Forecasting demand for pickups per hour in 6 New York City boroughs for Uber

Transcription

1 Forecasting demand for pickups per hour in 6 New York City boroughs for Uber Group A4 Name PGID Aniket Jain Rachit Nagalia Nakul Singhal Ayush Anand Priyakansha Paul Prakhar Megotia February, 2019

2 Business Problem and Forecasting Goal Business Problem Data and Analysis Uber can manage its demand by optimizing driver location across six boroughs which in turn will drive pickup efficiency The data contained hourly data of pickups for New York city which is divided in 6 parts -namely, Newark, Manhattan, Bronx, Queens, Staten Island, Brooklyn. Uber will also get a better idea of its surge pricing in advance by understanding gap between capacity & forecasted demand Forecasting Horizon We propose to forecast demand across New York and in each of the six boroughs at an hourly level. Forecasting horizon was 2 weeks, adequate for planning. Source: Uber Data Analysis, Kaggle Data has been pulled from Kaggle for the timeline - 01/01/15 to 30/06/15 (6 months) The data for the Newark (EWR) borough was limited, so we combined it with other data that had borough as NA. The weather variables were included in the data, however we did not consider them very relevant for this analysis, as they would limit our forecast horizon to a few hours. We partition our data providing two weeks of validation data to match pour forecasting horizon.

3 Data Description Data fields o pickup_dt: Time period of the observations o borough: NYC's borough o pickups: Number of pickups for the period o spd: Wind speed in miles/hour o vsb: Visibility in Miles to nearest tenth o temp: temperature in Fahrenheit o dewp: Dew point in Fahrenheit o slp: Sea level pressure o pcp01: 1-hour liquid precipitation o pcp06: 6-hour liquid precipitation o pcp24: 24-hour liquid precipitation o sd: Snow depth in inches o hday: Being a holiday (Y) or not (N)

4 Relevant Charts Examples of boroughs Manhatta n Bronx Brooklyn Queens Source: Uber Data Analytics, Kaggle Pick up by Hour on Weekdays

5 Methods used Seasonal Naïve Forecast Assumed the forecast for last 1 week (for 16th Jun, we took a Naïve forecast of 9th Jun) Linear Regression with seasonal dummies Ran linear regressions creating dummies that captured time of day and day of week. Used a dummy variable for February (to capture the effect of what looked like snow) Linear Regression using external variables Ran linear regressions with other variables but the results were not encouraging. Limited our ability to forecast. Lag Analysis Conducted lag analysis to find any possible autocorrelations

6 Evaluation Over-fitting: Over-fitting was a serious concern with the use of a large number of variables, we found no over-fitting issues in our data. After running various models we found that: Naïve model was the best for all buroughs with limited pickups (Staten Island and Newark) Regression model (with/without lag analysis) was better than Naïve model for the others, but not my much. Something Interesting: In some of the time series, the amount of days where there were 0 pickups (Staten Island, Newark) was almost 30% of data size, so we did not use MAPE and used MAAPE instead, MAPE was used in the others. In almost all series (4 out of 6), the training error was lower than the validation error.

7 Evaluation

8 Evaluation Regression with external (with dummies) variables Borough Naïve model with lag Manhattan Newark (MAAPE) (MAAPE) - No autocorrelation Bronx Brooklyn Queens Staten Island (MAAPE) (MAAPE) - No autocorrelation

9 Recommendations