SmartPark Engine - BIRD

external engagement

Strategic Design Proposal for Bird

Flock : Akanksha, Manasa, Rucha, Sheldon

05.08

Agenda Problem Space Our Solution Why It Works?

Potential Risks Future Scope

Hi, I am Sarah. I actively use ebike as a student living in New York City. It provides a lot of convenience but time and again I face situations like -

Not finding parking while I am going to University. Everyday I feel so much unpredictability as to how much time will I spend on finding the parking

And then there are days when I can’t find a bike when I am in hurry to reach

And then usually during month ends, I find them a bit too expensive

Getting too dark, hopefully I get a parking

But it is not just Sarah, there is David, Denise, Carlos and many more facing these problems on daily basis

In dense urban areas,

riders spend an average of 1–3 extra minutes per trip

just hunting for legal parking, especially when Geo-fenced zones or racks are too far apart.

38% of riders use e-scooters to save time—but delays from parking, vehicle unavailability, and Geo-fencing can add 5+ minutes per trip, or ~40% of total trip time.

Public perception also suffers: ~30% of residents in U.S. cities view scooters negatively, often due to clutter and reliability issues.

Essentially,

shared micro-mobility is a logistics business.

Problem Statement How might we create a reliable, intuitive parking system for Bird riders in dense cities—one that ensures availability, minimizes end-of-trip friction, and builds rider loyalty through a clear, rewardbased incentive model?

We envision Bird to be a brand that represents Reliability, Accessibility and Safety

Introducing

delivers tailored nudges to riders at key decision moments

SmartPark

engine helping systems rebalance, using human behaviour as infrastructure.

Hybrid docking

SmartPark

engine

infrastructure more efficient

Real Time Nudges rides more predictable

Loyalty System turns riders to advocates

One Size Doesn’t Fit All — So We Zoned the City

We segment the city into Zones A, B, and C based on density and use case.

Zone C (Low-

Zone B (Mid-

Zone A (Dense):

density):

density):

Low Infra Docks

Each zone has a different mix of low infra docking systems — from fully docked in busy areas to dockless-first in less dense zones.

Mostly Dockless

Hybrid (Docked +

Tailored the infrastructure to spatial realities.

Dockless)

Layering Behavior Into the System We designed a incentive-based reward ecosystem that nudges better parking, rebalancing, and compliance.

Behavior becomes the infrastructure.

no points

-3$

-0$

+10 points

Real time route suggestion if destination parking is full Parking Unpredictability Go Dock-less in zone B/C from 10 pm- 5am Safety

Go Dock-less in zone B/C Convenience Park at an undersupplied dock Redistribution

Layering Behavior Into the System Real-Time Nudges That Shift Behavior, We turn uncertainty into opportunity — rerouting riders in real-time and rewarding behavior that supports the system.

Heads-up: Your destination dock is full.

Rerouting you to the nearest open spot.

It is 4 mins walk to the destination.

Accept New Route

Park at 34th street dock and earn 1$.

Layering Behavior Into the System These micro-moments rewire rider behavior.

SmartPark doesn’t just inform — it motivates, guides, and rewards in real time. That’s how we turn passive riders into active system collaborators.

Heads-up: Your destination dock is full.

Rerouting you to the nearest open spot.

It is 4 mins walk to the destination.

Accept New Route

Parking Predictability

When a dock is full, SmartPark reroutes riders to the nearest open station — gently guiding them with real-time updates and walking time transparency.

Park at 34th street dock and earn 1$.

Behavioral Incentive

Riders are nudged toward underused docks — earning micro-rewards for helping rebalance the system.

Layering Behavior Into the System These micro-moments rewire rider behavior.

SmartPark doesn’t just inform — it motivates, guides, and rewards in real time. That’s how we turn passive riders into active system collaborators.

Heads-up: Your destination dock is full.

Rerouting you to the nearest open spot.

It is 4 mins walk to the destination.

Accept New Route

Parking Predictability

When a dock is full, SmartPark reroutes riders to the nearest open station — gently guiding them with real-time updates and walking time transparency.

Park at 34th street dock and earn 1$.

Heads-up: Your destination dock is full.

Rerouting you to the nearest

Dock Availability:

open spot.

It is 4 mins walk to the destination.

SmartPark predicts when a rider is approaching a full dock and reroutes them to a nearby underutilized one —

Accept New Route

reducing drop-off frustration and eliminating overflow.

Zone-Based Demand: Riders earn points for parking in lowerdemand areas or soft hubs in outer zones, preventing bikes from clustering in high-traffic districts.

Real-Time Inputs: Hybrid docking

SmartPark

engine

infrastructure more efficient The system continually learns from realtime location, supply, weather, urgency, and rider type to update incentives accordingly.

Real Time Nudges rides more predictable

Loyalty System

Hybrid docking

SmartPark

engine

infrastructure more efficient

Loyalty System turns riders to advocates

Built-in Safety Protocols SmartPark allows dockless parking in unsafe areas during high-risk hours (e.g., 10pm–5am in low-lit zones) for no costs.

Point Based Loyalty Structure

Bird League

BirdBoost+

Free Tier Loyalty

Monthly/yearly Subscription

Engagement & behavior shaping

Upsell & retention

To replace street teams with gamified rider actions

Monetizing most loyal, task-accepting riders

Monthly or yearly

2× point multiplier

Free unlock every 5th ride

Access to early rewards (e.g., test new bike models, win free helmets)

Scoring Model

Peak hour congestion factors

Neighborhood-specific needs — making the system more responsive and efficient.

Transport hub, residential, or suburban area

High daily traffic = need for dock structure

% of loyalty members vs. casual users

Presence of shops, schools, transit, parks

Score: 90

Hoboken Terminal Station

(High usage: 40, Transport Hub: 30, Members >70%: 20, Peak >80%: 10)

Score: 38

(Low usage: 10, Suburb: 10, Members <50%: 10, Peak <50%: 3)

Score: 67

(Medium usage: 25, Residential: 20, Members 50-70%: 15, Peak 50-80%: 7)

Suburban Park Area Station

Newport Residential Area Station

Zone A

Zone B

Neighborhood-specific needs — making the system more responsive and efficient.

Zone C

Scoring Model

Our Goal

Turn casual riders into repeat users who keep the system moving - riding often and helping redistribute Bird bikes to where they’re needed most.

"One person's solution lies in another's choice."

We wanted to shift the conversation from ‘how do we fix user behaviour’ to ‘how do we design a system in tune with its environment?’

BIRD

Regulation

We’re solving for the ecosystem — where regulation, density, infrastructure, and human perception collide.

Personal Implications

Spatial heterogeneity

Regulation

Built in Environmental factors

BIRD

Economic benefits

USER

Our key takeaway: Dockless bikes don’t operate in vacuum, they’re shaped by context.

Infrastructure

Proximity

Spatial heterogeneity

BIRD Regulation

Density

CITY

Personal Implications Built in Environmental factors

Social Awareness

BIRD

Economic benefits

Why This Works Insights from prototype testing

Hurried: Willing to pay extra for convenient parking options (dockless or closer stations) to save time.

Non-Hurried Likely prefer cost-saving options (e.g., free parking or earning points), avoiding extra fees for convenience.

Key Driver Time sensitivity drives incentive success Price sensitivity impacts dockless choices Decisions are context-based; incentives guide behavior.

User Behaviour

City Source: dot

Destination incentives work better than origin-based

Immense social benefit in incentivizing modules,

if implemented and mandated by cities.

Users walk 3.8–4.2 min for $1 in reward

Women often optimize for safety and predictability

Men optimise for time savings

Bird

Low awareness is a bigger barrier than low motivation

Ridership Pattern & Willingness to Adapt Alternative Nudges PERSONA RIDERSHIP PATTERN

WORK COMMUTER

STUDENT

LEISURE/CASUAL

58-62%

12-15%

25-30%

Weighted by user mix (60 / 15 / 25) the

overall willingness score

≈ 0.6 × 0.25 + 0.15 × 0.50 + 0.25 × 0.40 = 0.34

LIKELINESS TO ACCEPT ALTERNATIVE NUDGE

(BASED ON OUR INTERVIEWS) PRICE SENSITIVITY

FLEXIBILITY TO DETOUR

25%

50%

40%

Low-Med

High

Med

Low (Mor)-Med (Eve)

Med-High

High

How did we come to the numbers, refer to addendum 1.

up to

~35 % city‑wide

Bird’s hybrid parking adoption model could cut rebalancing costs, recover lost peak-time rides, and reduce user frustration over lack of parking — proving loyalty points are more than just engagement.

Redistribution Vs Street Teams Street RESEARCH Teams

Incentives

Cost Efficiency

Lower operational cost since users handle redistribution organically

Incentives (discounts, credits) may be cheaper than paying street teams.

Speed

Slower since redistribution depends on user behavior

while street teams can respond immediately to demand spikes

Reliability

While redistributing, user behaviour isn't predictable

Scalability

Redistribution is highly scalable as there are no extra labor needed as user base grows.

Cost Comparison Table : Dockless vs Hybrid (Per 100 Vehicles) Category

Lime (Dockless)

Biketown (Hybrid)

Initial CAPEX

$80,000

~$250,000

Breakdown

100 e-bikes @ $800

25 docks @ $10K + 100 e-bikes @ $1.5K

Annual OPEX

$60,000

~$75,000

$70,000 (high

$40,000 (moderate)

Moderate

Strong (City of Portland

(Maintenance)

Rebalancing Costs

City Collaboration

partnership)

Understanding the financial and

operational trade-offs helps determine the most scalable and efficient model for citywide implementation.

Where hybird is beneficial and where it requires more effort?

Scalability City Compliance Hybrid systems offer compromise between control and freedom.

Scalable than dockless

Hybrid scales better with less backlash or enforcement risk.

Annual OPEX

(~$75K) Will require operational expertise and technological layer.

Initial CAPEX

(~$250K)

Cost-Efficient or Advantageous

Require More Effort or Coordination

Why Hybrid Wins

City Regulations

Brand Trust

Fleet Chaos

Pedestrian Needs

Hybrid Parking

Hybrid strikes a middle ground — optimizing cost, compliance, and user behavior through flexible infrastructure and behavioral incentives.

03

Risks or why our solution might not work?

A loyalty-based model could favor frequent riders or those with digital literacy — unintentionally excluding casual or lowincome users.

Equity Concerns

Policy or Regulatory Pushback

Maintenance Neglect Underperfor ming Zones (especially Zone C)

Incentive Fatigue

Exploit Incentive Loophole

Add a quick description of the number above.

Keep it as short and sweet as possible.

Provide context so viewers “get it.”

Provide links to docs or files.

If you have more stats, make a copy of this slide.

If you have fewer, cut what you don’t need.

Policy or Regulatory Pushback If riders get too many nudges,

Equity Concerns

Maintenance Neglect

or if rewards feel “small and meaningless” over time, the

Incentive Fatigue

Underperfor ming Zones (especially Zone C)

psychological value of SmartPark will decline.

Exploit Incentive Loophole

Add a quick description of the number above.

Keep it as short and sweet as possible.

Provide context so viewers “get it.”

Provide links to docs or files.

If you have more stats, make a copy of this slide.

If you have fewer, cut what you don’t need.

Policy or Regulatory Pushback

Equity Concerns

Maintenance Neglect

Underperfor ming Zones Riders might exploit point-based (especially incentives (e.g., taking unnecessary Zone C)

Incentive Fatigue

detours, switching accounts, or

Exploit Incentive Loophole

Add a quick description of the number above.

Keep it as short and sweet as possible.

Provide context so viewers “get it.”

faking rides to earn rewards).

Provide links to docs or files.

If you have more stats, make a copy of this slide.

If you have fewer, cut what you don’t need.

Cities might hesitate to allow flexible parking models (especially dockless) due

Policy or Regulatory Pushback

Equity Concerns

to prior issues with clutter, legal ambiguity, or pedestrian safety.

Maintenance Neglect Underperfor ming Zones (especially Zone C)

Incentive Fatigue

Exploit Incentive Loophole

Add a quick description of the number above.

Keep it as short and sweet as possible.

Provide context so viewers “get it.”

Provide links to docs or files.

If you have more stats, make a copy of this slide.

If you have fewer, cut what you don’t need.

Policy or Regulatory Pushback

Equity Concerns

Zone C (low-density, mostly dockless) might struggle with visibility, trust, or enforcement — leading to abandonment,

Maintenance misuse, or underutilization.

Neglect Underperfor ming Zones (especially Zone C)

Incentive Fatigue

Exploit Incentive Loophole

Add a quick description of the number above.

Keep it as short and sweet as possible.

Provide context so viewers “get it.”

Provide links to docs or files.

If you have more stats, make a copy of this slide.

If you have fewer, cut what you don’t need.

Incentives work well for rider behavior, but don’t solve for broken bikes,

Policy or Regulatory Pushback

Equity Concerns

vandalism, or neglected zones — which can erode trust.

Maintenance Neglect Underperfor ming Zones (especially Zone C)

Incentive Fatigue

Exploit Incentive Loophole

Add a quick description of the number above.

Keep it as short and sweet as possible.

Provide context so viewers “get it.”

Provide links to docs or files.

If you have more stats, make a copy of this slide.

If you have fewer, cut what you don’t need.

A good system nudges behavior.

A great one anticipates misuse, adapts across contexts, and builds trust across time.

And that is where city comes in to help shape and scale the system.

Shared Risk We can ask city to subsidise incentivisation program. That 1$ incentivisation given to users will be shared between city and bird.

Shared Data Policy makers and operators would benefit from an understanding of how the characteristics

of bike-share users and their trips influence mode substitution.

Car Substitution (Reduced VMT)

City’s Priority

Bike Share Systems

Transit Substitution

03

Future Scope A living system designed to grow with behavior, policy, and technology.

Evolve with Data

Enhance station placement and incentives using real-time user feedback and analytics.

Scale Hybrid Model

Expand hybrid stations to balance costs, maintenance, and coverage in new areas.

Incentivize Sustainability

Partner with cities to subsidize credits, promoting car and transit substitution.

Adapt to Challenges

Adjust pricing and infrastructure to mitigate risks like high fees or spatial mismatch.

03

Our solution is not a silver bullet — but a living system designed to evolve with behavior, policy, and infrastructure.

external engagement

Thank You

Flock : Akanksha, Manasa, Rucha, Sheldon

02.25