Ride-Hailing vs Personal Vehicle Usage Behaviour Survey
Measure how urban commuters evaluate, compare, and choose between ride-hailing platforms and personal vehicles across cost, convenience, and reliability, so you can sharpen acquisition targeting, refine pricing tiers, and fix retention gaps.
CONTEXT & RELEVANCE
Why run this survey now
Most mobility planners don't lose riders purely on fare pricing. They lose them due to trip reliability gaps, ownership cost shifts, route coverage mismatches, modal habit inertia, and surge perception fatigue, none of which fully show up in app engagement dashboards or vehicle registration data.
If you are...
- Ride-hailing network expansion lead
- OEM fleet and mobility partnerships head
- Personal vehicle category planning manager
- Urban mobility GTM strategy director
- Automotive retail demand forecasting lead
You're likely facing...
- Ownership vs subscription intent gap
- Surge pricing: convenience vs abandonment
- First/last mile modal substitution pressure
- Ride-hailing = fast/unreliable perception
- Fleet renewal cycle vs ridership shift
This will help answer...
- Modal preference drivers by segment
- Trip occasion switching triggers
- Ownership cost tolerance thresholds
- Ride-hailing vs personal vehicle frequency
- Retention and re-adoption inflection points
RESEARCH THEMES
What This Survey Investigates
Eight interconnected research themes that map the complete urban commuter journey from trip trigger to mode loyalty.
Mode Choice Triggers
- Primary trip initiation factors
- Situational vs habitual mode selection
Platform & App Preference
- Primary ride-hailing platform used
- App switching frequency, triggers
Ownership Cost Perception
- Perceived monthly personal vehicle cost
- Fuel, parking, EMI burden awareness
Journey Friction Points
- Cancellation, wait time, surge pain
- Personal vehicle parking and congestion friction
Pricing Sensitivity & WTP
- Fare threshold before mode switch
- Subscription and pass willingness
Safety & Comfort Drivers
- Safety perception by gender, time of day
- Vehicle category and comfort trade-offs
EV & Sustainability Signals
- EV ride-hailing preference and awareness
- Personal EV ownership intent vs ride-hailing
Mode Loyalty & Switching
- Long-term vehicle ownership intent
- Ride-hailing share of total monthly trips
SAMPLING STRATEGY
Tell us about your ideal sample
Help us understand your target respondent profile. Select what applies, we'll design the optimal sample plan based on your inputs.
METHODOLOGY
Survey approach
For the Ride-Hailing vs Personal Vehicle Usage Behaviour Survey, we recommend a quant-first design with flexible data-collection modes to balance reach, depth, and verification.
EXECUTION PROCESS
How we execute
A proven 9-step process from scoping to delivery, designed to ensure quality, speed, and actionable insights.
Define the decision frame
Confirm objectives, target cohorts, geographies, and reporting cuts
Define the decision frame
Design the instrument
Build workstream modules mapped to outputs (drivers, friction, pricing, retention, trust)
Design the instrument
Lock the questionnaire
Review wording, sequencing, LOI, and competitive context; approve final version
Lock the questionnaire
Pilot and calibrate
Test comprehension and ease quality; refine quotas and remove friction where needed
Pilot and calibrate
Run fieldwork
Execute collection with active quota management and feasibility controls
Run fieldwork
Assure quality
Dedupe, attention checks, speed/consistency rules, removals with audit trail
Assure quality
Prepare the dataset
Clean data and deliver codebook/variable definitions
Prepare the dataset
Analyse and synthesise
Driver ranking, leakage diagnostics, pricing bands, segment insights
Analyse and synthesise
Deliver and align
Executive deck (optional dashboard) and leadership readout with recommendations
Deliver and align
COMMERCIAL TERMS
Request a Commercial Proposal
Pricing depends on cohort, geography, sample size, approach, LOI, and deliverables. Configure below for an indicative estimate.
Select Sample Size
Geography
Select Mode of Survey
Length of the Interview
$0.00
+ applicable taxes
Cost Breakdown
Note: Estimate is indicative only. Final pricing is subject to scope finalization after discovery call.
REFERENCE CASELETS
Reference
Real-world examples of survey work in the urban mobility and personal transport space.
Last-mile mode preference & switching triggers (Tier 1 India)
OBJECTIVE
WHAT WE DID
DELIVERED
Last-mile mode preference & switching triggers (Tier 1 India)
Ownership intent & ride-hailing substitution among young earners (Metro India)
Last-mile mode preference & switching triggers (Tier 1 India)
OBJECTIVE
A pan-India mobility platform needed to isolate what drives daily urban commuters to shift between app-based autos and personal two-wheelers , specifically around surge pricing events and monsoon-period availability gaps.
WHAT WE DID
Ran a structured quant survey across 6 Tier 1 cities with 1,200 respondents, capturing trip frequency by mode, switching triggers, surge tolerance thresholds, and stated willingness to lock in monthly ride credits versus on-demand usage.
DELIVERED
A mode-switching trigger map by commuter segment, a surge tolerance corridor by trip purpose, and a ranked friction list identifying the 4 conditions most likely to push daily riders back to personal vehicle ownership.
FREQUENTLY ASKED QUESTIONS
Common Questions
Answers to frequently asked questions about this survey mandate.
What decisions will this survey enable?
Who is the buyer vs who are the respondents?
Can we see differences between daily ride-hailing users, occasional ride-hailing users and personal vehicle-primary commuters?
How will you measure mode preference beyond simple ratings?
Will the survey map the full commute decision journey and drop-offs?
Can this survey inform product and pricing strategy?
How will findings improve our rider acquisition and retention strategy?
Still have questions?
Schedule a discovery call to discuss your specific needs and get a custom quote.