Telematics Data is Reshaping Our Understanding of Road Networks

In this episode MIT Professor Hari Balakrishnan explains how Cambridge Mobile Telematics (CMT) is transforming traditional road network analysis by layering dynamic behavioural data onto static map geometries.

Telematics data creates "living maps" that go beyond traditional road geometry and attributes. By collecting movement data from 45 million users through phones and IoT devices, CMT has developed sophisticated models that can:

- Generate dynamic risk maps showing crash probability for every road segment globally - Detect infrastructure issues that aren't visible in traditional mapping (like poorly placed bus stops) - Validate and correct map attributes like speed limits and lane connectivity - Differentiate between overpasses and intersections using movement patterns - Create contextual understanding of road segments based on actual usage patterns

Particularly interesting for GIS professionals is CMT's approach to data fusion, combining traditional map geometry with temporal movement data to create predictive models. This has practical applications from infrastructure planning to autonomous vehicle navigation, where understanding the cultural context of road usage proves as important as precise geometry.

The episode challenges traditional static approaches to road network mapping, suggesting that the future lies in dynamic, behavior-informed spatial data models that can adapt to changing conditions and usage patterns.

For anyone working with transportation networks or smart city initiatives, this episode provides valuable insights into how movement data is changing our understanding of road infrastructure and spatial behaviour.

Connect with Hari on LinkedIn!

https://www.linkedin.com/in/hari-balakrishnan-0702263/

Cambridge Mobile Telematics

https://www.cmtelematics.com/

BTW, I keep busy creating free mapping tools and publishing them there

https://mapscaping.com/map-tools/ swing by and take a look!

In this week’s episode, I’m thrilled to welcome back Ariel Seidman, founder of HiveMapper. Ariel was my very first podcast guest back in 2019, and HiveMapper has come a long way since then!

We explore how HiveMapper has evolved from a drone-based mapping system to a cutting-edge platform collecting street-level data at a global scale. Ariel shares the challenges of scaling large-scale mapping efforts, the pivot to building their own hardware, and the role of blockchain-based incentives in driving adoption.

Here are just a few topics we cover:

• Why HiveMapper shifted focus from drones to street-level mapping.
• The power of combining hardware and software to solve mapping challenges.
• How HiveMapper has already mapped 28% of the global road network.
• The revolutionary edge computing and data filtering techniques driving efficiency.
• What it takes to compete with industry giants like Google Maps.

Whether you're fascinated by the intersection of geospatial technology and innovation or looking for insights into scaling impactful startups, this episode is packed with value.

Let me know your thoughts or hit reply if you’d like to discuss the episode!

https://beemaps.com/

Connect with Ariel here https://www.linkedin.com/in/aseidman/

PS

I have just finished creating a web-based tool that lets you explore and download OpenStreetMap data, It is a bit different from other tools and I would appreciate some feedback.

https://mapscaping.com/openstreetmap-category-viewer/

Tracking elephants in Southern Africa’s Kavango-Zambezi (KAZA) region, the largest transfrontier conservation area in the world.

Lead scientist Robin Naidoo from the World Wildlife Fund-US explains the complex, cross-border collaboration required to understand elephant movements across vast landscapes and the role of GNSS.

Connected with Robin

https://www.worldwildlife.org/experts/robin-naidoo

Read more information about this study here