【摘要】 居住与就业分布不平衡是大型城市快速发展过程中一个非常突出的问题,会引发交通需求失衡以及严重拥堵。为探讨城市发展过程中职住分布与出行需求的交互关系,依据城市发展宏观数据作为影响变量,建立职住分布与通勤出行的网络动力学模型,并构建以参数关系为基础的小波神经网络作为求解算法。使用北京市宏观经济发展数据和9个典型居住区的职住出行数据进行模型标定和验证,结果显示,该网络动力学模型能够较好地描述大型城市职住分布结构对通勤出行特征的影响关系,并对通勤出行特征变化趋势做出预测。更多还原

【Abstract】 Unbalanced distribution of resident and employment is a very predominant issue in the rapid metropolis development. It can cause the unbalance of traffic demand and heavy traffic congestion. We construct a network dynamics model for the relationship between resident-employment distribution and commuting with urban development macro data as influential variables to investigate the relationship in city development. We also establish parameter relationship based wavelet neural networks to solve the model. We further employ Beijing macro-economic data and commuting data from nine typical residential areas to calibrate and validate the model. Results show that the model can better denote the influence of resident-employment distribution structure on commuting feature and predict the trend of commuting feature variance.更多还原