Bikeshare Users on a Budget? Trip Chaining Analysis of Bikeshare User Groups in Chicago
Bikeshare Users on a Budget? Trip Chaining Analysis of Bikeshare User Groups in Chicago
复制标题
DOI：
10.1177/0361198119838261
复制
发表时间：
2019-05
影响因子：
1.7
通讯作者：
Siyue Yang;C. Brakewood;Virgile Nicolas;Jake Sion
中科院分区：
文献类型：
--
作者：
Siyue Yang;C. Brakewood;Virgile Nicolas;Jake Sion
本次分析主要关注一款名为“Transit”的智能手机应用，该应用被用来解锁芝加哥的共享单车。来自该应用程序的数据被用于三部分分析。首先，使用公开数据将Transit应用程序的自行车共享使用模式与系统范围内的自行车共享使用进行了比较。结果显示，工作日的每小时使用量通常遵循经典的高峰通勤模式;然而，周末的每日使用量达到最高水平。这表明可能有大量的通勤和娱乐用户。第二部分旨在通过聚类分析确定不同的用户群;结果显示了六个不同的聚类：（1）通勤者，（2）公用事业用户，（3）休闲用户，（4）不经常通勤者，（5）工作日游客，（6）周末游客。解锁最多共享单车的群体（占所有Transit应用解锁的45.58%）是通勤者，占Transit应用共享单车用户的10%。第三部分提出了一种出行链识别算法，用于识别“出行链骑行者”。这个术语指的是共享单车用户归还共享单车后立即查看另一辆共享单车，大概是为了避免为超过30分钟的行程支付额外的使用费。算法显示，27.3%的公交应用共享单车用户表现出这种类型的“自行车连锁”行为。然而，这在用户群体之间存在很大差异;值得注意的是，66%的公交应用程序共享自行车用户被识别为通勤者，他们进行了一次或多次自行车链解锁。这些影响对于自行车共享提供商了解定价政策的影响非常重要，特别是在鼓励自行车周转方面。
This analysis focuses on a smartphone app known as “Transit” that is used to unlock shared bicycles in Chicago. Data from the app were utilized in a three-part analysis. First, Transit app bikeshare usage patterns were compared with system-wide bikeshare utilization using publicly available data. The results revealed that hourly usage on weekdays generally follows classical peaked commuting patterns; however, daily usage reached its highest level on weekends. This suggests that there may be large numbers of both commuting and recreational users. The second part aimed to identify distinct user groups via cluster analysis; the results revealed six different clusters: (1) commuters, (2) utility users, (3) leisure users, (4) infrequent commuters, (5) weekday visitors, and (6) weekend visitors. The group unlocking the most shared bikes (45.58% of all Transit app unlocks) was commuters, who represent 10% of Transit app bikeshare users. The third part proposed a trip chaining algorithm to identify “trip chaining bikers.” This term refers to bikeshare users who return a shared bicycle and immediately check out another, presumably to avoid paying extra usage fees for trips over 30 min. The algorithm revealed that 27.3% of Transit app bikeshare users exhibited this type of “bike chaining” behavior. However, this varied substantially between user groups; notably, 66% of Transit app bikeshare users identified as commuters made one or more bike chaining unlocks. The implications are important for bikeshare providers to understand the impact of pricing policies, particularly in encouraging the turn-over of bicycles.