YourCabs Forecasting Analytics Project. Team A6 Pratyush Kumar Shridhar Iyer Nirman Sarkar Devarshi Das Ananya Guha
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1 YourCabs Forecasting Analytics Project Team A6 Pratyush Kumar Shridhar Iyer Nirman Sarkar Devarshi Das Ananya Guha
2 Business Assumptions YourCabs acts as an aggregator of radio-cabs from several operators Yourcabs is aware of the number of cabs available for booking at any given time Yourcabs can arrange for more cabs of any particular type within an hour Cabs can be redirected from any location in the city to any pick up point Cost of having too few cabs is the cost of upgrading a passenger or losing a sale Cost of having too many cabs standing by is loss of relationship with cab operator
3 Business Problem YourCabs offers a wide selection of taxis ranging in luxury and capacity The cab supply is dynamically shifting and depends on the number of cab drivers logged in with YourCabs YourCabs needs to ensure that sufficient taxis of each type are available at any given time to meet the demand For this, they need to predict the demand of each cab type in the immediate future
4 Forecasting Problem YourCabs needs to predict the demand for each cab type to ensure supply of cabs for the next hour at any given time Cab types can be aggregated into segments depending on interchangeability Demand for each segment is to be forecasted at an hourly level for one week
5 Taxi Segmentation 1. Small car : Indica, I-10, Alto, Wagon R etc. (8 models) 2. Sedan: City, Civic, Logan etc. (31 models) 3. Utility: Scorpio, Bolero, Safari etc. (16 models) 4. Bus: Marcopolo, Swaraz Mazda, Volvo etc. (7 models) 5. Premium: Mercedes C class, BMW A8 etc. (17 models) Basis: vehicles which are similar to each other are replaceable so can be clubbed together to form a segment This helped us generate aggregate demand for each type of segment and avoids extremely granular results
6 Data Visualization demand for the month Small cars Sedan Utility Bus & premium Since levels drastically shift in 2013 from , only 2013 data considered for training
7 Segmentwise demand average for each day of the week Small cars Sedans Utility Bus & premium Small cars Light seasonality seen within-week (peaks on Fridays)
8 Segmentwise demand through the day Small cars Sedans Utility Small cars Strong seasonality seen within the day (peaks at 8 am and then at 5 pm)
9 Small Cars Training data: 14th May 2013 to 31 st Oct 2013 (6 months) Validation data: 1 st Nov 2013 to 14 th Nov 2013 (2 weeks) Forecast Horizon: 15 th Nov 2013 to 22 nd Nov 2013 (1 week) Forecasting granularity 1 hour Level: 5 bookings / hour Trend: No dominant trend over forecast horizon Seasonalities considered : Day of the Week, Hour of the day Seasonality appears additive rather than multiplicative Models considered: Naïve forecast Linear Regression with additive seasonality Holt Winter without trend; additive seasonality
10 Small Cars Naïve Forecast Training RMSE Validation RMSE Hourly Adjusted Naïve Forecast Daily Adjusted Naïve Forecast Weekly Adjusted Naïve Forecast Based on the RMSE values the hourly adjusted prediction model fits best to the validation data The Validation plot is also indicative of the same phenomenon However 1 hour would not be very helpful to help plan future capacity Daily Adjusted Naïve forecast is the ideal trade-off considering accuracy and available reaction time
11 Residual Actual vs Predicted Small Cars Naïve Forecast
12 Small cars Linear Regression Sample of fit over training data (16 th Oct 2013 to 31 st Oct 2013)
13 Small cars Linear Regression Fit over Validation data (1 st Nov 2013 to 14 th nov 2013)
14 Small cars Linear Regression Residual Plot over validation data
15 Regression vs Naïve Forecast Regression residuals Naïve residuals RMSE Naïve Forecast Regression Training Period Validation period Linear regression is a better predictor of Small cars demand
16 Forecasts Forecast for the next one week (15 Nov 2013 to 21 Nov 2013) Forecast with 25% FOS
17 Sedans Training data: 14th May 2013 to 31 st Oct 2013 (6 months) Validation data: 1 st Nov 2013 to 14 th Nov 2013 (2 weeks) Forecast Horizon: 15 th Nov 2013 to 22 nd Nov 2013 (1 week) Forecasting granularity 1 hour Level: 2 bookings / hour Trend: No dominant trend over forecast horizon Seasonalities considered : Day of the Week, Hour of the day Seasonality appears additive rather than multiplicative Models considered: Naïve forecast Linear Regression with additive seasonality Holt Winter without trend; additive seasonality
18 Residue Actual vs Predicted Sedans- Naïve Forecast Training RMSE Validation RMSE Hourly Adjusted Naïve Forecast Daily Adjusted Naïve Forecast Weekly Adjusted Naïve Forecast
19 Sedans Linear Regression Sample of fit over training data (16 th Oct 2013 to 31 st Oct 2013)
20 Sedans Linear Regression Fit over Validation data (1 st Nov 2013 to 14 th nov 2013)
21 Sedans Linear Regression Residual Plot over validation data
22 Regression vs Naïve Forecast Regression residuals Naïve residuals RMSE Naïve Forecast Regression Training Period Validation period Linear regression is a better predictor of Small cars demand
23 Sedans- Forecasts Forecast for the next one week (15 Nov 2013 to 21 Nov 2013) Forecast with 30% FOS
24 Utility Training data: 14th May 2013 to 31 st Oct 2013 (6 months) Validation data: 1 st Nov 2013 to 14 th Nov 2013 (2 weeks) Forecast Horizon: 15 th Nov 2013 to 22 nd Nov 2013 (1 week) Forecasting granularity 1 hour Level: <1 bookings / hour Trend: No dominant trend over forecast horizon Seasonalities considered : Day of the Week, Hour of the day Seasonality appears additive rather than multiplicative Models considered: Naïve forecast Linear Regression with additive seasonality Holt Winter without trend; additive seasonality
25 Residue Actual vs Predicted Utility- Naïve Forecast Training RMSE Validation RMSE Hourly Adjusted Naïve Forecast Daily Adjusted Naïve Forecast Weekly Adjusted Naïve Forecast
26 Utility Linear Regression Sample of fit over training data (16 th Oct 2013 to 31 st Oct 2013
27 Utility Linear Regression Fit over Validation data (1 st Nov 2013 to 14 th nov 2013)
28 Utility Linear Regression Residual Plot over validation data
29 Regression vs Naïve Forecast Regression residuals Naïve residuals RMSE Naïve Forecast Regression Training Period Validation period Linear regression is a better predictor of Small cars demand
30 Forecasts Forecast for the next one week (15 Nov 2013 to 21 Nov 2013) Forecast with 50% FOS
31 Bus Premium Premium and Bus segment These 2 segments are not considered while forecasting due to negligible demand as depicted by the graphs alongside. The firm may be moving out of these segments and focusing on the high demand segments
32 Recommendations Linear regression with additive seasonality is the best predictor of Hourly demand Hour of the day and Day of the week seasonality must be taken into account Bookings data can be aggregated for cab model segments Forecasts can be generated at an hourly level for the entire day at the start of the day Forecasts can be rolled forward on a daily basis to include fresh data Since cost of under-prediction is higher than cost of over-prediction, a factor of safety must be used when using forecasts for capacity planning Multiplicative factor of safety is preferred to ensure capturing of peak demand Higher FOS for Utility and Sedans as cost of under-prediction will be higher Cabs in transit that will become available in the next hour must be taken into account when calculating supply
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