difficult to obtain the satisfactory solution. In this paper, a method based on Quantum behaved Particle Swarm Optimization (QPSO) algorithm is developed to obtain the global optimal solution. It designs the method based on QPSO algorithm to solve the OD matrix prediction model, lists the detailed steps and points out how to choose the PSO operator. Moreover, it uses MATLAB program-ming language to carry out the simulation test. The simulation results show that the method has higher efficiency and accuracy.
文摘Currently, there are kinds of algorithms in order to detect real-time urban traffic condition. Most of these approaches consider speed of vehicles as a main metric to describe traffic situation. In this paper, we find out two important observations through several experiments. (1) In urban city, the speed of vehicles is influenced significantly by some factors such as traffic lights delay and road condition. The actual situation rarely satisfy hypothesis required for these solutions. Therefore, these traditional algorithms do not work well in practical environment. (2) Traffic volume on a road segment shows strong pattern and changes smoothly at adjacent time. This feature of traffic volume inspires us to define a metric: traffic-rate, which is used to detect traffic condition in real time. In our solution, we develop a novel traffic-detection algorithm based on original- destination (OD) matrix. We illustrate our approach and measure its performance in real environment. The performance evaluations confirm the effectiveness of our algorithm.
