Negotiation-based Fare Analytics Using Indrive Pricing Data

By Author: travel scrape
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Introduction
Most ride-hailing platforms rely on algorithmic pricing. Fares are calculated automatically based on distance, demand, traffic, and surge logic. While this model offers predictability, it hides an important economic signal: what price the market is actually willing to accept.
Negotiation-based ride platforms operate very differently. Instead of fixed fares, riders propose a price and drivers decide whether to accept, reject, or counter. This creates a live pricing marketplace where every trip reveals real demand elasticity.
This case study explains how Travel Scrape helped a transport research firm analyse negotiation-driven pricing behaviour by scraping structured data from inDrive. By capturing user-offered fares, accepted prices, and acceptance rates across route types, the client gained insights that are impossible to extract from fixed-fare platforms.
Business Challenge
The client was a transport research and analytics firm studying real-world price formation in urban mobility.
Their core objective was to answer a simple but powerful question:
What price actually clears ...
... the market when riders and drivers negotiate freely?
However, existing mobility datasets were not designed for this purpose.
Limitations of Traditional Fare Data
Most ride-hailing data focuses on:
Algorithm-generated fare estimates
Distance-based pricing
Surge multipliers
Final charged price only
This approach ignores the negotiation process itself, which is the most valuable signal on platforms like inDrive.
Specific Challenges Faced
1. No Visibility Into Offered vs Accepted Prices
The client needed to observe:
What price riders initially propose
What price drivers actually accept
The gap between offer and acceptance
Without this, price elasticity could not be modelled accurately.
2. Lack of Route Context
Negotiation behaviour varies significantly by route type:
Airport routes behave very differently from residential trips
CBD congestion changes driver willingness
Residential trips show higher price sensitivity
The client needed route-level segmentation.
3. Time-of-Day Effects Were Unclear
The firm suspected that:
Drivers are more flexible during off-peak hours
Negotiation success rates drop during peak congestion
But they lacked structured data to prove it.
Why Negotiation-Based Fare Analytics Matters
Negotiation platforms represent true market pricing.
Unlike fixed-fare platforms:
Prices are not pre-optimized by algorithms
Drivers express real willingness-to-accept
Riders reveal real willingness-to-pay
For researchers, economists, and platform designers, this creates a rare opportunity to observe price discovery in action.
Travel Scrape proposed a data framework that captured the entire negotiation funnel, not just the final outcome.
Solution by Travel Scrape

Travel Scrape designed a negotiation-centric fare intelligence system focused on capturing every meaningful pricing signal from inDrive.
Instead of treating fares as a single value, the solution modelled negotiation as a multi-stage process.
Negotiation Data Framework
Travel Scrape structured the dataset around four core dimensions:
User-Offered Fare
Accepted Fare
Response / Acceptance Count
Route Type Classification
This framework allowed the client to analyse not just prices, but behavioural responses to pricing.
Route Classification Logic
To ensure analytical clarity, each trip was mapped to one of three route types:
Airport routes
Central Business District (CBD) routes
Residential routes
This segmentation revealed how negotiation dynamics shift based on trip importance, urgency, and driver opportunity cost.
Data Collection Methodology
Travel Scrape implemented automated data extraction with strict consistency rules.
Data Capture Frequency
Multiple data pulls per day
Coverage across:
Morning peak
Midday
Evening peak
Late-night off-peak
Both weekdays and weekends
Data Attributes Captured
For each negotiation instance, the following fields were collected:
City
Route type
User-offered fare
Driver-accepted fare
Acceptance or rejection flag
Number of driver responses
Time of day
Timestamp
This structure allowed deep behavioural analysis rather than surface-level pricing comparison.
2026 Ride Negotiation & Market Dynamics
The current year has seen a "Driver Empowerment" shift. With global inflation stabilizing but fuel/EV charging costs remaining high, drivers are using negotiation apps to ensure every trip covers their specific operational margins.
Airport → CBD (The "Value Gap"): This route shows the highest negotiation correction (19.4% jump). An initial offer of €18.00 results in a low 42% acceptance rate. Drivers typically hold out for €21.50 to compensate for "Deadhead" miles (returning empty) and long airport wait times. In 2026, airport routes are considered "Premium Commutes" where drivers have the most leverage.
CBD → Residential (The Commuter Balance): For urban commutes, a small €0.70 adjustment (approx. 7.4%) significantly boosts acceptance to 68%. This indicates that for routine trips, riders and drivers are generally "in sync" on value, and a minor tip or bid increase is enough to secure a vehicle during the evening rush.
Residential → Residential (The Efficiency Benchmark): This is the most efficient market segment with an 81% acceptance rate. The negligible difference between the offer (€7.00) and the accepted price (€7.40) suggests that for short, local trips, the "market price" is well-established. These trips often serve as "fillers" for drivers between longer, higher-value bookings.
This format clearly highlights price gaps and negotiation success rates.
Analytical Models Applied
Travel Scrape designed the dataset to support advanced modelling.
Key Metrics Derived
Offer-to-acceptance premium
Acceptance probability by price band
Route-wise price elasticity
Time-of-day negotiation success
Driver response sensitivity
Key Insights Uncovered
1. Airport Routes Required a 20–30% Premium for Acceptance
Airport trips showed the strongest negotiation resistance.
Low acceptance at initial offer prices
Higher driver expectations due to:
Longer distance
Toll costs
Opportunity cost of return trips
Accepted prices were typically 20–30% above user offers
This confirmed that airport routes have structural price floors.
2. Residential Routes Showed High Price Sensitivity
Residential routes behaved very differently.
Small price increases led to large acceptance jumps
Acceptance rates often exceeded 75%
Drivers were more flexible due to:
Shorter trips
Higher trip density
Lower risk
These routes were ideal for studying elastic pricing behavior.
3. Negotiation Success Varied Strongly by Time of Day
Time-of-day analysis revealed clear patterns:
Morning peak had the lowest acceptance rates
Midday and late night showed higher flexibility
Evening peak showed higher accepted premiums
This allowed the client to model temporal elasticity curves.
4. Response Count Signalled Market Tightness
The number of driver responses proved to be a powerful signal.
Fewer responses indicated supply scarcity
Higher responses correlated with lower accepted prices
Response count acted as a real-time liquidity indicator
This insight was not available on fixed-fare platforms.
Business Impact
The negotiation-based analytics delivered unique strategic value.
1. Enabled Advanced Price Elasticity Modelling
The client built elasticity models using:
Offer vs acceptance curves
Route-specific acceptance thresholds
Time-based sensitivity adjustments
This allowed far more accurate demand modelling than fixed-fare data.
2. Delivered Insights Unavailable on Fixed-Fare Platforms
Unlike Uber or Bolt-style pricing:
inDrive data revealed real human negotiation behaviour
Drivers actively expressed willingness-to-accept
Riders revealed willingness-to-pay
This made the dataset uniquely valuable for research and platform design.
3. Supported UX and Pricing Strategy Decisions
The client used the insights to advise mobility platforms on:
Suggested price nudges
Dynamic offer recommendations
UX design for negotiation prompts
Price anchoring strategies
Decisions were based on observed behaviour, not assumptions.
Why Travel Scrape
Travel Scrape specializes in advanced mobility and travel data intelligence, including non-traditional pricing models.
Key strengths:
Negotiation-based data capture
Behavioural pricing analytics
Route-level segmentation
Time-series mobility data
Research-grade datasets
Travel Scrape enables insights that go beyond averages and algorithms.
Conclusion
Negotiation-based ride platforms expose the true mechanics of pricing. They show where demand breaks, where supply resists, and what price ultimately clears the market.
This case study demonstrates how Travel Scrape transformed scraped inDrive negotiation data into actionable fare intelligence, enabling a transport research firm to model price elasticity, understand route-level behaviour, and uncover insights hidden from fixed-fare platforms.
For organizations studying mobility economics, negotiation-based fare analytics is one of the most powerful tools available.

Source : https://www.travelscrape.com/indrive-negotiation-based-fare-analytics-pricing-data.php

Originally published at https://www.travelscrape.com.

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