Client

A premium ride hailing company

Objective

The primary objective of this cab commuter study was to understand cab commuting behavior and dynamics across five key hotspots in Delhi from 10 AM to 5 PM on weekdays. The research aimed to capture:

• The demographic analysis of cab commuters (gender, age group, and profession).

• The volume and frequency of cab usage compared to autos,providing direct cab and auto comparison.

• Insights into the commuter base at high-traffic, moderate-traffic, and low-traffic zones within the selected locations, enriching our Delhi traffic hotspots understanding.

By identifying these patterns, the client sought to optimize their services and operations for cab drivers, aligning them with commuter preferences and hotspot dynamics.

Methodology

A mixed research methodology combining quantitative in-depth interviews and observation-based surveys was employed:

In-Person Interviews

• Conducted interviews with 400 cab drivers spread across different platforms:

• Interviewers visited cab hubs in Delhi and Gurgaon, engaging directly with drivers at hotspot locations.

Observation-Based Surveys

• Researchers were stationed at key hotspots:

  • Connaught Place (Inner Circle, Outer Circle, Janpath, Barakhamba Road)

  • Nehru Place & Lajpat Nagar

  • South Extension (Part 1 & 2)

  • Saket Malls (Select City, DLF Avenue, Anupam)

  • Vasant Kunj Malls (Ambience, Promenade).

• Data was recorded for 50 observation instances at each location, capturing:

  • Gender of commuters.

  • Professional vs. non-professional passengers.

  • Number of people per cab.

  • Number of autos observed.

  • Age group distribution (18–25, 25–40, 40–55, 55+).

Findings

Demographic Insights

• Age Groups: The majority of cab users fell into the 25–40 age group, followed by 18–25, indicating high usage by working professionals and highlighting a trend in young adult commuter behavior.

• Gender: Indicating distinct gender commuting patterns, male commuters formed a slightly higher percentage, though female usage spiked in areas near shopping hubs like Saket Malls and Vasant Kunj.

• Professional vs. Non-Professional: A significant portion of riders in South Extension and Nehru Place were working professionals, while areas like Saket and CP showed a mix of professionals and leisure travelers.

Commuter Volume

• High-traffic areas like Connaught Place, Nehru Place Metro Station and Saket Malls showed significant cab usage, informing strategic driver deployment strategies.

• Moderate-Traffic Areas:

  • South Extension 1 & 2.

  • Janpath and Barakhamba Road (CP peripheral areas).

• Low-Traffic Areas:

  • Amar Colony and M Block Market in GK1 (Lajpat Nagar zone).

Moderate-traffic and low-traffic areas provided insights into less saturated markets, presenting opportunities for targeted growth and cab service optimization.

Driver Preferences and Trends

• Duration on Platform: Most drivers worked between 8–10 hours daily.

• Break Frequency and Duration: Drivers took 2–3 breaks per day, each lasting around 30 minutes.

• Daily Trip Completion: Some drivers from premium ride hailing company completed the highest number of trips, averaging 12–15 daily.

• Weekly Revenue and Earnings: Gross weekly revenue varied significantly across platforms.

Mode Comparison

• Autos were more prevalent in areas like Lajpat Nagar and Nehru Place, while cabs dominated CP, South Ex, and Saket.

The research provided valuable insights into various aspects of cab drivers' experiences across different platforms. Below is a detailed summary of the findings:

• Drivers typically spent 8–10 hours daily on their respective platforms, with some reporting extended hours during peak demand periods.

• The ease of use, consistent trip assignments, and transparent payment processes were highlighted as positives for most platforms. At the same time, complaints included high commission rates, frequent app glitches, and unavailability of adequate customer support.

• I emerged that drivers generally took 2–3 breaks per day, averaging 30 minutes per break being utilized for meals or resting during non-peak hours.

• with regard to daily driving hours, on average, drivers were reported to be actively engaged for 6–8 hours of actual driving per day, with the remaining time spent waiting for trip assignments or resting.

• Most drivers completed 10–15 trips daily reporting the highest trip counts due to higher demand on these platforms.

• Gross weekly revenue varied by platform and driver activity. Drivers from premium platforms reported higher gross earnings compared to those on budget-friendly platforms.

• After accounting for fuel, maintenance, and platform commissions, drivers’ net weekly earnings ranged significantly more due to better incentives and trip volumes.

• Drivers appreciated features like surge pricing, daily incentives, and referral bonuses that increased their overall earnings.

• Non-monetary perks such as flexible working hours, platform-provided insurance, and driver training programs were well-received.

• While drivers expressed overall satisfaction with their ability to earn a living, concerns regarding platform transparency, commission structures, and support services were frequently cited as areas for improvement.

These findings highlight a clear roadmap for platform providers to enhance their offerings and improve driver experiences, ultimately fostering loyalty and efficiency within their networks.

Challenges

1. Data Collection Resistance:

   Some drivers were hesitant to share sensitive data such as weekly rides and revenue figures. Overcoming this required additional reassurance and tactful negotiation.

2. Observer Deployment:

   Managing observers at multiple locations simultaneously while ensuring uniformity in data capture posed logistical challenges.

3. Commuter Interaction:

   Observers faced occasional reluctance from passengers when approached for clarifications, especially in high-footfall areas.

Business Implications

The study provided the client with actionable insights to refine their operations:

• Strategic Location Prioritization:

  High-traffic areas like CP and Saket were earmarked for increased driver deployment and enhanced commuter services.

• Driver-Centric Improvements:

  Findings on driver satisfaction, break durations, and earnings enabled the client to optimize incentive structures and support services.

• Enhanced Cab-Allocation Efficiency:

  By understanding commuter profiles and peak times, the client could align driver availability with demand patterns.

• Better Competitive Positioning:

  The client gained insights into commuter preferences for autos vs. cabs, enabling targeted marketing strategies to convert auto users into cab passengers.

Conclusion

The study successfully mapped the cab commuter base across Delhi's top hotspots, uncovering valuable trends in demographics, traffic density, and commuter behavior. Despite challenges in data collection, the research provided a robust foundation for the client to optimize their services, prioritize high-demand areas, and improve overall driver satisfaction.

These insights not only enhanced operational efficiency but also solidified the client’s reputation as a driver-focused service provider in the competitive cab market.

Explore how our research transformed commuter experiences and operational efficiency for cab service providers, Connect with an expert to gain actionable advice.