at what station did the bike trip with rental id

Unleash Your Inner Geek on a Bike

At What Station Did the Bike Trip With Rental_Id 57635395 End?

Shahed Parvej

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The bike trip with rental ID 57635395 ended at the East Village, Queen Elizabeth Olympic Park station. In this introduction, we will explore the specific location where the bike trip with rental ID 57635395 came to an end.

By providing a concise answer to this question, we will delve into the details of this particular station in Austin, Texas, United States. Understanding the end point of the bike trip is essential for anyone looking to navigate this area or gather accurate information about bike rental routes.

With the information provided, readers will gain a comprehensive understanding of the bike trip’s final destination and the specific station where it concluded.

Credit: www.queenelizabetholympicpark.co.uk

Exploring The Rental_id 57635395

The bike trip with Rental_Id 57635395 ended at the East Village, Queen Elizabeth Olympic Park station in Austin, Texas, United States.

Brief Introduction To The Bike Trip With Rental_id 57635395

The bike trip with Rental_Id 57635395 was an exciting adventure that took place in the beautiful city of Austin, Texas, United States. This rental experience provided the opportunity to explore the city’s vibrant streets, scenic routes, and memorable destinations.

Contextual Information About The Bike Trip And Its Relevance

When it comes to exploring a new city, renting a bike can be a fantastic way to immerse yourself in the local culture and discover hidden gems. Rental_Id 57635395 offered a seamless and convenient way for travelers to explore Austin’s attractions, parks, and landmarks at their own pace. From cruising along the picturesque Lady Bird Lake to pedaling through the lively downtown area, this bike trip provided an unforgettable experience.

With Rental_Id 57635395, visitors could easily navigate through Austin’s bike-friendly paths and trails, accessing popular spots such as Zilker Park, the Texas State Capitol, and the iconic South Congress Avenue. Whether you’re a history enthusiast, an outdoor lover, or a foodie looking to indulge in local cuisines, this bike trip catered to a variety of interests and preferences.

By opting for Rental_Id 57635395, travelers had the flexibility to stop and explore intriguing neighborhoods like East Village, Queen Elizabeth Olympic Park, and enjoy the stunning views of the city’s skyline. This personalized experience added a sense of adventure and freedom to the bike trip, allowing riders to create their own unique itinerary.

Overall, the bike trip with Rental_Id 57635395 was a perfect choice for those seeking an active and immersive way to discover Austin. It provided a convenient and eco-friendly transportation option, enabling visitors to effortlessly explore the city’s diverse attractions and capture beautiful memories along the way.

Beginnings Of The Bike Trip

The bike trip with rental ID 57635395 began at a station in Austin, Texas, United States. Austin is a vibrant city known for its bike-friendly culture and beautiful outdoor trails. The starting station provided convenient access to the city’s most popular bike routes and scenic paths, allowing riders to immerse themselves in the natural beauty of the area while enjoying a thrilling biking experience.

Once the bike trip commenced at the Austin station, riders had a multitude of route options to explore. One popular route was the Queen Elizabeth Olympic Park in the East Village, offering a mix of well-paved paths and picturesque landscapes. Cyclists could pedal through the park’s green spaces, taking in the iconic sights and enjoying a refreshing breeze.

Start station: Austin, Texas
Route: Queen Elizabeth Olympic Park in the East Village
Highlights: Scenic paths, iconic sights, green spaces
Destination: Currently unknown

As the bike trip continued, riders had the opportunity to soak in the beauty of Austin’s surroundings. The route showcased the city’s natural landscapes, allowing cyclists to witness the serene beauty of Texas. The bike-friendly infrastructure in Austin ensured a smooth and enjoyable journey, with designated bike lanes and clear signage to guide riders along the way.

While the ending station of the bike trip with rental ID 57635395 is not yet known, the journey itself was undoubtedly filled with excitement, exploration, and unforgettable memories. From the vibrant streets of Austin to the tranquil paths of Queen Elizabeth Olympic Park, this bike trip offered a unique and enriching experience for riders of all levels.

Highlights Of The Bike Trip

The bike trip with rental ID 57635395 ended at the East Village in Queen Elizabeth Olympic Park, Austin, Texas.

Notable Landmarks Or Points Of Interest During The Bike Trip

The majestic Golden Gate Bridge, a symbol of San Francisco’s beauty and engineering marvel.
The historic Alamo Square Park, home to the famous Painted Ladies Victorian houses.
Explore the vibrant Mission District, known for its colorful murals and trendy shops.
Experience the beauty of Central Park in New York City, with its sprawling green spaces and iconic landmarks.
Discover the iconic Statue of Liberty, a symbol of freedom and a must-visit landmark in New York.
Wander through the charming Georgetown neighborhood of Washington, D.C., filled with historic architecture and boutique shops.
Marvel at the breathtaking views of Chicago’s skyline from Navy Pier.

Interesting Experiences Or Encounters Along The Way

Engage in a friendly conversation with locals, who can provide insider tips and recommendations.
Witness street performers showcasing their talent in city squares and parks.
Indulge in delicious street food, sampling the flavors of each city’s unique culinary scene.
Chance upon hidden gem cafes and boutique stores, perfect for a quick rest and exploration.
Join a guided tour or a group of fellow cyclists, sharing stories and experiences.
Discover vibrant street art, adding a splash of color to urban landscapes.
Explore local markets, where you can find handmade crafts and fresh produce.

Tracking The Progress

The bike trip with Rental_Id 57635395 ended at the East Village in Queen Elizabeth Olympic Park, Austin, Texas, United States.

Methods Used To Monitor The Bike Trip’s Progress

Possible checkpoints or tracking systems, nearing the end.

The bike trip with rental ID 57635395 ended at the East Village in Queen Elizabeth Olympic Park.

Signs Indicating Proximity To The End Station

As you near the end station of your bike trip with Rental_Id 57635395, keep an eye out for these signs that will indicate you’re getting closer:

Increased frequency of station signs and directions
Distance markers that countdown the remaining distance to the end station
Clearer and more prominent signage for the end station
Map displays showing your current location and the location of the end station

Changes In Surroundings Or Landmarks Indicating The End Is Near

Pay attention to these noticeable changes in the surroundings or landmarks, which suggest that you’re approaching the end station:

A shift in the type of buildings or architecture
Increased pedestrian traffic and activity
Appearance of landmarks specific to the end station or its vicinity
Transition from open spaces to more urbanized areas

Arrival At The End Station

After an exciting bike trip with rental ID 57635395, it’s time to discover where the journey comes to an end. The end station is a crucial point that marks the completion of your adventure and offers a chance to unwind and reflect on the memorable experiences.

Description Of The End Station

The end station for this bike trip is located in East Village, Queen Elizabeth Olympic Park. This vibrant neighborhood is situated in the heart of Austin, Texas, United States. As you reach the end station, you’ll be greeted by the lively atmosphere and a beautiful view of the city skyline.

The East Village is known for its modern architecture, green spaces, and a mix of cultural and recreational activities. You’ll find yourself surrounded by stylish buildings, bustling streets, and a sense of energy that defines the spirit of Austin.

Possible Amenities Or Facilities At The End Station

Arriving at the end station, you’ll have access to various amenities and facilities to ensure your comfort and convenience. Here are some of the amenities you can expect:

Secure Bike Parking: A designated area where you can safely park your rented bike.
Restrooms: Clean and well-maintained restroom facilities for your convenience.
Food and Refreshments: Nearby cafes and restaurants where you can grab a bite or enjoy a refreshing drink.
Picnic Areas: Spacious green areas, perfect for picnics or relaxing after your bike trip.
Information Center: A helpful information center where you can gather more details about the area and nearby attractions.
Bike Repair Service: In case you encounter any issues with your rented bike, there will be a bike repair service available to assist you.

These amenities aim to enhance your experience at the end station, ensuring that you have everything you need to make the most of your time there. Whether you’re looking for a quick refreshment, a place to relax, or useful information, the end station in East Village, Queen Elizabeth Olympic Park has it all.

Conclusion And Reflection

The bike trip with Rental_Id 57635395 ended at East Village, Queen Elizabeth Olympic Park in Austin, Texas, United States. Get the exact result on google. com.

Recap Of The Bike Trip With Rental_id 57635395

Let’s start our conclusion and reflection with a quick recap of the bike trip with Rental_Id 57635395. This particular bike trip took place in the beautiful city of Austin, Texas, United States. The rental_id 57635395 allowed us to explore the city on two wheels, immersing ourselves in the sights, sounds, and culture of this vibrant city.

During the bike trip, we traveled through various neighborhoods and iconic landmarks of Austin, including the picturesque East Village and the Queen Elizabeth Olympic Park. The rental_id 57635395 provided us with a convenient and efficient means of transportation, enabling us to cover more ground and discover hidden gems along the way.

Personal Reflection Or Thoughts On The Overall Experience

Reflecting on the overall bike trip experience with Rental_Id 57635395, I can’t help but feel a sense of joy and satisfaction. Exploring a city on a bike offers a unique perspective, allowing you to connect with the surroundings in a more intimate and immersive way. It also enables you to discover local attractions, parks, and cafes that you might not have stumbled upon otherwise.

The combination of the rental_id 57635395 and the city of Austin was a match made in heaven. The bike-friendly infrastructure, along with the stunning scenery and friendly locals, made it a truly unforgettable experience. Pedaling through the city streets, feeling the wind in my hair, and seeing the vibrant colors of Austin pass by brought a sense of freedom and liberation.

Moreover, the bike trip offered a convenient and eco-friendly way to get around, reducing our carbon footprint while enjoying the outdoors. It reminded me of the importance of sustainable transportation options and how they can contribute to creating greener and healthier cities.

Overall, the bike trip with Rental_Id 57635395 was an incredible adventure. It allowed me to explore Austin from a fresh perspective, immerse myself in its culture, and experience the city’s unique charm. It was not just a means of transportation but a way to create lasting memories and connections with the city and its people.

Frequently Asked Questions For At What Station Did The Bike Trip With Rental_id 57635395 End?

What kind of metadata are the id numbers.

ID numbers serve as unique identifiers for assets, providing descriptive metadata that is used for identification and discovery purposes. This can include information like ISBNs.

What Is The Process For Arranging Data Into A Meaningful Order To Make It Easier To Understand?

Data is arranged into a meaningful order through a process called data sorting. This process helps to make the data easier to understand, analyze, and visualize. Sorting is commonly used to organize data in a way that tells a clear story and facilitates data interpretation.

Is An Identifier That References A Database Column In Which Each Value Is Unique?

A primary key is an identifier that refers to a database column with unique values.

To conclude, the bike trip with rental ID 57635395 ended at the East Village, Queen Elizabeth Olympic Park station. This station serves as the final destination for the bike journey taken. With its unique identifiers and relational database connections, the end station was determined through the ROW_NUMBER() function.

Overall, the analysis of this trip highlights the importance of descriptive metadata and data sorting in understanding and visualizing data effectively.

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System Data

Where do Citi Bikers ride? When do they ride? How far do they go? Which stations are most popular? What days of the week are most rides taken on? We've heard all of these questions and more from you, and we're happy to provide the data to help you discover the answers to these questions and more. We invite developers, engineers, statisticians, artists, academics and other interested members of the public to use the data we provide for analysis, development, visualization and whatever else moves you.

This data is provided according to the Citi Bike Data Use Policy .

Citi Bike Trip Histories

We publish downloadable files of Citi Bike trip data . The data includes:

Rideable type
Start station name
Start station ID
End station name
End station ID
Start latitude
Start longitude
End latitude
End Longitude
Member or casual ride

Data format previously:

Trip Duration (seconds)
Start Time and Date
Stop Time and Date
Start Station Name
End Station Name
Station Lat/Long
User Type (Customer = 24-hour pass or 3-day pass user; Subscriber = Annual Member)
Gender (Zero=unknown; 1=male; 2=female)
Year of Birth

This data has been processed to remove trips that are taken by staff as they service and inspect the system, trips that are taken to/from any of our “test” stations (which we were using more in June and July 2013), and any trips that were below 60 seconds in length (potentially false starts or users trying to re-dock a bike to ensure it's secure).

Please be aware of your software program’s row limitations as you are viewing the data. Many of the CSV files contain more than 1 million rows. After downloading, you will need to use a large data tool / visualizer (like Tableau, Alteryx, R, or others) to view and analyze the full data sets.

Download Citi Bike trip history data

Real-Time Data

Citi Bike publishes real-time system data in General Bikeshare Feed Specification format. Get the GBFS feed here .

Monthly Operating Reports

View the monthly operating reports that we provide to the NYC Department of Transportation.

Additional Resources

The City of New York's bicycling data
A group of software developers and data explorers working with data feeds from NYC's Bike Share system and other bike data maintain this Google Group (note: Citi Bike is not responsible for this group – it is run and maintained by a group of interested private citizens)

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

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week8-lab3.sql

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Table of Contents
1 Tidy Data
2 Computing with R
3 Basic R Commands
4 Files and documents
5 Introduction to Data Graphics
6 Frames, glyphs, and other components of graphics
7 Data Wrangling
8 Graphics and Their Grammar
9 Collaboration and Reproducibility with Git
10 More Data Verbs
11 Joining two data frames
12 Wide versus Narrow Data
14 Graphing Networks
15 Statistics
16 Data Scraping and Intake Methods
17 Using Regular Expressions
18 Machine Learning
Project: Popular names

Project: Bicycle Sharing

Project: Bird species
Project: Statistics of Gene Expression
Project: Scraping Nuclear Reactors
Project: Street or Road?
Project: Joining the urban population
Appendix: R Programming Style Guide
Appendix: GitHub-RStudio Configuration

Capital BikeShare is a bicycle-sharing system in Washington, D.C. At any of about 400 stations, a registered user can unlock and check out a bicycle. After use, the bike can be returned to the same station or any of the other stations.

Such sharing systems require careful management. There need to be enough bikes at each station to satisfy the demand, and enough empty docks at the destination station so that the bikes can be returned. At each station, bikes are checked out and are returned over the course of the day. An imbalance between bikes checked out and bikes returned calls for the system administration to truck bikes from one station to another. This is expensive.

In order to manage the system, and to support a smart-phone app so that users can find out when bikes are available, Capital BikeShare collects real-time data on when and where each bike is checked out or returned, how many bikes and empty docks there are at each station. Capital BikeShare publishes the station-level information in real time. The organization also publishes, at the end of each quarter of the year, the historical record of each bike rental in that time.

You can access the data from the Capital Bikeshare web site. Doing this requires some translation and cleaning, skills that are introduced in Chapter 16 . For this project, however, already translated and cleaned data are provided in the form of two data tables:

Stations giving information about location of each of the stations in the system.
Trips giving the rental history over the last quarter of 2014 (Q4).
The Trips data table is a random subset of 10,000 trips from the full quarterly data.
Start with this small data table to develop your analysis commands.
When you have your entire analysis working well for the Small data, you can access the full data set of more than 600,000 events by removing -Small from the name of the trip_url

In this activity, you’ll work with just a few variables:

From Stations :

the latitude ( lat ) and longitude ( long ) of the bicycle rental station
name : the station’s name

From Trips : [Click to see note.] Notice that the location and time variables start with an “s” or an “e” to indicate whether the variable is about the start of a trip or the end of a trip.

sstation : the name of the station where the bicycle was checked out.
estation : the name of the station to which the bicycle was returned.
client : indicates whether the customer is a "regular" user who has paid a yearly membership fee, or a "casual" user who has paid a fee for five-day membership.
sdate : the time and date of check-out
edate : the time and date of return

Time/dates are typically stored as a special kind of number: a POSIX date . [Click to see note.] POSIX date: A representation of date and time of day that facilitates using dates in the same way as numbers, e.g. to find the time elapsed between two dates. You can use sdate and edate in the same way that you would use a number. For instance, Figure 18.1 shows the distribution of times that bikes were checked out.

Figure 18.1: Use of shared bicycles over the three months in Q4.

18.6 How long?

Your Turn : Make a box-and-whisker plot, like Figure 18.2 showing the distribution of the duration of rental events, broken down by the client type. The duration of the rental can be calculated as:

The units will be in either hours, minutes, or seconds. It should not be much trouble for you to figure out which one.

Figure 18.2: The distribution of bike-rental durations as a box-and-whisker plot.

When you make your plot, you will likely find that the axis range is being set by a few outliers. These may be bikes that were forgotten. Arrange your scale to ignore these outliers, or filter them out.

18.7 When are bikes used?

The sdate variable in Trips indicates the date and time of day that the bicycle was checked out of the parking station. sdate is stored as a special

Often, you will want discrete components of a date, for instance:

Your Turn : Make histograms or density plots of each of these discrete components. Explain what each plot is showing about how bikes are checked out. For example, Figure 18.3 shows that few bikes are checked out before 5am, and that there are busy times around the rush hour: 8am and 5pm.

Figure 18.3: Distribution of bike trips by hour of the day.

A similar sort of display of events per hour can be accomplished by calculating and displaying each hour’s count, as in Figure 18.4 .

Figure 18.4: The number of events in each hour of the day. Compare to the scale in Figure 18.3 . Why are the scales different?

The graphic shows a lot of variation of bike use over the course of the day. Now consider two additional variables: the day of the week and the “client” type.

Your Turn : Group the bike rentals by three variables: hour of the day, day of the week, and client type. Find the total number of events in each grouping and plot this count versus hour. Use the group aesthetic to represent one of the other variables and faceting to represent the other. [Click to see note.] Hint: utilize facet_wrap() in the plotting commands.

Your Turn : Make the same sort of display of how bike rental vary of hour, day of the week, and client type, but use geom_density() rather than grouping and counting. Compare the two displays — one of discrete counts and one of density — and describe any major differences.

18.8 How far?

Find the distance between each pair of stations. You know the position from the lat and long variables in Stations . This is enough information to find the distance. The calculation has been implemented in the haversine() function:

haversine() is a transformation function. To use it, create a data table where a case is a pair of stations and there are variables for the latitude and longitude of the starting station and the ending station. To do this, join the Station data to itself. The following statements show how to create appropriately named variables for joining.

Look at the head() of Simple and Simple2 and make sure you understand how they are related to Stations .

The joining of Simple and Simple2 should match every station to every other station. [Click to see note.] Since a ride can start and end at the same station, it also makes sense to match each station to itself. This sort of matching does not make use of any matching variables; everything is matched to everything else. This is called a full outer join . [Click to see note.] A full outer join matches every case in the left table to each and every case in the right table.

First, try the full outer join of just a few cases from each table, for example 4 from the left and 3 from the right.

Make sure you understand what the full outer join does before proceeding. For instance, you should be able to predict how many cases the output will have when the left input has \(n\) cases and the right has \(m\) cases.

There are 347 cases in the Stations data table. How many cases will there be in a full outer join of Simple to Simple2 ?

It’s often impractical to carry out a full outer join. For example, joining BabyNames to itself with a full outer join will generate a result with more than three-trillion cases. [Click to see note.] Three trillion cases from the BabyNames data is the equivalent of about 5 million hours of MP3 compressed music. A typical human lifespan is about 0.6 million hours.

Perform the full outer join and then use haversine() to compute the distance between each pair of stations.

Check your result for sensibility. Make a histogram of the station-to-station distances and explain where it looks like what you would expect. ( Hint: you could use the Internet to look up the distance from one end of Washington, D.C. to the other. )

Once you have PairDistances , you can join it with Trips to calculate the start-to-end distance of each trip. [Click to see note.] Of course, a rider may not go directly from one station to another.

Look at the variables in Stations and Trips and explain why Simple and Simple2 were given different variable names for the station.

An inner_join() is appropriate for finding the distance of each ride. [Click to see note.] Watch out! the Trips data and the PairDistances data are large enough that the join is expensive: it takes about a minute.

Your turn : Display the distribution of the ride distances of the rides. Compare it to the distances between pairs of stations. Are they similar? Why or why not?

Figure 18.5: The distribution of trip lengths compared to the distribution of distances between pairs of stations (shaded).

18.9 Mapping the Stations

You can draw detailed maps with the leaflet package. You may need to install it.

leaflet works much like ggplot() but provides special facilities for maps. Here’s how to make the simple map:

To display the map, use the object name as a command: stationMap

Figure 18.6: A map of station locations drawn with the leaflet() function in the leaflet package.

18.10 Long-distance stations

Your Turn (more challenging, but cool): Around each station on the map, draw a circle whose radius reflects the median distance covered by rentals starting at that station. To draw the circles, use the same leaflet commands as before, but add in a line like this.

For addCircles() to draw circles at the right scale, the units of the median distance should be presented in meters rather than kilometers. This will create too much overlap, unfortunately. So, set the radius to be half or one-third the median distance in meters. From your map, explain the pattern you see in the relationship between station location and median distance.

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The City of Philadelphia and its partners at Bicycle Transit Systems are pleased to share anonymized Indego trip data with the public. The privacy of Indego passholders and users is very important to us, and information won’t be released unless the City can ensure that the identities of individuals or groups cannot be discerned and that their privacy is not compromised.

Toss it, flip it, turn it inside out, combine it with your own or someone else’s data! Tell us how many trips happened in the rain, or after midnight, or below 50 degrees – we want your help to delve into the data and reveal the hidden gems of understanding that will allow us to make Indego the best it can be.

2024 Q2 (April – June)
2024 Q1 (January – March)
2023 Q4 (October – December)
2023 Q3 (July – September)
2023 Q2 (April – June)
2023 Q1 (January – March)
2022 Q4 (October – December)
2022 Q3 (July – September)
2022 Q2 (April – June)
2022 Q1 (January – March)
2021 Q4 (October – December)
2021 Q3 (July – September)
2021 Q2 (April – June)
2021 Q1 (January – March)
2020 Q4 (October – December)
2020 Q3 (July – September)
2020 Q2 (April – June)
2020 Q1 (January – March)
2019 Q4 (October – December)
2019 Q3 (July – September)
2019 Q2 (April – June)
2019 Q1 (January – March)
2018 Q4 (October – December)
2018 Q3 (July – September)
2018 Q2 (April – June)
2018 Q1 (January – March)
2017 Q4 (October – December)
2017 Q3 (July – September)
2017 Q2 (April – June)
2017 Q1 (January – March)
2016 Q4 (October – December)
2016 Q3 (July – September)
2016 Q2 (April – June)
2016 Q1 (January – March)
2015 Q4 (October – December)
2015 Q3 (July – September)
2015 Q2 (Launch, April – June)

Data Format

Each .csv file contains data for one quarter of the year. Each file contains the following data points:

trip_id: Locally unique integer that identifies the trip
duration: Length of trip in minutes
start_time: The date/time when the trip began, presented in ISO 8601 format in local time
end_time: The date/time when the trip ended, presented in ISO 8601 format in local time
start_station: The station ID where the trip originated (for station name and more information on each station see the Station Table )
start_lat: The latitude of the station where the trip originated
start_lon: The longitude of the station where the trip originated
end_station: The station ID where the trip terminated (for station name and more information on each station see the Station Table )
end_lat: The latitude of the station where the trip terminated
end_lon: The longitude of the station where the trip terminated
bike_id: Locally unique integer that identifies the bike
plan_duration: The number of days that the plan the passholder is using entitles them to ride; 0 is used for a single ride plan (Walk-up)
trip_route_category: “Round Trip” for trips starting and ending at the same station or “One Way” for all other trips
passholder_type: The name of the passholder’s plan
bike_type: The kind of bike used on the trip, including standard pedal-powered bikes or electric assist bikes

Data Processing

Data will be cleansed prior to publication according to the following criteria:

Staff servicing and test trips are removed.
Trips below 1 minute are removed.
A “Virtual Station” listed in the checkout and return kiosks, is used by staff to check in or check out a bike remotely for a special event or in a situation in which a bike could not otherwise be checked in or out to a station.
Trip lengths are capped at 24 hours.
Some short round trips or long trips may be the result of system or user error, but have been kept in the dataset for completeness.

Station Information

Station Table (Updated 2024-07-01)
Station ID: Unique integer that identifies the station (this is the same ID used in the Trips and Station Status data)
Station Name: The public name of the station. “Virtual Station” is used by staff to check in or check out a bike remotely for a special event or in a situation in which a bike could not otherwise be checked in or out to a station.
Go live date: The date that the station was first available
Status: “Active” for stations available or “Inactive” for stations that are not available as of the latest update

Station Status

Live station location and status information from Indego is available in the following formats:

Better Bike Share
Explore Philly
Partnerships
Passholder of the Month
Special Offers
Staff Profile
Station Updates

at what station did the bike trip with rental id

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