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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
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                            [ja] => コロンバス
                            [pt-BR] => Columbus
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                            [zh-CN] => 哥伦布
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [zh-CN] => 美国
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                    [longitude] => -83.0061
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            [postal] => Array
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            [registered_country] => Array
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                            [es] => Estados Unidos
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                                    [ru] => Огайо
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            [traits] => Array
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    [continent:protected] => GeoIp2\Record\Continent Object
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                    [geoname_id] => 6255149
                    [names] => Array
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                            [de] => Nordamerika
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                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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    [country:protected] => GeoIp2\Record\Country Object
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                    [iso_code] => US
                    [names] => Array
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [validAttributes:protected] => Array
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                    [3] => isoCode
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [validAttributes:protected] => Array
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [2] => isInEuropeanUnion
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                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.150
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
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                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
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                    [19] => staticIpScore
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                )

        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

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            [validAttributes:protected] => Array
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    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
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                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
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                    [4] => metroCode
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                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [validAttributes:protected] => Array
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)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)
Real-Time Regional Insights with Customizable E-commerce Dashboards

Executive Summary

Actowiz Solutions conducted a 30-day deep-dive into hourly ride fare trends across popular U.S. routes, focusing on airport-to-city and city-to-suburb routes, such as LAX → Santa Monica, JFK → Manhattan, and Downtown Chicago → O’Hare. Using real-time scraping from ride-hailing platforms like Uber, Lyft, and Curb, we tracked fare estimates every hour — analyzing price fluctuations, surge triggers, and route-specific patterns.

This granular fare intelligence uncovered predictable surge windows, showed how pricing behavior differs by platform, and revealed which hours commuters can save up to 38% on the same ride. The insights help ride aggregators, business travelers, and pricing engines make data-driven decisions.

Research Objective

The core goal of this case study was:

  • Analyze hour-by-hour fare changes on high-traffic U.S. ride routes.
  • Identify peak fare windows and surge-prone hours.
  • Compare platform-specific behavior (Uber vs. Lyft vs. Curb).
  • Build a real-time API and dashboard to deliver hourly pricing insights for urban mobility applications.

Routes Tracked

Primary Focus Route: LAX → Santa Monica (17.5 miles)

Additional Routes:

  • JFK Airport → Manhattan (NYC)
  • Chicago O’Hare → The Loop
  • San Francisco Airport (SFO) → Downtown SF
  • Miami Intl. Airport → Brickell
  • Seattle-Tacoma Airport → Downtown
  • Boston Logan Airport → Back Bay

Data Collection Methodology

Parameter Description
Platforms Uber, Lyft, Curb
Tools Used Headless browsers (Puppeteer, Selenium)
Coordinates Fixed pickup/drop-off GPS points
Frequency Every 1 hour, 24/7 for 30 days
Fields Extracted ETA, Fare Estimate, Ride Tier, Surge Info
Scraping Volume Over 300,000+ hourly records
Output Format JSON, CSV, and real-time dashboard feed

Anti-blocking methods:

Proxy rotation, user-agent spoofing, and browser fingerprinting simulation.

Sample Dataset – LAX to Santa Monica (UberX, July 2025)

Time (PST) Fare (USD) Surge Multiplier ETA (mins)
06:00 AM $28 1.0 8
08:00 AM $38 1.4 12
10:00 AM $33 1.2 10
01:00 PM $27 1.0 7
05:30 PM $42 1.6 14
09:00 PM $29 1.0 9
12:00 AM $24 1.0 6

Fare range: $24 to $42

Peak: 5:00–6:30 PM

Lowest fares: 11 PM–6 AM

Visualization – Hourly Fare Heatmap (LAX → Santa Monica)

Introduction
Hour Avg Fare Surge Frequency
12 AM–6 AM $25.80 5%
6 AM–9 AM $34.60 31%
9 AM–12 PM $31.20 22%
12 PM–4 PM $29.70 18%
4 PM–7 PM $39.10 42%
7 PM–10 PM $30.20 16%
10 PM–12 AM $26.50 6%

Insight: Commuter traffic + airport pickups between 6–9 AM and 4–7 PM trigger the most price hikes.

Platform-Wise Comparison

Platform Avg Base Fare Surge-Triggered Hours ETA Avg
Uber $32.5 39% 10.5 min
Lyft $30.8 28% 11.2 min
Curb $35.2 18% 13.1 min

Lyft had more stable pricing

Uber surged most aggressively

Curb was consistently costlier

Sample Insights from Other Routes

JFK to Manhattan
  • Peak fare: $69 (Uber Black, 7 PM)
  • Lowest fare: $41 (Lyft Shared, 2 AM)
  • Surge triggers: Rain, Friday rush
Chicago O’Hare to Loop
  • Surge window: 6–8 AM and 4–7 PM
  • Uber: Faster ETA, Lyft: Lower prices
Miami Airport to Brickell
  • Surge spikes after cruise arrivals
  • Fare range: $19 – $43

Key Market Observations

Consistent Surge Zones
  • Airport pickups surge predictably during peak air traffic windows.
  • Evening downtown drop-offs have dynamic pricing triggered by demand fluctuations and local events.
Price Spread by Hour
  • Hourly fare range for the same route varied by up to 70% in some cases.
  • Early morning rides saved up to 38% vs. peak evening hours.
Platform Differences
  • Uber’s aggressive dynamic pricing can lead to higher volatility.
  • Lyft’s algorithm showed more moderate surging.
  • Curb’s traditional pricing model made it expensive but predictable.

Strategic Recommendations

1. Fare Aggregators:

Build fare alerts based on hourly patterns. Users can save 20–35% by delaying travel by just 1 hour.

2. Airport Mobility Services:

Target non-surge hours for marketing or promo codes to attract budget travelers.

3. Fleet Operators:

Use hourly fare trends to reposition drivers during high-profit windows.

4. Smart Ride Apps:

Embed Actowiz fare API into maps/ride booking tools for real-time savings alerts.

Technical Challenges & Solutions

Challenge Resolution
Captchas on Lyft API Headless Chrome + human delay emulation
Geo-fencing restrictions VPN + IP pool rotation
Cross-platform ride tier mapping Created unified ride tier index
Rate-limiting during surge Distributed scheduler with retry logic

Use Case: API + Dashboard Snapshot

Output Features:

  • Hourly price charts by route
  • Cheapest platform at each hour
  • Surge index per platform
  • Export to CSV or webhook-based updates

Client used this to:

  • Offer ride price predictions
  • Generate weekly fare trend reports
  • Power a public-facing “When to Ride” tool

Business Impact

Client’s travel app saw a 14% increase in retention due to smart fare alerts.

New partnership offers for airport shuttle companies using predictive surge trends.

Generated recurring revenue with a B2B API model for ride fare data licensing.

Conclusion

Hourly fare tracking across busy U.S. routes unlocks strategic advantages for everyone — from everyday commuters to transportation startups and fleet managers. Actowiz Solutions empowers businesses with accurate, real-time ride fare intelligence to save costs, improve routing, and enhance user experience.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Industry:

Fintech / Digital Payments

Result

Accurate daily voucher &

cashback visibility across platforms

★★★★★

“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”

Product Manager, Fintech Platform (India)

✓ Daily voucher & cashback tracking via Push & Pull APIs

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

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