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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。
In order to accurately predict the inter-city travel distribution of OD matrices with imprecise status quo, firstly, by using the method of location theory, according to the land use attributes and social economic attributes of cities, the city location advantages factors are introduced and the cities’ Based on the degree of urban land use, determine the attractiveness factor of cities, and determine the relative reachability of cities according to the travel time between cities. Secondly, according to the three types of data obtained from the aggregation scale factors and reachability of cities 2 angles were used to quantify the urban location information to find out the influential factors and the influencing factors of each city’s generating location and the improved gravity model based on the urban location influencing factors so as to get the trip distribution probability matrix between cities. Forecast the distribution of travel between cities; Finally, based on the above model, this paper conducts an empirical study on the travel distribution forecast of intercity travel between 9 cities in the Pearl River Delta. The results show that the urban agglomeration mass factor can be quantified through the socio-economic indicators, and the relative reachability between cities can be quantified by the inverse of the time required for each mode of transport between cities. The improved gravity model does not need the baseline annual travel distribution matrix , The distribution probability matrix of inter-city traffic can be obtained by using the influence factors of city location, attracting location impact factors and relative accessibility. According to the Furness model, the final travel distribution matrix can be obtained through iterative calculation. The proposed travel distribution forecasting method can simplify the collection of basic data and thus greatly reduce the workload of urban residents’ travel surveys, which has good universality.