|Title:||Daily variability of ridership and its relationship with rainfall : evidence from a dockless bike-sharing system|
|Advisors:||Xu, Yang (LSGI)|
|Subject:||Human settlements -- Data processing|
Human settlements -- Jamaica
High resolution imaging
Spatial analysis (Statistics)
Hong Kong Polytechnic University -- Dissertations
|Department:||Faculty of Construction and Environment|
Department of Land Surveying and Geo-Informatics
|Pages:||viii, 60 pages : color illustrations|
|Abstract:||A new generation of bike sharing system sweeps through Asia and worldwide by its attractive features of dockless bikes and online payment. This new transportation option provides researchers with an aspect to investigate the mobility of urban residents. Previous studies on bike sharing systems mainly focused on dock-based mobility. Little work has been done to the newly born system and the outer factors that might affect it. In response to such research gap, a 60-day GPS dataset is collected from a company who operated widely used bike sharing system in Singapore. With the application of eigen-decomposition technique, the hidden structural variation patterns at different places in Singapore are extracted, and a hierarchy clustering method is used to group similar patterns. The correlation between environment factors and daily cycling demand of dockless shared-bike system is revealed by correlation analysis, and rainfall contributes the most to the decreasing of daily bike demand. With further study of the periodic rainfall effect on the first principle components (PCs) of different places in Singapore, it is found that rainfall has similar effects on weekday demand and weekend yet different in some ways. The concurrent rainfall has the most negative effect on the cycling demand at the period when it is the easiest to change on both type of days (weekday & weekend). But for places with different demand variate patterns, the responses to rainfall are different. For evening sensitive places on weekdays, rainfall at daytime has a monotonic decreasing until evening sensitive period (4-8pm). For morning (8-12am) sensitive places, rainfall shows a constrained yet negative effect on cycling demand. Some squares have diurnal (8am-8pm) type pattern, which usually occurs in squares with schools. Such squares have the longest sensitive period, the whole 12-hours daytime of Singapore. The long and even lagged negative effect on the cycling demands rainfall shown in these area may raise safety concerns from parents and students. The deviation patterns on weekends are more complex than weekdays'. Besides similar effect of concurrent rainfall on sensitive periods, an interesting insight of weekend diurnal sensitivity squares is that the rainfall before sunrise increases diurnal bike demands in a large amount of squares. In other word, a positive lagged effect is observed on weekend bike ridership in Singapore. Considering the average temperature of Singapore is 26°C - 31°C, the uncommon coolness brought by the rain can stimulate people's desire to go out and ride bikes on weekends. This study also provides insights into users' cycling mobility behaviors, which further support applications such as short-term origin (destination) forecast, simulation of bike demand distribution, and finally, bicycle dispatch optimization.|
|Rights:||All rights reserved|
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