Redesigning safety experience for women drivers across Southeast Asia
Overview
Role
Product Strategy
End-to-end Visual Design +
Interaction Design Sizzle Reel
Timeline
Q2 2024 - Q3 2025
Q1 2025
Press
Link
Link
Link
Grab serves millions of rides daily across Southeast Asia with a 99.99% safety rate. But behind that impressive number, the remaining 0.01% tells a different story - real people facing harassment, assault, and life-threatening situations.
When we looked closer, a pattern emerged: women drivers and passengers were disproportionately affected, especially during night rides paid in cash. I led a project to redesign Grab's safety experience to protect our most vulnerable users, women drivers and passengers.
Background
Safety-rich but context-poor
Grab's safety features evolved through accretion: emergency SOS, Trip Monitoring, Share live location, selfie verification. But everyone got the same experience. [Image] A male driver working daytime in Singapore had identical safety features as a female driver accepting late-night cash rides in a rural area in Cambodia. Our universal approach was failing the people who needed protection most. (Source: 18 months of Grab incident data)
Challenge
Shift from one-size-fits-all to contextual safety
Our goal wasn't to introduce more features. we needed to redesign safety to recognize different levels of vulnerability and create contextual experiences that matched real risk.
Our objectives:
1. Reduce safety incidents among vulnerable users (deliver actual safety)
2. Increase trust and confidence in the platform (strengthen perceived safety)
3. Build a scalable foundation for future safety innovation
My role
I led the design of the safety experience for vulnerable users between Jan 2024 and Dec 2025 and collaborated with other designers on driver app experience and transport experience. In addition, I worked alongside a Research Operations Producer, Data Scientists, Content Strategist, Engineers, and 3 Product Managers. I worked from ideation stage to implementation.
At the start of the project, “making rides safer” was an abstract goal. We didn’t yet understand where safety gaps existed. I partnered with our research operation producer to ground the problem in real experiences.
Early insights from the field
Two different types of risk:
Constant risk vs. Conditional risk
Women drivers loved the flexibility—they could work around school drop-offs and family time. But their risk was constant. They worked alone with strangers all day, and couldn't take breaks without losing income. They needed protection that was always on.
Women passengers were different. Most rides felt safe. But risk appeared in uncomfortable moments—when nothing bad had happened yet, but something felt wrong. They had no way to flag this early, so they just stayed alert. We counted these rides as "safe" because nothing was reported. But our users didn't feel safe.
Safety and security concerns
Few women want to be drivers
Lack of benefits
Required hours incompatible with other commitments
Lack of financial resources/access to buy/rent a vehicle
Being a driver is not a suitable job for a woman
Driving car is typically done by men in my culture
In your opinion, why do more women not drive with Grab?
Scope 1
Scope 2
Scope 3
Discovery
Starting with women drivers
“Safety remain the biggest barrier for women to sign up for Grab”
Our research revealed something important: safety fears stopped women from becoming drivers. In 2024, only 5% of our drivers were women.
We saw an opportunity: if we could make driving safer for women, we'd attract more women drivers. A more gender-balanced driver base would create a safer environment for women passengers too.
Deeper Insights
Working backwards from Safe
Before jumping into solutions, I needed to define what "safe" actually meant. Prior to this project, Grab measured safety through a single metric: P1C incident rate — the percentage of rides with critical safety events. But a low incident rate didn't tell us if riders felt safe, if they knew how to get help, or if our safety features were even being used. I unpacked the concept of safety experience and broke it down into three dimensions — what I called the 3A framework. Partnering with our data scientists, I used this framework to investigate safety across the Southeast Asia region.
Digging into the data revealed some big insights. Women drivers weren't just experiencing more incidents. Women were actively managing risk in ways that hurt their income.
Women drivers cancel male passengers at higher rates. The pattern was clear: women drivers cancelled significantly more rides when matched with male passengers. During daytime, cancellation rates were already elevated. At night, they spiked dramatically.
This wasn't random behavior. It was deliberate risk management. But every cancellation meant lost income and hurt their acceptance rating on the platform.
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