Safer Roads

We are using data to uncover safety challenges and come up with solutions that could save lives. We are looking to emerging technologies to address challenges in transportation, environment, and safety.

Going forward, governments at every level, as well as their private-sector counterparts, will have to revisit data standards, procurement policies, measurement technologies — and privacy and cybersecurity — to maximize road safety.

The challenge, then, is designing policies that facilitate better data sharing between governments, private companies, and citizens. Driving data and its insights can improve city transportation infrastructure and safety.

We seek to leverage advances in AI, machine learning, vision systems, LIDAR, telematics, and other data sources to provide key insights into road safety, crash characteristics, and the prioritization of investments, for all road users.

Hotspot forecasting, which relies on historic data, predicts where crashes are likely to occur, which allows transportation planners to build solutions to help prevent severe injuries and even deaths.

Road infrastructure can be improved to have an immediate, positive impact on driver behavior to save lives.

Building effective and robust infrastructure systems

Through infrastructure analysis, stakeholders can leverage historic and real-time data to shape effective, robust infrastructure systems.

This kind of smart data-driven transportation solution is part of the ecosystem approach.

If, for instance, the driving data shows that a specific corridor is seeing regular substantial increases in traffic, resources could effectively be allocated to research the implementation of a new transit proposal.

Analyzing aggregated, contextualized driving data could take it one step further and give insights about collective behaviors, both typical and atypical.

Aggregated and contextualized driving data moves beyond single data points to reveal areas in which driving behaviors cluster, begin, or cease.

The effect of those changes can then be tracked over time to see whether there is an improvement in near misses. As demonstrated, aggregating and contextualizing driving behavior data can yield actionable insights into our transportation infrastructure.