基于出行者稳定性的简化活动模型结构研究

Study on a Simplified Activity-Based Model Framework Based on Stability of Travelers

本研究回顾了国内外交通模型发展态势,归纳传统四步骤模型和基于活动的模型等各自的局限性,特别是既有交通规划模型在解释、提取出行者稳定性特征以改善模型精度方面的不足。基于大数据为新时代交通模型带来的机遇与挑战,阐述了利用移动通信位置数据解决上述问题的可能性。针对出行者活动“端点—目的”的稳定性特征,定义了区别于传统四步骤模型中以基家和非基家为基础的出行目的分类,提出了充分利用出行者居住地、工作地(学校)和生活出行场所固定特点,区分稳态出行和偶然出行的简化活动模型,建立一种出行生成与出行分布联合计算的改进四步骤模型结构。以广州市花都区为例,对比分析了基于传统最优化模型的标准重力模型、K系数矩阵重力模型结果与稳态出行矩阵估计的差异。结果表明:最优化模型对城市稳态出行的解释存在不足,基于大数据的城市稳态出行特征分析结果更能反映城市交通活动的实际情况。最后,展望了未来更多出行者活动稳定性因素嵌入交通规划模型的可能性。

From the review of the transport model development trend in China and other countries,the limitations of both traditional four-step model and activity-based model,especially the shortcomings of the current transport mo-dels in explicating and extracting the stability properties of travelers to promote the accuracy of transport model,were summarized.Based on the opportunities and challenges brought with big data to transport model,the feasibility to solve these problems by using location-based services data was discussed.Considering that travel behaviors have stable endpoints-purpose property,this paper defined trip purpose classification which is different from the home-based or none home-based in the traditional four-step model,and developed a simplified activity-based model considering the fact that travelers have fixed house location,job locations(school locations)and other basic daily live locations and the difference between stable trips and occasional trips.Furthermore,a new four-step model structure combined with trip generation and trip distribution was developed.Taking Huadu District of Guangzhou as an example,the results among a new simplified activity-based model,a traditional optimization model(i.e.,the standard gravity model)and a gravity model with K factors was comparatively analyzed.The results show that the optimization model can not reveal the stability attributes of the city,while the simplified activity-based model based on travelers'stability fits urban reality better.Besides,this paper sheds light on the possibility of more stability factors of travelers being introduced to the transport model in the future.