城际间出行分布量预测方法
Trip distribution forecasting method of intercity-travel

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 predict the trip distribution of intercity-travel without the current accurate OD matrix. Firstly, the urban location advantage factor was introduced based on location theory method and urban land use attributes and socioeconomic attributes. The city’s quality factor was determined by the city’s own prosperous degree, the city’s attractiveness factor was determined by the degree of urban land use, and the relative accessibility of the city was determined by the travel time between cities. Secondly, the urban location information was quantified from two aspects of the city’s aggregation scale factor and accessibility on the basis of the above mentioned three types of data. The city’s production location impact factors and attract location factor were respectively obtained, and the trip distribution probability matrix of different cities was obtained by the improved gravity model. Thirdly, the trip distribution of different cities was forecast by Furness model. Finally, the trip distribution forecasting of the Pearl River Delta Region, including nine cities, was taken as an example based on the above model. The results show that city’s aggregation scale quality factor can be quantified by socioeconomic indicators, accessibility between cities can be quantified by the reciprocal of the time needed for transportation between cities. The improved gravity model doesn’t need the trip distribution matrix of the base year, and the trip distribution probability matrix of intercity-travel can be obtained by production factor, attract location factor and relative accessibility (reciprocal of travel time). According to Furness model, after times of iterative calculation, the final trip distribution matrix can be obtained. The trip distribution forecasting method mentioned in this paper can simplify the collection of basic data and greatly reduce the workload of traffic survey of intercity residents. So it has extensive applicability