Taxi Trip Time
Prediction Analysis

taxi trip time prediction analysis
overview taxi trip time prediction analysis


Machine learning has been of significant help as it has helped businesses in abundant ways. ML is a subset of AI and does not need to be directly trained like AI to perform tasks. ML is used for prediction analysis in businesses, which we will learn in this case study. HData Systems created a solution that can forecast time-based on initial partial trajectories. For someone in the logistics business, this is indispensable. It is important to predict how long a driver will have his taxi occupied. If a dispatcher got estimates about the taxi driver's current ride time, they could better recognize which driver to allocate for each pickup request.

Client Requirement

Most on-demand taxi platforms require a way to know the estimated time which driver will be occupied. Our client was using Google Matrix API for this purpose, but they discovered that it was highly unreliable in their country. Thus, our client approached us to build a robust and reliable solution that could make their business processes much easier. Therefore, HData Systems built these robust solutions using machine learning models to predict the approximate time the driver will stay busy with the taxi. Though it might be super accurate, however, the difference would be negligible and reliable to the previous solutions they used.

client requirements for taxi trip time prediction analysis

The Business Challenge

  • It was a bit tricky to remember the time of day & traffic to forecast accurately.

  • There wasn't any fixed time on how long it generally took to drop off a customer and start a new trip, as it varies with the driver's ability as well.

  • Our team found difficulty with the data as it was very raw, and we needed to clean it first to move further.


  • To predict data, we first get the trip data of the entire year from the system. This data was very raw, and a proper data cleaning was needed. This clean data was plotted using matplot to plot the path in the map. Later this data was plotted on open maps to forecast commute time. With clean and correct data, the system can predict 78% of the time.


The great news is that after 6 months of successfully incorporating this model into our client's business, we saw substantial progress in their work.

They were able to give correct estimates about the estimated ride to their customers or dispatchers. This improved their business workflows as well and helped them in making correct estimates.



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