Journey of a Tourist via Grab

Summary

Grab analyzed platform ride data from millions of tourist passengers representing over 150 countries visiting Singapore. Over 60% of these tourist riders originated from Southeast Asia, while non-regional visitors mainly came from China, the United States, and India. Seasonal demand revealed a trimodal distribution for tropical travelers aligning with holiday periods, contrasting with a September-to-January peak for visitors escaping winter in four-season climates. Airport trips showed that nearly 90% of tourist passengers headed directly to hotels, concentrated heavily in central areas like Orchard, Bugis, Downtown Core, and Kallang. Additional key destinations included major shopping districts, iconic dining locations like Newton Food Centre and Chijmes, and medical centers, which saw tourist ride volume grow over 500% between 2015 and 2017.

Context

Public interest surrounding Singapore as a tourist destination following the release of the film Crazy Rich Asians prompted an examination of tourist travel patterns across the city-state.

Approach / What changed

Grab examined ride booking data across millions of international passengers from more than 150 countries to identify visitor demographics, arrival seasonality, and mobility patterns across attractions, shopping malls, dining hubs, and hospitals.

Takeaways

• More than 60% of tourist passengers on Grab originated from Southeast Asia, primarily Malaysia, Indonesia, the Philippines, Vietnam, and Thailand.
• Almost 90% of first rides leaving Changi Airport headed directly to hotels, with 80% clustered in central areas such as Orchard, Bugis, Singapore River, and Downtown Core.
• Hospital and medical center drop-offs for tourist passengers on Grab grew by more than 500% between 2015 and 2017, driven largely by Southeast Asian visitors.

Related reading

• Grab · May 22, 2019

  Tourists on GrabChat!

  Grab examined more than 3.7 million tourist messages across Singapore, Malaysia, and Indonesia sent between December 2018 and March 2019 to evaluate passenger communication patterns. The platform deployed in-house translation and prewritten, auto-translated chat templates to bridge language barriers between international riders and local drivers. Analysis showed that bookings utilizing chat templates experienced a 10% higher ride completion rate than those without. Image-sharing features were most heavily used in high-traffic hubs such as airports, shopping malls, and major tourist centers to aid driver location. Passengers also consistently used messaging to clarify luggage capacity, provide identifiable passenger descriptions, and check pet policies.

  Lara PuReum Yim

• Grab · Mar 20, 2020

  7 Fun Facts about Grab’s Driver-Partners in Singapore

  Grab analyzed ride-hailing metrics from driver-partners operating in Singapore to identify platform usage trends and driving patterns. Findings indicate that drivers have a 1 in 400 chance of encountering a repeat passenger among the 5.4 million population, with Tampines recording the most pickups and Orchard and Marina Bay serving as top destinations in 2018. Driver behavior data shows that partners with over two years of platform experience routinely start shifts an hour earlier and leverage auto-accept features to minimize idle waiting time. Furthermore, drivers are twice as likely to receive back-to-back ride allocations during evening peak hours, resulting in roughly 50% higher hourly earnings. The dataset also highlights customer satisfaction metrics, showing that shared GrabShare rides achieved an average rating of 4.8 stars.

  Lara PuReum Yim

• Grab · Apr 12, 2019

  Guiding You Door-to-Door via Our Superapp!

  Grab addressed passenger navigation challenges at large Southeast Asian venues such as airports and shopping centers. Satellite signals weaken through concrete and steel, creating GPS inaccuracies that caused the rendezvous distance between passengers and drivers at large venues to exceed twice the average. While introducing Entrances previously mapped over 120,000 green dots to lower rendezvous distances, passengers still struggled to locate specific pickup spots indoors. The company launched Venues, an in-app feature delivering turn-by-turn text and photo directions to designated pickup points. To support this system, operations teams surveyed sites with cameras and scanners to capture landmarks, after which in-house teams masked faces and vehicle license plates.

  Neeraj Mishra

• Grab · Feb 20, 2019

  Understanding Supply & Demand in Ride-hailing Through the Lens of Data

  Grab measures ride-hailing supply and demand across space and time to resolve geo-temporal allocation mismatches between moving drivers and ride-seeking passengers. The analytics pipeline defines supply as idle online drivers and demand as passengers checking fares within brief time slots, aggregating locations into geohashes. Each driver is mapped across neighbouring demand units and inversely weighted by straight-line distance, which yields the effective supply, supply-demand ratio, and supply-demand difference for each geographic polygon. Grab uses these aggregated metrics to identify marketplace imbalances, deploying driver heatmaps to shift excess supply and passenger travel trend widgets to defer time-insensitive ride requests.

  Aayush Garg