城际间出行分布量预测方法

发布时间：2018-07-07 23:29

本文选题：交通工程 + 城际间出行　；参考：《长安大学学报(自然科学版)》2017年05期

【摘要】：为了准确预测无精确现状出行OD矩阵的城际间出行分布量,首先借鉴区位理论方法,根据城市的土地利用属性和社会经济属性,引入城市区位优势因子,根据各城市的自身繁华程度确定城市的质因子,根据城市的土地利用程度确定城市的吸引量因子,根据城市之间的出行时间确定各城市的相对可达性;其次,根据得到的3类数据从城市的聚集规模因子和可达性2个角度量化城市区位信息,求得各城市的产生区位影响因子和吸引区位影响因子,并提出基于城市区位影响因子的改进重力模型,从而得到城市间的出行分布概率矩阵;再次,根据Furness模型预测城市之间的出行分布量;最后,基于上述模型以珠三角地区9个城市间城际出行的出行分布量预测进行实证研究。结果表明:城市的聚集规模质因子可通过社会经济指标量化,城市间的相对可达性可采用城市间各交通方式出行所需时间的倒数量化;改进后的重力模型无需基准年出行分布量矩阵,利用城市的产生区位影响因子、吸引区位影响因子和相对可达性可以得到城际间出行的分布概率矩阵;根据Furness模型,经过迭代计算求得最终的出行分布量矩阵。提出的出行分布预测方法可以简化基础数据的收集,从而极大地减少城市间居民出行调查工作量,具有较好的普适性。
[Abstract]:In order to accurately predict the inter-city travel distribution without accurate current travel OD matrix, firstly, according to the land use attribute and the social economic attribute of the city, the urban location advantage factor is introduced according to the location theory and method. According to the degree of prosperity of each city to determine the quality factor of the city, according to the degree of urban land use to determine the urban attraction factor, according to the travel time between cities to determine the relative accessibility of each city; secondly, According to the three kinds of data, the paper quantifies the city location information from the two angles of city agglomeration scale factor and reachability, and obtains the producing location influence factor and attraction location influence factor of each city. An improved gravity model based on the influence factors of urban location is proposed to obtain the probability matrix of travel distribution among cities. Thirdly, the travel distribution between cities is predicted according to Furness model. Finally, Based on the above model, an empirical study is carried out on the prediction of intercity travel distribution among nine cities in the Pearl River Delta region. The results show that the mass factor of urban agglomeration can be quantified by social and economic indexes, and the relative reachability between cities can be quantified by reciprocal quantization of the time required for each mode of transportation between cities. The improved gravity model does not need the base annual travel distribution matrix, and the distribution probability matrix of intercity travel can be obtained by using the factors of location influence, attraction location and relative reachability. The final travel distribution matrix is obtained by iterative calculation. The proposed method of trip distribution prediction can simplify the collection of basic data and greatly reduce the workload of intercity residents' travel survey.
【作者单位】：长安大学汽车学院;广东省道路运输管理局;深圳市都市交通规划设计研究院;
【基金】：交通运输部应用基础研究项目(2014319812240) 陕西省自然科学基础研究计划项目(2017JM5084)
【分类号】：U491

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