摘要
随着社会网络等新兴技术与沟通方式的发展,动态共乘作为一种新的出行方式在许多国家和地区得到了推广,被认为是缓解当前城市交通压力的重要补充手段,然而它的供需匹配情况如何还不得而知,有很多因素会影响匹配的成功率。根据出行决策理论和网络信任理论挑选6个影响动态共乘匹配的个体因素和3个城市因素,构建了单层面板计数模型和双层线性回归模型,利用某社会网络的实际数据与这2个模型对影响动态共乘匹配的因素分别进行了分析,不仅发现了各因素对匹配影响的显著性,而且还发现,这些个体因素的影响在不同城市存在差异,同时,城市因素通过个体因素也会改变动态共乘的匹配结果。
With increasing penetration of social networks,dynamic ridesharing（DR）evolves to be popular in many countries.As a good supplement,DR to some extent helps to alleviate the traffic congestions for metropolises.However,how to match the two sides in DR is yet to investigate.This paper,grounded on the theory of activity travel pattern and online trust,screens six factors at the individual level and three factors at the city level.A single-layer count panel model and a double-layer hierarchical linear model are developed to help find out which factors significantly influence DR matching based on a real dataset.The results demonstrate the different influence of the factors in different cities as well as the indirect influence of factors at the city on DR matching.The findings are helpful to facilitate the development of DR.
出处
《系统管理学报》
CSSCI
北大核心
2016年第5期837-843,共7页
Journal of Systems & Management
基金
国家自然科学基金资助项目(71372108)
教育部人文社科项目(12YJC630059)
