【作者】 高嵩；

【导师】 刘有军；

【作者基本信息】 华中科技大学 ， 交通运输规划与管理， 2011， 硕士

【摘要】 城市群的形成和发展已成为全球范围内的典型社会经济现象,这种趋势带来了城市群内人口的增长和区域范围的大幅膨胀,带来的是城市及区域之间的人员流动和交通联系愈加频繁,城市群内的完善而合理的交通网络是城市群健康快速发展的物质基础,因此有必要进行针对性的城市群交通规划,确定城市群交通的发展方向和网络的合理规模。作为规划的主要组成部分,交通需求预测是确定交通网络规模的重要技术手段。根据城市群客流具有的特殊性,找出相对应的客流模型是有其现实意义的。本文分析了城市群问题的研究现状,在此基础上,首先,对城市群的定义和形成做出了归纳,描述了空间分布形式,叙述了城市群客流产生机理,在此基础上,描述和总结了城市群客运需求的特点,并归纳出影响城市群客运交通需求的主要因素。在城市群城际客运交通生成预测,列举了常规的时间序列法、回归分析法及弹性系数法,以及对这三种方法进行组合的预测方法,并指出了它们存在的不足。在此基础上,提出了根据神经网络,利用各种相关因素的预测方法。最后,论文运用交通组合模型将交通方式划分与交通分布两阶段进行组合,简化了四阶段法的预测流程,提高预测精度,并将此模型仿照传统的双约束重力模型进行改进,在引入调整系数,进一步提高结果的准确性。更多还原

【Abstract】 The formation and development of urban agglomeration has become a global typical socio-economic phenomenon,this trend brought population growth and substantial expansion of the regional scope within it, directly result the traffic contact between urban and regional become more frequent, reasonable transportation network is the material basis of healthy and rapid development of urban agglomeration, it is necessary to make targeted urban transportation planning, determine the direction of development and the reasonable scale of traffic network. As a major component, transportation demand forecasting is a important technique to determine it. Combined with the characteristics of urban agglomeration, a reasonable model for traffic demand forecasting is very important.With analysis and summary of existing studies, built traffic demand forecasting model according to characteristics of urban agglomeration. Firstly, elaborate the definition, the formation and spatial distribution patterns, describe the passenger mechanism of urban agglomeration. On this basis, describes and summarizes the characteristics of demand for urban transport demand, summed up the major factors impact the transport demand.In the urban agglomeration passenger trip generation forecast,listing the time series method,regression analysis and elasticity coefficient method,and the combined model of them. And pointed out their shortcomings. On this basis, proposed a model using a variety of related factors based on neural network.Finally,suggest a combine model of traffic distribution and mode split,simplified four-stage method of forecasting process, improve the accuracy, and modeled after the traditional double-constrained gravity model to improved, the introduction of the adjustment factor further improve the accuracy of the results.更多还原