In what ways can we improve Swvl's route optimization to reduce travel times for passengers?

Introduction

To improve Swvl's route optimization and reduce travel times for passengers, we need to take a comprehensive look at the current system, user behavior, and technological capabilities. I'll outline a strategic approach to tackle this challenge, focusing on data-driven solutions and user-centric improvements.

Step 1

Clarifying Questions

• Looking at Swvl's business model, I'm thinking about the scale of operations. Could you provide insight into the current number of daily rides and the average route length? This information is crucial for understanding the complexity of our optimization challenge and the potential impact of improvements.

Why it matters: Determines the scale of data we're working with and the potential for machine learning applications. Expected answer: 100,000+ daily rides with an average route length of 15-20 km. Impact on approach: High volume would justify investment in advanced AI/ML solutions for route optimization.

• Considering user behavior, I'm curious about the booking patterns. What percentage of rides are pre-booked vs. on-demand? This distinction could significantly affect our approach to route optimization.

Why it matters: Pre-booked rides allow for more predictive routing, while on-demand requires real-time optimization. Expected answer: 70% pre-booked, 30% on-demand. Impact on approach: Would focus on predictive algorithms for pre-booked rides and real-time adjustments for on-demand.

• Regarding current pain points, what's the average deviation from the estimated arrival time, and what are the primary causes of delays? This information will help us prioritize areas for improvement.

Why it matters: Identifies the most impactful areas for optimization and the root causes of inefficiencies. Expected answer: 15-20 minute average deviation, primarily due to traffic congestion and suboptimal route planning. Impact on approach: Would focus on traffic prediction and dynamic rerouting capabilities.

• From a technology standpoint, what data sources are currently being used for route planning? Understanding our current capabilities will help determine where we can make the most impactful improvements.

Why it matters: Identifies potential gaps in our data sources and opportunities for integration of new data streams. Expected answer: Currently using GPS data from vehicles and basic traffic information from third-party APIs. Impact on approach: Would explore integration of real-time traffic data, weather information, and historical performance data.