Hypothesis on making Google Maps more intuitive with road surface condition indicator

The Background

It is beyond any doubts that Google maps have disrupted the way we use maps in mobile. It has revolutionised the way we travel, use location-based content, show live traffic data or use it just for fun. In fact, Google Maps has tremendously increased tourism with more and more travellers preferring to use roads than other means.

Over the years, Google Maps has improved greatly. The advancements in hardware and software have enabled Google to pack in very powerful features. One such feature is the AI-based alternate routes recommendation.

Google Maps, by default, calculates the distance between two points and plots the shortest route possible over the map surface. This is the primary recommended route and highlighted in blue. The secondary alternate routes are displayed in grey. This feature is very helpful in letting the user decide the optimal route based on live traffic data or personal preferences. However, in Indian context, this information is not enough in letting the user decide the best route. There’s a piece of even more important information missing, and it is the condition of the road surface.

The Objective

The objective of this research is to use smartphones as a means to measure and visualise the road surface condition in the map UI by analysing the vibrations observed by the accelerometer.

Tools & Technologies

Google Maps is used as the map service for illustrating the road surface quality and traffic data.

To measure the vibration, an Android app iNVH developed by Bosch is used. This app has the feature to record vibrations in x, y and z dimensions and generates a CSV file that can be exported. And, to represent it graphically, DatPlot desktop tool built by Michael Vogt is used. It accepts the CSV data and plots individual charts for all the 3 axes.

Finding a Solution

The problem can be solved the same way Google Map works out real-time traffic data.

Google analyses road traffic to give us a pretty accurate indicator of the length of a roadblock and the approximate time to clear it. It uses multiple factors like:

  • Road sensors used by local highway authorities
  • Data from taxi fleets
  • Telecom service providers, and
  • Anonymous GPS data from smartphones

How Google computes traffic data using smartphones

The GPS device inside any smartphone, tracks and transmits the location (longitude and latitude) and speed at which the vehicle is moving. Based on the location accuracy, the live position of the vehicle is plotted on the map. Whenever there’s any interruption in the speed of the vehicle, it is marked and sent to Google. As and when more data gets accumulated through crowd-sourcing, Google Maps is able to plot the traffic condition to a pretty accurate rate. Based on the traffic density and average time taken to move across the jam, the navigation segments are coloured amber or red.

The below graphic shows the traffic conditions of a cross-road at different times of the day. It is interesting to note that based on traffic density, the time to cover that route changes dynamically.

Google Maps

How to use smartphones to measure road conditions?

A simple logic can be applied to measure the road surface conditions. The smoother the road surface, lesser the vibration will be observed and rougher the road, more the vibration will be observed in the vehicle and in-turn the accelerometer in the smartphone.

Let’s investigate, if by using the smartphone accelerometer along with the technology used for live traffic data, can we get any insights on road condition?

But first, let’s understand what an accelerometer is and how it works.

What is a smartphone accelerator?

An accelerometer inside a smartphone is an electro-mechanical device which is used to measure acceleration forces like any movement or vibration. This is possible with the motion sensors in accelerometer that can detect any tiny movement or orientation. In our everyday smartphone use, this helps in identifying portrait or landscape orientation, compass direction or even calculate how many steps you’ve climbed today.

The electro-mechanical system also called MEMS (micro-electro-mechanical systems) is made up of Silicon and can move between the sensors inside the housing. Any movement in the smartphone, triggers the Silicon structure to vibrate and generate a capacitance change relative to the frequency and amplitude of movement.

Three of these systems across each x, y and z planes can accurately track the orientation and movement of smartphone and trigger any event inside the phone.

Let's get to work

To start with, let’s observe the vibrations recorded by iNVH app when the vehicle is in parking status. This can be used as a benchmark to compare with the vibration data when the vehicle is moving.

The x-axis represents the time in seconds and the y-axis in each of the 3 graphs, represents the acceleration measured in m/s2. This represents the rate at which accelerometer changes its velocity per unit of time.

The vibration data reveals that there’s very minimal vibration observed in the horizontal planar axis of x and y. The vertical dimension or the z-axis detects maximum vibration.

The maximum and minimum value recorded is +0.733 and -0.4206.

Now, let’s observe the vibrations experienced by the iNVH app when the vehicle is in motion. First, we will examine the vibration on a smoother surface of road and then on a rougher section. The test is carried out for a minute, during which a dashboard camera captures the visuals of the road condition and the app records the vibrations inside the vehicle.

Vibrations from a smoother tarmac

A video of the vehicle moving in a smooth stretch of road. The speed of the vehicle was around 70 kms/hr.

Let’s observe the vibrations experienced by the iNVH app.

The data shows that in the z-axis, the maximum amplitude recorded is +3.815 and the minimum is -3.584.

Vibrations from a rough patch

A video of the vehicle moving on a rough stretch of road. The speed of the vehicle was around 20 kms/hr.
Here’s the data observed by the iNVH mobile app.
The maximum amplitude recorded is +7.642 and the minimum is -6.799.

Examining the vibration data

It is noticeable from the recorded data that there’s considerable variation in the amplitude of vibration observed by the iNVH app when tested on the smoother road surface and a rough patch.

Based on the amplitude displacement, certain threshold value can be set that can categorise any road surface as normal, average or rough.

Vibration data
Note that the iNVH app uses accelerometer and smartphone hardware to measure and analyse the data. The results may vary depending on the type of phone and its variants, its internal sensors and the measurement locations.

Visualising the vibration data

Road type variations

Users preferred Option 4 over others because of its minimalist approach. However, since it uses only colour to differentiate the road types, additional visual cues had to be added for better comprehension by the users.

Additional visual cues

The existing Google Maps colour palette for navigation was extended to include the road condition types.

Google Maps colour palette
The map UI is re-visualised with this new visual representation and guidelines at multiple zoom levels.
Google Maps extended

The UI is now showing traffic data along with the road condition type. Yellow colour denotes average road surface whereas dark grey represents the rough road.

Powered with this new set of insights, Google Map UI looks and behaves way more intuitive than before. In one view, users can get live traffic data along with the road condition which is going to make it easier for users to select the best route.

However, there’s still one more limitation. It is up to the user again to make sense of all the available information and make the right informed decision.

Final hypothesis

Road Health Index

With the Road Health Index value, it would be so much easier to select the optimal route from all the recommended ones. The alternate routes recommendation feature would be an even more powerful and will take map navigation to a totally new level.



  • https://en.wikipedia.org/wiki/Google_Traffic
  • http://labinyourpocket.com/sensors-the-accelerometer/
  • http://www.techulator.com/resources/8930-How-does-smart-phone-accelerometer-work.aspx

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