From Data to Decisions: Why Automation is the Key to the Future of Mobility

Urban mobility plays a crucial role in the development and success of today's cities. Optimizing available resources and infrastructure is becoming increasingly important as populations grow and cities expand.

This is where traffic and vehicle analytics come in—providing valuable data insights can aid in developing cost-effective policies and infrastructure investment decisions for cities.

Historically, capturing these analytics has been a challenging and lengthy process, often involving manual methods using humans or static sensors. Recently, there has been a push towards the complete automation of the capture of these analytics to deliver ground truth studies quickly and safely.

Understanding Urban Mobility Analytics

Traffic and vehicle analytics are focused on understanding how people move in urban areas.

These insights inform planning and development decisions, help improve safety, and reduce congestion. Collected data types that affect development and planning include:

• Vehicle class & count
• Vehicle speed and acceleration
• Cyclist and pedestrian count
• Travel time between destinations
• Environmental factors (air quality, noise levels, weather conditions, etc.)

Using this data correctly, cities can better understand how people move within their confines and make informed decisions on optimizing their infrastructure and resources.

The Challenges of Traditional Analytics Capture

Traditionally, these analytics were captured in urban areas using manual methods (seriously, someone sitting on the roadside counting cars) or static sensors. While this approach can yield accurate data, it is often time-consuming and expensive.

Manual methods can be very labor-intensive and require much time to conduct. It is also limited in its analysis capabilities, as the amount of data that can be collected is often heavily restricted.

Static sensors are easier to deploy and collect more data but are limited in accuracy.

The Advantages of Fully Automated Analytics Capture

Instead of relying on traditional methods of capturing traffic and vehicle analytics, fully automated systems provide a more accurate and reliable alternative.

These systems use artificial intelligence and machine learning algorithms to capture real-time data and generate robust data sets safely and efficiently.

Reduced expenses

Automated systems provide a more cost-effective approach to data collection and generation. This is because staff resources are reduced, and it requires less time for maintenance and data interpretation.

Real-time data capture

Automated systems provide a near real-time data stream, allowing for immediate insights and adjustments to policies and infrastructure planning.

Increased accuracy

Automated systems produce more accurate data, as errors in capture are minimized. This results in more sound decision-making in urban planning and infrastructure development.

Comprehensive data capture

Automated systems capture a wide range of data points, providing richer and more nuanced insights than traditional static sensor technology.

Conclusion

Urban mobility analytics provides valuable insights and benefits for cities looking to optimize their infrastructure and resources.

While traditional methods of collecting this data can be costly, time-consuming, and error-prone, fully automated systems provide a more accessible and practical approach. Benefits include greater accuracy, real-time data capture, comprehensive data sets, and cost-effectiveness.

By automating the capture of traffic and vehicle analytics, cities can better understand their communities and populations and make more informed, impactful decisions about their infrastructure.