| University | Singapore University of Social Science (SUSS) |
| Subject | ANL303: Fundamentals of Data Mining |
Shared e-scooter has emerged as an affordable transportation means for short-distance trips. It also helps alleviate traffic congestion by reducing the number of trips made by vehicles. However, e-scooter-sharing service providers face numerous operational challenges.
One of the challenges is to ensure that riders can always rent and park the e-scooters at stations and that the e-scooters can be sufficiently charged before the next trip. Ideally, at the start of a trip, stations should have sufficiently charged e-scooters for riders to rent; at the end of a trip, stations should not be full so that riders can park the e-scooters.
From an operational perspective, this requires service providers to send trucks to redistribute e-scooters from full stations to empty stations so as to have balanced stations. Some providers have been spending a lot of money to perform overnight charging and re-allocation of e-scooters among stations.
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| Variable | Description |
| Ride ID | The unique identifier of a ride |
| Started at | The start time of a ride (dd/mm/yyyy hh:mm) |
| Ended at | The end time of a ride (dd/mm/yyyy hh:mm) |
| Start station name | The name of the start station |
| Start station ID | The unique ID of the start station |
| End station name | The name of the end station |
| End station ID | The unique ID of the end station |
| Start latitude | The latitude of the start station |
| Stat longitude | The longitude of the start station |
| End latitude | The latitude of the end station |
| End longitude | The longitude of the end station |
| Member | Whether the rider is a member (yes/no) |
Assume that a dataset is collected from an e-scooter sharing service provider to perform data mining in an attempt to generate useful insights to solve the abovementioned rebalancing problem. The dataset contains details of each e-scooter trip made by individual riders. The variables in the dataset are described in Table 1.
- Give one (1) example of data quality issues that may potentially exist in the dataset described in Table 1 and propose a solution for it.
- (b) Based on the dataset described in Table 1, give one (1) example of descriptive data mining and discuss how it might generate useful insights to solve the rebalancing problem.
- Based on the dataset described in Table 1, give one (1) example of predictive data mining and discuss how it might generate useful insights to solve the rebalancing problem.
- Suggest two (2) additional variables that could be included in the analysis for solving the rebalancing problem. Explain the rationale for their inclusion and describe how you can collect them.
- When the rebalancing problem is not handled properly, one of the consequences is that some riders do not return the e-scooters to the designated stations if there is no empty parking slot at their desired end stations. Describe how this would bring negative impacts to society.
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