Here’s why your Google Maps fails during sudden traffic disruptions

Many commuters found themselves at an unexpected standstill on the roads of Delhi on February 13 morning. Their trusted navigator, Google Maps, had led them straight into the path of concrete blocks, barricades, and iron nails — obstacles that had materialised overnight due to the farmers' protest. It was a reminder: Don’t rely on online maps blindly.

Tuesday’s incidents, which disappointed scores of office-goers, were not an anomaly but rather a symptom of a larger issue that users across the world encounter: Google Maps' failure to keep pace with sudden traffic changes.

The platform, which helps people travel over 20 billion kilometers every day globally, has become a lifeline for millions trying to navigate the complex and congested roadways of India. However, its ability to alert users about real-time traffic disruptions, road closures, and navigational changes often falls short.

The question arises: Why does Google fall short in alerting users about real-time traffic disruptions?

UNDERSTANDING GOOGLE MAPS

The core of the problem lies in how Google Maps gathers and processes traffic data. The app relies heavily on GPS and location services, utilising anonymised location data from smartphones to monitor traffic flow and congestion. This data, combined with real-time traffic information aggregated from user reports and external sources like Traffic Management Centers (TMCs), forms the backbone of its traffic predictions.

It also uses data obtained from local transport authorities about changes on the ground, the Waze app – the community-driven navigation platform it bought in 2013, and private companies to improve accuracy and user experience.

Google’s artificial intelligence (AI)-based algorithms and machine learning tech use the vast amount of real-time data, reviews, and user feedback in conjunction with historical data like the speed of vehicles at a particular time on a particular day to guide users and predict future traffic trends, to predict the estimated time and shortest route to reach a destination.

In a blog post in 2022, Google Maps’ vice president and general manager Miriam Daniel said that Google has been "using existing traffic patterns and the power of machine learning” to help cities better optimise traffic systems, including reducing road congestion.

The official said that the Maps’ had partnered with Bengaluru and Chandigarh traffic police. “In partnership with local authorities and data aggregators, we will also be providing information on road closures and incidents on Google Maps across eight cities in India, including Delhi, Hyderabad, Chandigarh, Ahmedabad, Kolkata, Gurgaon, Bangalore, and Agra, to help people make informed travel decisions and avoid congestion zones,” Daniel wrote.

LIMITATIONS OF GOOGLE MAPS

Despite the vast amount of data at its disposal, Google Maps' algorithms can still be caught off guard by sudden traffic changes. The reliance on user reports for real-time updates means that the system's efficiency is directly tied to the responsiveness and participation of its user base. In instances where changes occur swiftly or in areas with a less active user community, the app's ability to promptly reflect these changes is hindered significantly.

Let’s put it in perspective. Google Maps showed wrong routes and traffic information to users in many areas because the platform did not get updates from authorities and the community in time.

There's a notable gap in its direct access to such official data sources in India. This limitation, coupled with the challenges in integrating and verifying the flurry of user reports and external data, contributes to the app's delayed response to unexpected traffic disruptions.