Uber's Market Strategy - An example of modern day business modelsRahul Shaha
This is a presentation on Uber's two-sided market strategy. Tools suchas the Porter's 5 Forces, Business Model Canvas and PESTEL analysis have been used.
Uber's Market Strategy - An example of modern day business modelsRahul Shaha
This is a presentation on Uber's two-sided market strategy. Tools suchas the Porter's 5 Forces, Business Model Canvas and PESTEL analysis have been used.
Burke: Learning and Growing through Marketing ResearchAsif Mahmood Abbas
Burke is a century-old market research firm that uses a reliable research process and cutting edge technology to help its clients.
Burke works with clients to help them identify what information is needed to make a decision they are facing.
With a series of creative methods, Burke makes sure that what the client thinks the problem is, is really the problem.
UBER-Current Strategy, Competition Analysis and Global ExpansionShaminder Saini
UBER Worldwide, Business Proposition, Funding Mechanism, Taxi Industry Impact, Porter's Five Forces, PESTEL Analysis, BCG Matrix, SWOT, Levels of Service, Customer Engagement, Value Proposition, Disruptive Strategies, Global Expansion
As a pioneer within the radio taxi service in India, Meru Cabs once again launched Carpool by Meru, an initiative to make people travel together and give back to others around them.
The Digital Marketing campaign, #DilKaDarwazaKholo, revolved around the sentiment of giving back to people by offering them a ride whilst also enjoying the company of others.
Digital marketing solutions right from social media campaigns to emailers, SMS pushes and banner ads created an outreach providing Meru Cabs with 3x the amount of downloads for its app, whilst successfully creating a MindShift amongst the Indian audiences towards sharing a car versus riding alone.
Where Do I Start? New Tools to Prioritize Investments in Bicycle and Pedestrian Infrastructure
Track: Connect
Format: 90 minute panel
Abstract: Most communities have a laundry list of important bike/ped projects. This session will help you understand where to start and provide you with an objective and transparent process to shortlist priority projects. Learn from three expert practitioners and get your program going!
Burke: Learning and Growing through Marketing ResearchAsif Mahmood Abbas
Burke is a century-old market research firm that uses a reliable research process and cutting edge technology to help its clients.
Burke works with clients to help them identify what information is needed to make a decision they are facing.
With a series of creative methods, Burke makes sure that what the client thinks the problem is, is really the problem.
UBER-Current Strategy, Competition Analysis and Global ExpansionShaminder Saini
UBER Worldwide, Business Proposition, Funding Mechanism, Taxi Industry Impact, Porter's Five Forces, PESTEL Analysis, BCG Matrix, SWOT, Levels of Service, Customer Engagement, Value Proposition, Disruptive Strategies, Global Expansion
As a pioneer within the radio taxi service in India, Meru Cabs once again launched Carpool by Meru, an initiative to make people travel together and give back to others around them.
The Digital Marketing campaign, #DilKaDarwazaKholo, revolved around the sentiment of giving back to people by offering them a ride whilst also enjoying the company of others.
Digital marketing solutions right from social media campaigns to emailers, SMS pushes and banner ads created an outreach providing Meru Cabs with 3x the amount of downloads for its app, whilst successfully creating a MindShift amongst the Indian audiences towards sharing a car versus riding alone.
Where Do I Start? New Tools to Prioritize Investments in Bicycle and Pedestrian Infrastructure
Track: Connect
Format: 90 minute panel
Abstract: Most communities have a laundry list of important bike/ped projects. This session will help you understand where to start and provide you with an objective and transparent process to shortlist priority projects. Learn from three expert practitioners and get your program going!
Title: Taking Pedestrian and Bicycle Counting Programs to the Next Level
Track: Connect
Format: 90 minute panel
Abstract: Panelists will provide practical guidance for pedestrian and bicycle counting programs based on findings from NCHRP Project 07-19, "Methods and Technologies for Collecting Pedestrian and Bicycle Volume Data."
Presenters:
Presenter: Robert Schneider University of Wisconsin-Milwaukee
Co-Presenter: RJ Eldridge Toole Design Group, LLC
Co-Presenter: Conor Semler Kittelson & Associates, Inc.
Title: New Tools for Estimating Walking and Bicycling Demand
Track: Sustain
Format: 90 minute panel
Abstract: Walking and bicycling demand estimates can make a stronger case for investing in new facilities and are necessary inputs to important planning tasks. This session presents state-of-the-art tools to predict walking and bicycling demand at varying geographic scales. Tools include: 1) a framework to incorporate walking into regional travel demand models; 2) a method to estimate bicycle and pedestrian traffic based on count data; 3) new mode choice models; and 4) a web-based repository of non-motorized demand analysis tools.
Presenters:
Presenter: Patrick Singleton Portland State University
Co-Presenter: J. Richard (Rich) Kuzmyak Renaissance Planning Group
Co-Presenter: Greg Lindsey University of Minnesota, Humphrey School
Co-Presenter: Jeremy Raw Federal Highway Administration
Improving the quality and cost effectiveness of multimodal travel behavior da...Sean Barbeau
Multimodal transportation such as transit, bike, walk, transportation network companies (TNCs) (e.g., Uber, Lyft), car share, and bike share are vital to supporting livable communities. However, current data collection techniques for multimodal travel behavior, including apps built specifically for travel behavior surveys, have limitations (e.g., significant negative impact on battery life, user acquisition) which prevent a better understanding of significant real-world challenges (e.g., multimodal traveler choices, relationships between travel behavior and health).
This webinar discusses the results of a recently completed research project funded by the National Center for Transit Research, “Improving the Quality and Cost Effectiveness of Multimodal Travel Behavior Data Collection”. In this project, the research team developed and deployed a proof-of-concept system to collect multimodal travel behavior data on an ongoing basis directly from users of a popular open-source mobile app for multi-modal information, OneBusAway (OBA). To overcome battery life challenges, the research team used the Android Activity Transition API, which leverages hardware advancements in modern mobile phones.
This webinar presents the technology used to implement this data collection tool, as well as the results of a pilot deployment to 676 beta testing users. Over 10 weeks, 74 users opted into the study without any incentive and contributed 65,582 trips. Key concerns discussed for data collection when conserving battery life include the timeliness and accuracy of data.
A webinar recording of this presentation can be found here:
https://www.cutr.usf.edu/2020/04/cutr-webinar-improving-the-quality-and-cost-effectiveness-of-multimodal/
The final report for this project can be downloaded at:
https://scholarcommons.usf.edu/cutr_nctr/13/
Equity in Bike Share: Practical Methods for Addressing Equity and Measuring Outcomes
Bike share systems across the country have experienced enormous success in ridership and popularity, but riders are not always representative of the local population. This panel focuses on how to design, administer, communicate about, and evaluate programs to reach people most in need of this healthy, affordable travel option.
Presenters:
Presenter: Morgan Whitcomb Sam Schwartz Engineering
Co-Presenter: Melissa Ballate Blue Daring
Co-Presenter: Andrew Duvall University of Colorado Denver
Co-Presenter: Nicole Freedman City of Boston
Boosting Active Transportation at the Regional Level: Setting and Meeting Performance Measures
How can Metropolitan Planning Organizations increase and best utilize support for active transportation? Learn about approaches from MPOs in Chattanooga and Atlanta in effectively engaging the public and other agencies, setting performance measures, and prioritizing active transportation projects.
Presenters:
Presenter: Jenny Park Chattanooga Regional Planning Agency
Co-Presenter: Byron Rushing Atlanta Regional Commission
Open data in the General Transit Feed Specification (GTFS) format has led to many innovations in the transit industry. One of these innovations has been the emergence of open-source software projects that utilize open transit data and offer various multi-modal traveler information services. OneBusAway (http://onebusaway.org/) started as a student project at the University of Washington, and now offers real-time transit arrival information riders at more than 10 cities around the world. OpenTripPlanner (http://www.opentripplanner.org/) started as a project in TriMet, OR and has been used for the basis of many other trip planning applications world-wide, including the university campus-centric USF Maps App (http://maps.usf.edu/). This presentation will discuss the evolution and benefits of the OneBusAway and USF Maps App, including the ability for anyone to deploy these projects in new locations.
O Centro de Excelência em BRT Across Latitudes and Cultures (ALC-BRT CoE) promoveu o Bus Rapid Transit (BRT) Workshop: Experiences and Challenges (Workshop BRT: Experiências e Desafios) dia 12/07/2013, no Rio de Janeiro. O curso foi organizado pela EMBARQ Brasil, com patrocínio da Fetranspor e da VREF (Volvo Research and Education Foundations).
With collaborations with various City divisions and private service providers (in this case Streetlight data providers), our North York mobility innovation team uncovered several surprising suburban travel behaviour, patterns and distributions of trips that lead to meaningful and quantitative multimodal mobility planning. This presentation is a summary of project experiences and describes the key findings.
Multimodal Mopbility Planning Using Big Data in Toronto
Capital Bikeshare Presentation
1. Capital Bikeshare
MAY 2, 2015
GEORGETOWN UNIVERSITY
SCHOOL OF CONTINUING STUDIES
CERTIFICATE IN DATA ANALYTICS
CAPSTONE PROJECT
SELMA ORR
RYAN DONAHUE
ODETTE RIVERA
NORA GOEBELBECKER
KARINA HIDALGO
2. Problem Statement
Cities want to build bike systems for economic development and sustainability, but they face
serious fiscal constraints.
3. Problem Statement
As a result, the public has little tolerance for error – even though usage, and therefore
profitability, is highly variable.
Most popular bikeshare station, 2014:
Union Station – 131,700 trips
Least popular bikeshare station, 2014:
34th St & Minnesota Ave SE – 112 trips
For roughly the same fixed costs, there are more than
1,000 times as may riders each year using the Union
Station location as many others.
4. Currently, there is no standardized, rigorous methodology for accurately predicting which
stations will be most heavily used.
Problem Statement
5. Goal
Develop a model, based on Washington DC but applicable to other
U.S. cities, that will predict the popularity of bikeshare stations based
on characteristics of the area surrounding each station.
Such a model could be used to increase the popularity of new and
existing bikeshare systems, making them more financially
sustainable.
6. Hypotheses
• Bike share station popularity is influenced by station location– specifically, the
economic, demographic, and geographic characteristics of surrounding
neighborhoods.
• Certain determinants of bikeshare station popularity hold true across cities,
allowing for the construction of a model that could accurately predict the
popularity of bikeshare stations in other cities.
•Regression models, such as linear regression, are a good fit to predict station
popularity.
7. Project Background
This project builds primarily upon two previous analyses of Washington, DC:
1. Maximizing Bicycle Sharing: An Empirical Analysis of Capital Bikeshare Usage
• Multivariate regression to identify five factors that influenced bikeshare station popularity:
population 20-39, non-white population, retail proximity, Metro proximity, and distance from
system center.
2. Predicting the Popularity of Bicycle Sharing Stations: An Accessibility-Based Approach Using
Linear Regression and Random Forests
• Linear regression and random forest analysis to understand how job and residential proximity
influenced station popularity. Although the study attempted to extend the model to San Francisco
and Minneapolis, it found that the model was a poor predictor of station popularity in those cities.
Why revisit this topic?
• Our team identified other characteristics that might drive usage.
• The bikeshare system has expanded considerably since those studies, offering a larger and more
varied sample.
8. Data Sources and Ingestion
Data Variable Type Source Year Geography Format
Bikeshare trips Dependent Capital Bikeshare 2014 N/A CSV
Bikeshare stations Dependent DC Open Data 2014 Point CSV/shapefile
Population- age Independent U.S. Census/ACS 2013 Block Group CSV
Population- race Independent U.S. Census /ACS 2013 Block Group CSV
DC liquor license locations Independent DC Open Data 2014 Point CSV/shapefile
DC Metro stations (train and
bus)
Independent WMATA 2014 Point CSV/shapefile
Parks (DC, National) Independent DC Open Data 2014 Polygon CSV/shapefile
Campuses (college, university) Independent DC Open Data 2014 Polygon CSV/shapefile
Historic landmarks Independent DC Open Data 2014 Polygon CSV/shapefile
Starbucks and McDonald’s
locations
Independent Various online
sources
2014 Point CSV
9. Data Sources and Ingestion
Data Variable Type Source Year Geography Format
Total trips per bike per year Dependent Capital Bikeshare 2014 N/A CSV
Bikeshare stations Dependent DC Open Data 2014 Point CSV/shapefile
Population- age Independent U.S. Census/ACS 2013 Block Group CSV
Population- race Independent U.S. Census /ACS 2013 Block Group CSV
DC liquor license locations Independent DC Open Data 2014 Point CSV/shapefile
DC Metro stations (train and
bus)
Independent WMATA 2014 Point CSV/shapefile
Parks (DC, National) Independent DC Open Data 2014 Polygon CSV/shapefile
Campuses (college, university) Independent DC Open Data 2014 Polygon CSV/shapefile
Historic landmarks Independent DC Open Data 2014 Polygon CSV/shapefile
Starbucks and McDonald’s
locations
Independent Various online
sources
2014 Point CSV
Data Variable Type Format
Amenities within walking
distance from bikeshare
stations (sum)
Independent CSV
Distance from bikeshare
station to each type of
amenity (closest amenity
within walking distance of
station-- -.5 miles or less)
Independent CSV
Socioeconomic characteristics
of the population sharing the
same census block group as
the bikeshare station
Independent CSV/shapefile
10. Capital Bikeshare Data
• Data attributes for bike trip: Trip Duration, Start/End Station (address), Start/End Date Time,
Bike Number, and Member Type
• Divided by time period: A year of trip data downloaded from the Capital Bikeshare website was
divided in four files with each file representing a quarter.
•Separate dataset with coordinates of bikeshare station’s locations was obtained from DC Open
Data website
• How to measure popularity? Trips leaving, trips arriving, total trips? Different capacity at each
station, so popularity = total trips (arrive + depart)/bike/year
11. Census Data
• Socioeconomic and demographic data collected for all Census blocks within DC, in the form
of CSV files downloaded from American Factfinder
• Census blocks are the smallest geographic area for which sample data is collected (typically
600 to 3,000 residents)
• Challenge 1: how to link stations (discrete points) with block groups (boundaries)?
oSolution: ArcGIS was able to identify block groups by lat/long of bikeshare station
• Challenge 2: how to deal with missing data?
oFor missing rent (4 instances): impute by calculating average rent/income ratio across city
oFor missing income (2 instances): impute by averaging two adjacent block groups
oFor missing population (10 instances, national park areas): leave blank
12. Nearby Amenity Data
• Selection of amenities based largely on past studies and common-sense drivers
of bikeshare usage: metro and bus stations, college campuses, DC and national
parks, entertainment, restaurants, bars (proxied by liquor licenses)
• Two ways to think about importance of amenities as drivers of usage:
o How close the single closest location of each type of amenity is (likely most
important for metro stations)
o How many locations of each type of amenity there are within a half mile
(likely most important for restaurants, bars)
• One challenge: whenever there wasn’t a single location of an amenity within a
half mile (common result for metro stations), ArcGIS identified distance as “0”
13. Data Wrangling
The primary challenge in the data
wrangling process was to create an
architecture that links each individual
station with its census block group
(and associated socioeconomic
characteristics) as well as with
distances to surrounding physical
amenities.
Census/Bike
Station.csv
Bike
Station
Address
Census
Block
Group
Amenity
Proximity
17. Data Wrangling
All four files with quarterly
ridership data were loaded into a
PostgreSQL table to be merged to
the Census/Bike station.csv file to
add the stationID, latitude and
longitude of the stations.
The resulting file contained all the
trips in 2014 with reference
information about the station and
created a common field uniquely
linking the records between the
files.
BikeStation
_Census.csv
DCBikeTrips2014.csv Census_BikeTrip.csv
18. Data Exploration
Correlated dependent variables with one another to anticipate collinearity. Shown here are
metro station proximity vs. density, and bar proximity vs. single households.
19. Data Exploration
A few variables that appear to have little correlation to station popularity: metro station
proximity and % of households in Census block that commute by public transit.
20. Data Exploration
A few variables that seem to have correlation to station popularity: % of population in Census
block that drives to work, and % of population in Census block with a college degree.
22. 2014 Capital Bikeshare Member Survey
“Compared with all commuters in the region, they were, on average, considerably younger,
more likely to be male, Caucasian, and slightly less affluent.”
“Two-thirds (64%) of respondents said that at least one of the Capital Bikeshare trips they made
last month either started or ended at a Metrorail station and 21% had used bikeshare six or
more times for this purpose. About a quarter (24%) of respondents used Capital Bikeshare to
access a bus in the past month.”
24. Study Methodology: Feature Selection or
better said “Feature Wrangling”
Step 1 : Ran a full Ordinary Least Squares Regression with all thirty independent variables using StatsModels in Python.
◦ R-squared: 0.636
◦ Adjusted R-squared: 0.577
◦ Eight variables with significant p-values included: DRIVE, WHITE, DENSITY, AGE, WALK, BUS_N, TRANSIT, MCDON_N
◦ As expected, very large conditions number, 1.11e+06 indicating strong multicollinearity
◦ F-statistic: 10.73 and Prob(F-statistic): 3.42e-25
◦ Designated this as Model 2
Step 2 : Beginning with the variable DRIVE, the variable with the highest linear correlation to y, sequentially added the
other variables to the OLS regression according to correlation
◦ Any variable that triggered a multicollinearity warning was left out
◦ Any variable without a significant p-value was left out
◦ Seven variables with significant p-values included: DRIVE, WHITE, LIQUOR_N, BUS_N, MCDON_N, SINGLE, CAMPUS_N
◦ Designated this as Model 1
◦ Note: Experimented quite a bit with k features module but this adds features sequentially using descending correlations, but does not
take account of multicollinearity
25. Definitions of relevant features
Census:
DRIVE: share of population in Census block that drives to work (Model 1 and Model 2)
WALK: share of population in Census block that walks to work (Model 2)
TRANSIT: share of population in Census block that takes transit to work (Model 2)
WHITE: white share of population in Census block (Model 1 and Model 2)
DENSITY: population density in Census block (Model 2)
AGE: median age in Census block (Model 2)
SINGLE: share of households in block group that are single (i.e., non-family) (Model 1)
Amenities:
BUS_N: number of bus stations within half mile (Model 1 and Model 2)
MCDON_N: number of McDonald’s within half mile (Model 1 and Model 2)
LIQUOR_N: distance to nearest establishment with a liquor license (Model 1 and Model 2)
CAMPUS_N: number of college campuses within half a mile (Model 1)
30. Study Methodology: Machine Learning
Step 1 : Selected the following regression types for Machine Learning on Model 1 and Model 2
◦ OLS
◦ Ridge
◦ RidgeCV
◦ Lasso
◦ LassoCV
◦ Decision Tree
◦ Random Forest
Step 2 : Prepared the data
◦ Because there were only 201 stations (rows of data) opted against the K-fold cross-validation
◦ Used Repeated Random sub-sampling validation with 20% splits for testing and 80% splits for training
◦ Iterated for each regression type for n=15 times and averaged the results for the 15 trials
36. Data Product
Groundwork: By analyzing the correlation between the factors such as bikeshare’s stations location, geographic and
demographic information, we obtained results that allow us to create a data product that predicts the likelihood of
success or failure of a new Bikeshare station prior to implementation in the DC area
Results: Our models succeed in explaining about half of the variance in the Bikeshare Station popularity as measured by
our utilization factor. This should at least help in predicting the potential popularity of a station based on the
combination of demographic and geographic factors we identified as significant.
Further applications: In addition to identifying promising locations for new Bikeshare stations in DC, the results may also
generalize to other cities. By using data on the demographic and geographic factors we identified as significant, it could
allow a user to predict promising locations for bike stations during an initial roll-out ,thus enhancing the overall success
of a new project without costly experimentation
37. What worked?
GIS was critical to creating effective architecture: linked stations to amenities by distance, and
to Census blocks and associated data.
Since the data volume that we handled was small, using local machine rather than a powerful
data base or cloud environment helped to achieve faster results.
Spending time exploring the variables before beginning analysis. This, plus domain knowledge,
allowed team to identify and address data issues manually that the software didn't calculate
accurately from the beginning.
Trying many different types of regressions using different variables (including different forms of
independent variable – log, natural log).
38. What didn’t work? And lessons learned.
Data sample was relatively small – started at roughly 350 stations, but shrank to roughly 200
once MD and VA locations were removed from the analysis.
Data and feature wrangling take a long time (80% of the process); domain expertise makes this
easier.
Would have been harder to detect and address anomalies and missing values in data had the
sample been larger; familiarity with DC allowed us to understand why data missing for national
parks or institutional land uses.
Couldn’t do k-fold cross validation given small sample size.
Decision tree model didn’t work for our analysis.
39. Conclusion
Model offers a good starting point for assessing likely popularity of station locations, using data that is readily
available for most major U.S. cities. Some subjective decision-making will still be required around major parks
(i.e., National Mall) or institutional land uses (campuses, hospitals).
Bikeshare continues to gain momentum across the country. Future studies should:
• Use a larger sample
• Idea: use DC as model, test against NYC and Chicago. Instead: use DC, NYC, and Chicago to build model with larger
sample, test against fourth city.
• Categorize stations by function within network
• Stations have different functions: residential feeders to metro stations, tourism. With a larger sample, these station
types could be separated and the drivers of popularity independently determined.
Important to note that there are valid reasons other than current popularity that should determine station
placement (i.e., equity, driving changes in travel behavior). This model helps ensure financial viability so that
these outcomes can be pursued.
Editor's Notes
The goals of this project are:
1. Develop a general model (using Washington DC as a test dataset) that will predict the popularity of bike share stations based on characteristics in proximate areas, in order to help cities deploy optimal station networks without expensive experimentation.
The goals of this project are:
1. Develop a general model (using Washington DC as a test dataset) that will predict the popularity of bike share stations based on characteristics in proximate areas, in order to help cities deploy optimal station networks without expensive experimentation.
The goals of this project are:
1. Develop a general model (using Washington DC as a test dataset) that will predict the popularity of bike share stations based on characteristics in proximate areas, in order to help cities deploy optimal station networks without expensive experimentation.
The goals of this project are:
1. Develop a general model (using Washington DC as a test dataset) that will predict the popularity of bike share stations based on characteristics in proximate areas, in order to help cities deploy optimal station networks without expensive experimentation.
This project builds primarily upon two previous analyses. This study, based on Washington DC, used multivariate regression to identify five factors that influenced bikeshare station popularity, including population 20-39, non-white population, retail proximity, Metro proximity, and distance from system center. This study, also based on Washington DC, used linear regression and random forest analysis to understand how job and residential proximity influenced bikeshare station popularity. It then sought to extend the model to San Francisco and Minneapolis, but found that the model was a poor predictor of station popularity in those cities.
(There’s a transition effect on this slide. Click once and this refined dataset table will slide in.)
We joined these separate datasets
The primary challenge is to create an architecture that links each individual station with its block group (and associated socioeconomic characteristics) as well as with distances to surrounding physical amenities. The complexity is a result of the fact that while stations locations are recorded as coordinates, block groups are boundaries that cover an array of coordinates, and physical amenities are variously recorded as addresses, intersections, and/or coordinates.