Scraping Grab Car & Bike Fare Data For Route-wise Pricing Analysis

By Author: travel scrape
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Introduction
Urban mobility platforms in Southeast Asia have evolved beyond simple ride booking apps. They now operate as complex, multi-modal ecosystems where cars, bikes, and other transport options compete for the same rider across different routes, time windows, and price sensitivities.
For mobility aggregators and city planners, understanding how consumers switch between car and bike rides is critical. Small pricing differences, time savings, or route characteristics can significantly impact ride demand and fleet utilization.
This case study explains how Travel Scrape helped a Southeast Asian mobility aggregator analyse route-wise pricing behaviour by scraping and structuring car and bike fare data from Grab. The outcome was a detailed pricing intelligence system that revealed mode preferences, time-price trade-offs, and actionable insights for product and planning decisions.
Business Challenge
The client operated a regional mobility aggregation platform covering multiple Southeast Asian cities. While they had access to high-level usage metrics, they lacked granular visibility into how riders ...
... chose between cars and bikes on specific routes.
Key challenges included:
No route-level comparison between car and bike fares
Limited understanding of airport versus CBD pricing behaviour
Inability to quantify peak and off-peak fare differences by mode
No structured view of time saved versus price paid
Difficulty forecasting modal demand for city-level planning
Without this intelligence, decisions around fleet allocation, product mix, and partner strategy were largely assumption-based.
Objective
The client partnered with Travel Scrape to build a route-wise pricing intelligence framework with the following goals:
Compare car and bike fares on identical routes
Measure price differences across airport, CBD, and residential routes
Track peak vs off-peak pricing behavior
Analyze time saved versus fare paid
Identify consistent switching patterns across cities
Scope of Data Collection
Travel Scrape designed a structured scraping framework to ensure clean, comparable data across cities and time windows.
Routes Covered
CBD → Airport
Airport → CBD
CBD → Residential Zones
Short intra-CBD routes
Ride Modes
Grab Car
Grab Bike
Time Segments
Morning peak
Midday off-peak
Evening peak
Late-night off-peak
Cities
Major SEA metro cities with high ride density
Tourist-heavy cities with strong airport demand
Business hubs with dense CBD traffic
Data Points Captured
Each fare request was normalized to ensure accurate comparison between car and bike modes.
Route start and end coordinates
Ride mode (car or bike)
Estimated fare
Estimated travel time
Surge or dynamic pricing indicator
Time of day and day type
Car vs. Bike: 2026 Urban Transit Analysis
The data reveals a clear "Congestion Tax" on car travel in dense city centers, where the speed advantage of a vehicle is almost entirely neutralized by gridlock.
CBD → Airport (The Premium Gap): Choosing a car for this route costs an additional $10.50 for a gain of only 12 minutes. In 2026, many airports have introduced "Green Priority Lanes" for micromobility, narrowing the time gap even further. For a solo traveler, the car option essentially charges $0.87 per minute of time saved.
CBD → CBD (The Efficiency Trap): This is the least efficient route for car travel. A car costs double the price of a bike ($6.20 vs. $3.10) to save a mere 4 minutes. In most major metros today, by the time you factor in parking or ride-hail wait times, the bike is often the faster "door-to-door" option.
Residential → CBD (The Commuter Balance): Car travel remains popular here for comfort, but at $9.80, it is more than twice as expensive as the bike ($4.60). The 6-minute time difference is often negligible for most commuters, leading to the 11.9% growth in e-bike adoption we are seeing this year for "mid-range" commutes.
This structured dataset allowed the client to compare price and time trade-offs on identical routes.
Methodology Used by Travel Scrape
Route Normalization
Fixed pickup and drop points were defined for every route. This eliminated variability caused by different rider locations and ensured consistent comparisons.
Time-Window Sampling
Fare data was captured multiple times per day across weekdays and weekends to reflect real-world pricing dynamics.
Mode-to-Mode Matching
Car and bike fares were always captured within the same time window to prevent bias caused by sudden demand fluctuations.
Data Cleaning & Validation
Outliers caused by abnormal surges or technical errors were flagged and reviewed before analysis.
Key Insights

1. Bikes Dominated Short CBD Routes
Across all cities studied, bike rides consistently outperformed cars on short CBD routes.
Bike fares were 45–60% lower than car fares
Time difference was typically under 5 minutes
Riders prioritized cost savings over minimal time loss
This made bikes the default choice for short urban trips.
2. Cars Remained Preferred for Airport Routes
Despite significantly higher fares, cars were strongly preferred on airport routes.
Average car fares were 2–2.5x higher than bike fares
Time savings ranged from 10 to 20 minutes
Riders valued comfort, luggage space, and reliability
This behavior was consistent across both business and tourist cities.
3. Price-Time Tradeoffs Were Highly Consistent
One of the most valuable findings was consistency.
When time saved exceeded 10 minutes, riders chose cars
When time saved was under 5 minutes, bikes dominated
These thresholds remained stable across cities
This allowed the client to model rider behavior with higher confidence.
4. Peak Pricing Affected Cars More Than Bikes
During peak hours:
Car fares showed sharper surge behavior
Bike fares increased marginally or remained stable
Price gaps widened significantly during congestion
This created strong bike adoption during peak CBD traffic.
5. City Patterns Were Predictable, Not Random
While absolute fares differed city to city, relative behavior remained similar.
CBD density increased bike preference
Airport distance increased car preference
Tourist cities showed higher willingness to pay for cars
This insight supported scalable expansion strategies.
Business Impact
Improved Product Mix Decisions
The client used Travel Scrape’s insights to adjust how car and bike options were presented in different zones and time windows.
Bikes highlighted for short CBD routes
Cars emphasized for airport and long routes
Data-Driven City Mobility Planning
Route-level demand forecasts helped the client:
Plan driver onboarding by mode
Anticipate peak-hour demand shifts
Optimize supply distribution by zone
Modal Demand Forecasting
By combining pricing and time data, the client built predictive models to estimate:
Mode switching under fare changes
Impact of congestion on ride choice
Sensitivity to surge pricing
Stronger Partner Negotiations
Clear evidence of route-wise performance allowed the client to negotiate better terms with fleet partners and operators.
Why Travel Scrape
Travel Scrape specializes in large-scale mobility and travel data extraction, offering:
Route-based fare intelligence
Multi-modal pricing comparison
City-level mobility insights
Clean, analysis-ready datasets
Our approach focuses on accuracy, repeatability, and real-world business relevance.
Conclusion
Understanding how riders choose between car and bike options requires more than average pricing data. It demands route-level, time-aware, and mode-specific intelligence.
Through systematic scraping and analysis of Grab car and bike fares, Travel Scrape enabled a mobility aggregator to uncover consistent behavioral patterns, improve product decisions, and plan smarter urban mobility strategies.
This case study demonstrates how structured mobility data can directly influence pricing, planning, and growth decisions across Southeast Asia.

Source : https://www.travelscrape.com/scraping-grab-car-bike-fare-data-route-wise-analysis.php

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

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