6. Conclusions
6.2 Suggestions for future research
Based on the research process and results as shown previously, there are still some work can be performed to enable the research being better and provide some insights for further research. In addition, as a growing number of bikesharing stations and bicycles are appearing in the near future, some of the key research opportunities and recommendations for further research are discussed as following.
1. More robust data retrieval environment
As mentioned in previous chapter, it can be observed that there are several errors regarding the insufficient computer ram run by Python IDE while collecting the data. It is expected to fix this issue by more ram provided or running the retrieving code in the original Python shell instead.
2. To have access to finer grained data as well as match it with other sources of information As the data is collected from retrieving the OpenData API provided the Taipei City Government, the collected data is limited to the information that has set by the government.
Therefore, the collected data only obtains the present number of available bicycles where the data is retrieving and it could only be retrieved at 5 minutes interval as it is constrained to the free provided policy. It would be better to have access to transport authority’s central database, allowing us to view the bikesharing system in the view of trip-basis.
Consequently, the information of each bicycles user’s origination and destination, as well as the journey duration time. It would not only allow us to perform above analysis better through being able to explicitly differentiate between registered user and casual users, but also investigate how bikesharing system is utilised from user’s perspective. In addition, if we match the dataset with other daily information such as precipitation and temperature data, it is expected to explore the effect of weather on daily bikesharing ridership.
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3. Longer period of collecting bikesharing usage data
To collect longer period of bikesharing usage data would allow us to explore station activity patterns more comprehensively through investigating the longer effect and the possible changes which might be brought by system expansion or minor changes of number of bicycles and slot in certain stations. In addition, it may be interesting to perform sensitive analysis to investigate the seasonal and designed changes affect the system.
4. Conducting questionnaire to investigate why people opt to bikesharing
It should be noted that the data we collected by passive sensors would only tell us how, when and what changes occur of the number of available bicycles; however, it fails to incorporate any information as to why people choose bikesharing at a certain station at a certain time. In other words, while we can investigate the variations of bikesharing activities through seeking hints by inspecting places such as transport depots, amenities, or residential area surrounded by the station, it still exist the gap between usage patterns and human behaviour. As a result, it may be useful to conduct a qualitative survey to help clarify and better understand why travellers use public shared-bikes. In addition, it may also decorate the results with these additional contextual information.
5. Incorporating location factors into clustering
The research could be further extended by incorporating location factors such as docking station size, docking station area, or maximum load factor of station during the day.
Therefore, it is expected to examine the relationship between shared-bicycles’ activity and station location and surrounded amenities.
6. Prediction of bicycles availability with the short time outlook into the future
The research also could be further extended by predicting the available bicycles or free slots at a given station at a given time. The prediction model is based on the current state of the station as well as aggregate statistics of the station’s usage patterns. Hence time series analysis technique seems to be able to conduct the task. In addition, the predictions of bike availabilities would allow us to improve the current service of YouBike and enhance users’ satisfaction, enabling the operator to predict shortage or overflow of bicycles in certain stations in advance. Hence, the redistribution plan could be meet the needs more precisely.
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7. Comparative analysis to Kaohsiung City Bike
It is also interesting that to compare the station activity patterns with Kaohsiung City. Not only specific station activity patterns of Kaohsiung City can be explored but also to examine the temporal trends of City Bike usage, reflecting the pulse of active transport users and their transport behaviours in Kaohsiung.
8. Redistribution mechanism and management
As conducting average daily station activity patterns of each station and clustering results, it gives insights on the location of bicycle redistribution hubs and provide the robust information of bicycle availability throughout the day. Therefore, the optimal redistribution routes can be achieved and it allows us to know where and when to redistributing the bicycles to and from the full/empty station at acceptable cost basis.
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