Abstract
The goal of the project is to estimate the fleet size, creating models and optimizing parameters to get the best revenue and utilization of the vehicles on road.
The Autofleet’s platform is a tool which provides elastic supply of vehicles to any source of demand.
There were many projects approached during the 5 weeks:
- Simulator optimization: Find the optimal parameters of the clients demand simulator, to optimize utilization of vehicles and completion rate of trips.
- Predict number of trips by hour by tile (~neighborhood)
- Analize clients reviews sentiment by different categories.
Challenges
- The most difficult challenge was the lack of data. In some cases synthetic data was created, in others, follows distributions, but wasn’t enough to create a valid model.
- On the NLP project, the fact that the data wasn’t labeled was a challenge, but approaching it as an unsupervised sentiment and with some libraries we achieve a decent naive result.
Achievements (according to KPIs)
- Find the amount, time, distance distributions of the trips by hour during the year.
- Determine that a tile level of prediction is not worth it to do, because most of the trips are located in the city center, and the distances are big, so the losses are greater than the profits.
- Acceptable unsupervised sentiment analysis by 7 categories per review.
Further development
- Integrate the sentiment analysis with google API to increase the accuracy. (NLP)
- Label more data for getting more metrics. (NLP)
- Define more grammatical rules. (NLP)
