Nyc taxi data github

Big Data involves large volume of data, which cannot be processed using traditional tools. These data sets are complex and evolves continuously in real time. The challenge is to analyze and extract useful knowledge from this volume of data. There are many areas which can increase an organization's profit by observing user patterns.

Hence, we need some techniques in order to analyze the user behavior and accordingly generate some patterns. In this paper we will propose some general ideas by which we can gather user behavior and how that information can be used to generate further information specific to a user. We will use Hadoop echo system as our framework that will implement Map-Reduce to analyze these patterns in an acceptable time.

How Taxis Arrive at Fares? — Predicting New York City Yellow Cab Fares

Based on these patterns, we can suggest the user of the other related patterns. Number of Records: 77, Rows Size of the file: Pre-processing has been performed using python script. Below are the type of record which will be deleted after pre-processing. Source Code. For calculating busiest area of New York we have calculated pickup and drop-off location counts by rounding latitude and longitude to 3 decimal points. What about the precision of the location?

What is the distance between the two rounded points? General New York latitude and longitude is XXXXX and We have rounded latitude and longitude by three decimal points in pickup and drop-off locations, so the distance between the two points will be approximately 80 m. We have considered the drop-off location to compute speed. After specifying the drop-off location latitude and longitude, we get the average speed of the taxi around that location for every one hour.

We have applied the logic- that if the average speed is high at a particular location, it implies that traffic is less in that area. One of the outputs we derived from this analysis was average number of pickups for a day in New York and for that we rounded the pickup time by an hour and counted the number of pickups for every hour and then we just divided the final count by because our data is from Jan to Jun which is days. Using these outputs, taxi drivers can schedule their day and hence make maximum profit in a day.

As a final output, we calculated location wise, tip percentage. Linked In Email. Introduction Big Data involves large volume of data, which cannot be processed using traditional tools.

Below are the type of record which will be deleted after pre-processing Record with less then 17 attributes. Record with pickup latitude and longitude as 0. Top Pick up Location.On the other hand, to visualize the information extracted from data, the libraries in below are also needed. And Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.

Furthermore, to deal with the large scale of data 4GB for 6 months in this casea database is needed. A shapefile containing the boundaries for the taxi zones can be found here. In the following 3 Analytic Problems, I will show my analytic results and graph visualizations along with the Python code that generates the plots.

Next, we investigate boroughs with most pickups and drop-offs. In the tables below, we can see that Manhattan is obviously the most popular borough and Staten Island is the least popular borough. In the figure above, it is noticed that in the first half ofthere are more pickups in Queens than in Brooklyn while there are similar number of drop-offs in both Queens and Brooklyn. According to the dustribution of trip distances and the fact that it takes about 30 miles to drive across the whole New York City, we decided to use 30 as the number to split the trips into short or long distance trips.

After extracting data from database, we then arrange the information and show the top 3 'pickup zone', 'dropoff zone' pair for both short trips and long trips.

These findings support our guesses that long trips are for traveling and that short trips are for eating and entertaining. On the other hand, we can also observe the popular zones for short and long trips on map. Unexpectedly, the distribution of passenger count is nearly the same for short trips and long trips.

RateCodeID represents the final rate code in effect at the end of the trip:. It can be seen that 40 percent of long trips use Negotiated fare and another 40 percent of long trips use JFKNewarkor Nassau or Westchester while less than 5 percent of short trips use any of them. Passengers of long trips paid a little more frequent in credit card and a little less frequent in cash comparing to that of short trips.

Download the Trip Record Data for month in range 17 : urllib. Short Trips: records in total. Long Trips: records in total. Prev: Categorical Data Clustering. Next: Aggregation of Clustering Methods. Please enable JavaScript to view the comments powered by Disqus.New York City, being the most populous city in the United States, has a vast and complex transportation system, including one of the largest subway systems in the world and a large fleet of more than 13, yellow and green taxis, that have become iconic subjects in photographs and movies.

Thanks to some FOIL requestsdata about these taxi trips has been available to the public since last year, making it a data scientist's dream. We endeavoured to delve into this gold mine using 2. The primary objective of this project was to predict the density of taxi pickups throughout New York City as it changes from day to day and hour to hour. So, given a specific location, date and time, can we predict the number of pickups in that location to a reasonably high accuracy? A secondary objective was to also predict the dropoff location.

Predictive models like these are interesting for many people, including of course the taxi companies themselves. After preparing the data in the cloud with Amazon Web Services, we trained random forests with deep trees to predict the pickup density. We did that in two approaches, one which predicts pickup density on an average day of the week e.

A second forest predicts pickup density on a specific day e. May 1, The second, which also incorporates weather data, still does reasonable well, predicting density within about a factor of 1. Lastly, we started work on predicting where people wanted to be dropped off, based on their pickup location. Initial results aren't terribly good, but we have ideas to improve upon this.

Throughout the days of the year horizontal axis and the hours of the day vertical axis. Predicted Density Distribution vs. Actual Density Distribution on a Monday. The above image shows the predicted number of pickups on a given Monday using a random forest regressor on the the left and the actual number of pickups on the right.

The sheet number at the top of each image corresponds to the hour of the day.

Data Visualization

Underneath you find the importance of each of the features in the random forest. Clearly location is most important, followed by time of the day. Note: the noise in the data became more apparent when we used this fine temporal granularity, and the prediction accuracy decreased. We believe this results from the regressor thinking that that no data for a particular location and time means the number of pickups is unknown. Of course in reality, no records for a particular location and time means zero pickups at that location and time, because we assume that all taxi trips are recorded.

We hypothesize that this shortcoming in our data preparation leads to the widespread overprediction in areas outside Manhattan. You can play around in Tableau by clicking on the image below to explore the dropoff locations, given a pickup location.

To really make the pickup density model shine we would have to adjust the data preparation, so that we feed information about locations without any pickups to the model as well.

Right now our model receives no data about the number of pickups in these locations is and thus thinks that the number of pickups is unknown. However, the absence of records at some locations means that there were zero rides in that time period.

TLC Trip Record Data

We believe that training a model with that knowledge would lead to more accurate predictions for the number of pickups on a specific date and time, such as May 1st at 6am. The Yellow Taxicab: an NYC Icon Harvard Data Science Final Project Video New York City, being the most populous city in the United States, has a vast and complex transportation system, including one of the largest subway systems in the world and a large fleet of more than 13, yellow and green taxis, that have become iconic subjects in photographs and movies.

Predicting pickup density using million taxi trips Thanks to some FOIL requestsdata about these taxi trips has been available to the public since last year, making it a data scientist's dream. Making transportation more efficient Predictive models like these are interesting for many people, including of course the taxi companies themselves. The model can be enhanced in future to incorporate features like weather, holiday etc.

Data scientists : It is interesting for data scientists to see how we have modeled location data in a simple way and yet able to get reasonably good predictions Random forests find the hot spots After preparing the data in the cloud with Amazon Web Services, we trained random forests with deep trees to predict the pickup density.

Tutorial: Load the New York Taxicab dataset

Our Approach 1. Exploratory data analysis The data is currently available in Google BigQuerywhich allowed us to explore the data directly in Tableau. Number of Pickups in and Throughout the days of the year horizontal axis and the hours of the day vertical axis 3.Predicting taxi fare is definitely not as flourishing as predicting airline fares. However, since we do not currently have airline fare open data available, why not start practicing from predicting taxi fare?

In this task, we are going to predict the fare amount for a taxi ride in New York City, given the pick up, drop off locations and the date time of the pick up. We will start from creating a simplest model after some basic data cleaning, this simple model is not Machine Learning, then we will move to more sophisticated models. The data set can be downloaded from Kaggle and the entire training set contains 55 million taxi rides, we will use 5 million. The dataset is taxi rides.

We have a look first a few rows. When we look at statistic summary, we have several discoveries:. Then we check statistical summary again. Some ride distances were very short, some are in the middle distance, one of them was pretty long. Fare amount. The histogram of fare amount shows that most fare amount are very small.

nyc taxi data github

The most common fare amount are very small at only 6. Passenger count. The first model we are going to create is a simple model based on rate calculation, no Machine Learning involved. We expect ML will achieve better than this. Our new data frame looks like after adding new features.

The minimum distance is 0, we will remove all 0 distance. We are ready for more sophisticated models and beat the RMSE produced by the baseline model.

Jupyter notebook can be found on Github. Have a great weekend! Sign in. How Taxis Arrive at Fares?

nyc taxi data github

How to go from a baseline model to machine learning. Susan Li Follow. Towards Data Science A Medium publication sharing concepts, ideas, and codes. Changing the world, one post at a time. Sr Data Scientist, Toronto Canada.

Towards Data Science Follow. A Medium publication sharing concepts, ideas, and codes. See responses 3. More From Medium. More from Towards Data Science. Rhea Moutafis in Towards Data Science. Emmett Boudreau in Towards Data Science. Discover Medium. Make Medium yours. Become a member. About Help Legal.If you don't have an Azure subscription, create a free account before you begin.

A SQL pool is created with a defined set of compute resources. Select Server to create and configure a new server for your new database. Fill out the New server form with the following information:. Select Performance level to specify whether the data warehouse is Gen1 or Gen2, and the number of data warehouse units. For this tutorial, select SQL pool Gen2.

The slider is set to DWc by default. Try moving it up and down to see how it works.

Big Data in the Cloud #3 - NYCTaxi Dataset & HBase Datamodel

In the provisioning blade, select a collation for the blank database. For this tutorial, use the default value. For more information about collations, see Collations. Now that you have completed the form, select Create to provision the database.

Provisioning takes a few minutes. A firewall at the server-level that prevents external applications and tools from connecting to the server or any databases on the server.

To enable connectivity, you can add firewall rules that enable connectivity for specific IP addresses. Follow these steps to create a server-level firewall rule for your client's IP address. SQL Data Warehouse communicates over port If you are trying to connect from within a corporate network, outbound traffic over port might not be allowed by your network's firewall. The overview page for your database opens, showing you the fully qualified server name such as mynewserver Copy this fully qualified server name for use to connect to your server and its databases in subsequent quick starts.

Then select on the server name to open server settings. Select Show firewall settings. A firewall rule can open port for a single IP address or a range of IP addresses. Select Save. A server-level firewall rule is created for your current IP address opening port on the logical server.

When you connect, use the ServerAdmin account you created previously. Get the fully qualified server name for your SQL server in the Azure portal. Later you will use the fully qualified name when connecting to the server. In the Essentials pane in the Azure portal page for your database, locate and then copy the Server name. In this example, the fully qualified name is mynewserver In Object Explorer, expand Databases.Set up and import data for nyc-taxi-data and nyc-citibike-data repos.

You don't need all of the supporting analysis tables, but you do need the trips table from each database. If you want to make things go faster, you could load only data since Julyor download some of the processed data from Amazon S3 see below.

Having multiple. See here for instructions on how to download from a requester pays S3 bucket. The data is 1. Note that the dataset available on S3 is only the subset of taxi and Citi Bike trips used in the various Monte Carlo simulations, and other analysis code in this repo e.

I applied filters to both datasets to try to make them as comparable as possible, and also to try to maximize the percentage of Citi Bike trips in particular where the rider was likely trying to get from point A to point B relatively quickly. For both datasets, I filtered to weekday trips only, excluding holidays. Traffic patterns are different on weekdays and weekends, and I was afraid that weekend Citi Bike rides would often be primarily for leisure, not efficient transportation.

Within the Citi Bike dataset, I removed trips by daily use customers, keeping only the trips made by annual subscribers. Subscribers are more likely to be regular commuters, while daily users are more likely to be tourists who, even during the week, might ride more for the scenery than for an efficient commute.

Within the taxi dataset, I restricted to trips that picked up and dropped off within areas served by the Citi Bike system, i. Starting in Julyperhaps owing to privacy concernsthe TLC stopped providing precise latitude and longitude coordinates for every taxi trip. Instead, the TLC provides the pickup and drop off taxi zone for each trip, where the zones— see here for a map—roughly correspond to the neighborhoods of the city.

Harlem and Astoria were the Citi Bike-less residential neighborhoods with the most taxi trips, though they are part of Citi Bike's expansion plans.

I removed trips from both datasets that started and ended within the same zone. For Citi Bikes, these trips often started and ended at the same station, which is clearly an indication that the rider wasn't trying to get from point A to point B quickly, and even in cases where they started and ended at different stations, it seems likely that many of those trips might not be for commuting purposes.

When considering trips confined to a single zone, it also seems more likely that the average taxi and Citi Bike trip distances might differ by a larger magnitude than trips that span multiple zones. For most analyses, I used the most recent year of available data, July 1, to June 30,but for some I included all data since July to see how the taxi vs.

Citi Bike calculus might have changed over time.There are a few sources. I got it from here. There are of course plenty of ways to get the data into shape.

I chose whatever I could think of most quickly. There is probably an awk one-liner or more efficient way to do it, but it's not very much data and these steps didn't take long. There are two sets of files - one for trip data and one for fare data. This site has them broken down into 12 files for each set.

No good. I converted them to unix format with dos2unixwhich may not be installed on all linux flavors, but it's easy to install or there are other ways to deal with it. Looking at the files, it turns out that the number of lines match for each numbered trip and fare file. It would be nice to merge these, but we should make sure before merging that the rows match. We can run a simple awk command to make sure each these match for each row.

The code is commented out because we have already verified this so no need to re-run unless you really want to. Everything matches, except some header lines have spaces and therefore don't match. Reading in the raw data to R is as simple as calling drRead. However, some initial exploration revealed some transformations that would be good to first apply.

Second, there are some very large outliers in the pickup and dropoff latitude and longitude that are not plausible and will hinder our analysis. We could deal with this later, but might as well take care of it up front. Note that these quantiles have been computed at intervals of 0, 0. So we have some very egregious outliers.

We will set any coordinates outside of this bounding box to NA in our initial transformation. We don't want to remove them altogether as they may contain other interesting information that may be valid.

Here are some simple quick summaries to help us start to get a feel for the data and where we might want to start taking a deeper look. There's a lot more to look at, this is just a quick start. Here we make use of some of datadr's division-indpendent summary methods that operate over the entire data set and do not care about how it is divided. This gives us some initial interesting insight. Let's visualize some of these summaries. The ddf summary method has a cutoff for how many unique levels of a variable to tabulate to avoid tail cases where there might be millions of unique values.

In this case, the top and bottom medallions and hack licenses are reported. This is also interesting.

nyc taxi data github

For some of the quantitative variables, we want to look at more than just some summary statistics. We can compute and look at quantile plots for these using drQuantile. The plot on the left shows everything, while the plot on the right truncates the highest outlying percentiles. The max number of passengers is - this is surely an invalid value.

Half of cab rides are 10 minutes or less, nearly all are less than an hour. The maximum is minutes. It looks like there is some rounding going on in some cases, not in others.


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