ANL303: Shared e-scooter has emerged as an affordable transportation means for short-distance trips. It also helps alleviate traffic: Fundamentals of Data Mining Report,, Singapore

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.

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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