共享单车影响下的居民多日活动-出行决策行为及其演化规律研究

近年来，共享单车投放量和使用量迅速增加，为居民出行“最前/最后一公里”提供了新的出行选择，也对居民出行模式和交通系统产生了显著影响。为了分析这种影响，本项目研究如下内容：基于智能手机采集多城共享单车出行者和非共享单车出行者多日轨迹数据和个体属性数据，提出共享单车和私人单车的识别方法，提取个体多日完整活动-出行链；建立共享单车使用的二元选择模型，匹配共享单车出行者和非共享单车出行者，分析共享单车对公共交通分担率的影响；建立活动-出行链、决策日期、城市、个体和共享单车等五个维度的多日活动-出行需求模型，分析共享单车对多日活动-出行行为的影响；识别共享单车相关可控变量，以提高公共交通分担率和城市交通服务水平为目标，设计主动交通需求管理策略。研究成果在理论上丰富多日出行决策行为的分析框架，在实践上能够为制定共享单车相关的主动交通需求管理政策体系提供决策依据。

For the last several years, the number of shared bikes released and their cyclists are increasing rapidly. As a result, shared bikes become an alternative to solve the problem of “the first/last kilometer” travel, and pose a significant effect on both the travel pattern and the ransportation system. To depict such an effect, this project concentrates on the following contents: proposing a method to differentiate shared bikes and private bikes, mining complete multi-day activity-travel chains based on multi-day trajectories of shared bike users and non-shared bike users collected by smartphones in multiple cities; building a binary choice model of whether to use shared bikes, matching shared bike users and non-shared bike users, and analyzing the effect of shared bikes on the share ratio of transit; visualizing the complete multi-day travel trains, building a multi-day activity-travel demand model with five dimensions of activity-travel, decision date, city, traveler, and shared bike, analyzing the effect of shared bikes on the multi-day activity-travel behavior; recognizing the controllable variables related to shared bikes, designing proactive transportation demand management policies to improve the share ratio of transit and the service level of the ransportation. The results enrich the analysis framework of multi-day travel decisions theoretically, and provide the basis of decision making for formulating proactive transportation demand management policies associated with shared bikes in practice.