Term project for TR-GY 7073 Travel Behavioral Informatics, NYU

1. MINA LEE
Forecasting the rental demand in E-scooter
sharing system in New York

2. Background
Literature Review
Data
Objective
Methodology
Model Estimation
Conclusion
Future Work
Contents

3. Background
• Large electric scooter sharing companies like Lime and Bird have
been valued at over $1 billion USD in addition to receiving hundreds
of millions in funding from venture capital.
• Electric kick-scooters are energy-efficient, environmentally friendly.
• All of the companies charge the same prices in the US: $1 to rent an
electric scooter and $0.15 per minute afterwards.
• In light of current trends, growing demand has led many cities to
now consider legalizing the e-scooter sharing system.
Podgayetsky, Yev. (2018,08). What’s the future of electric scooters? 18 point roadmap.
Dickey, Megan Rose.(2018.12) The electric scooter wars of 2018

4. Literature Review
Bike Sharing Systems
• Demand Forecasting
- Wang, Bo., and Kim, Inhi. (2018). Short-term prediction for bike-sharing service using machine learning
- Singhvi, Divya., Singhvi, Somya., Peter I. Frazier., Shane G., Henderson et al.(2015). Predicting Bike Usage for New
York City’s Bike Sharing System
- Campbell, Andrew A. et al. (2015). Factors influencing the choice of shared bicycles and shared electric bikes in Beijing
• Rebalancing
- Liu, Jungmin. et al.(2016). Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization
E-scooter Sharing Systems
• Customer Segmentation(German)
- Degele , Jutta et al. (2018). Identifying E-Scooter sharing customer segments using clustering.
Although the e-scooter sharing system has become increasingly
popular in metropolitan cities, there has been limited research on its
potential impact and overall effect on existing modes of transportation.
 Since 2013, there has been an influx of research on bike sharing systems that address key
issues, such as forecasting rental demand, policy implementation, and supply rebalancing.
These research areas have greatly influenced the success and emergence of the bike sharing
system in major metropolitan areas.

5. Data
Portland implemented a four month pilot program in order to test the
potential for e-scooters to reduce pollution and congestion
Portland Bureau of Transportation(2018). 2018 E-Scooter Pilot User Survey Results. Portland, U.S
Portland Bureau of Transportation(2018). E-scooter finding report. Portland, U.S
• Total Trips: 700,369
• Total Miles: 801,887.84
• Average trips per day: 5,885
• Average trip per length: 1.15 miles

6. • Predicting the E-scooter demand in NewYork
• Estimating the multinomial logit mode choice model
• Analyzing the impact to the existing transportation system in NY,
such as walking, biking, and public transit.
Research Objective
This research may offer critical insight and reveal implications for E-
scooter sharing systems in New York city.

7. Methodology
• Summarize the data of Portland Pilot Program
• Estimate the linear regression model of E-scooter ridership
using demographic data in Portland and NY (Manhattan Only)
• Estimate the discrete choice model using MNL
• Analyze the impact on current mode choice in NY

8. Data
• 2018 E-Scooter Findings Report
• E-Scooter Routes Traveled Interactive Map
• DHM Research E-Scooter Pilot Project Survey Report
• 2010/2011 Regional Household Travel Survey
• 2013-2017 American Community Survey 5-Year Estimates
• Citi Bike System Data

9. Results
Regression Model
• Portland demographic data
• Portland survey data
• (Log)Linear regression Model
• Correlation matrix

10. Results
MNL Model
• Six different modes(Carpool, Transit, Taxi, Bike, Walk, and E-scooter)
• Including the availability of smartphones.
Brian Y. He, et al. Impact of built environment policies on New York City shared mobility travel patterns

11. Conclusion
• The e-scooter ridership will constitute of 1% of total trips in New York.
• E-scooters have more of an impact on short trips; The trips inside
Manhattan were relatively short, with an average of 7 minutes biking
• The most profitable neighborhoods for e-scooter ridership in Manhattan
are near Tribeca and Soho, followed by Chelsea and Bloomingdale.
• The demand prediction model can advise where to target and how many e-
scooters will be needed.
[Fig2] E-scooter ridership and revenue per zip code[Fig1] Mode Choice after E-scooter [Fig3] Citi Bike ridership map

12. Future Works
• Social Impact
- Predict the change in consumer surplus after deploying the e-scooter
sharing system
- Estimate the effect on Citi Bike and FHV ridership; how many people are
drawn from Citi Bike and determine the characteristics of those riders
• Resource Allocation
- Optimize the re-balancing strategies with minimum operation cost

13. Thank you!
-The End-