Managing a road network often means being in a constant race against time.
A traffic control team detects an unusual slowdown and then tries to identify its cause across several separate systems. By the time the problem is confirmed, congestion may already have spread to additional road segments. Drivers face delays, emergency and maintenance services work under pressure, and road operators must respond to a rapidly deteriorating situation.
The problem is rarely a lack of data. Operators collect information from sensors, cameras, incident reports, and infrastructure maintenance systems. The real challenge is turning fragmented data into up-to-date, network-wide insights.
Roads Management Insights (RMI), a Google Maps Platform solution, helps organizations move from reactive monitoring to more proactive infrastructure management and data-driven predictive modeling. By combining historical mobility data with near-real-time information, the solution makes it possible to analyze traffic, identify bottlenecks, and detect anomalies. It also supports better operational and investment decisions.
Why traditional monitoring isn’t enough
Roadside sensors and cameras provide valuable information about specific locations. However, expanding their coverage may require purchasing additional equipment, maintaining it, and integrating more systems. Even organizations with extensive monitoring infrastructure may struggle to combine their data into a consistent picture of how the entire network is performing.
As a result, operations often follow a reactive cycle: detection, analysis, response, and reporting. Proactive management requires road operators to ask different questions:
- Is this type of slowdown typical for this road and time of day?
- Where does recurring congestion begin?
- How do disruptions on one route affect nearby roads?
- Which road segments consistently perform worse than expected?
- Where would maintenance or modernization deliver the greatest benefits?
- Can warning signs be detected before the situation gets worse?
Answering these questions requires both an up-to-date view of current conditions and the right historical context.
What Is Roads Management Insights?
Roads Management Insights is a Google Maps Platform solution for geospatial analytics. Through Google Cloud, it provides traffic and congestion data for selected roads and routes. Depending on the scope of the implementation, data may be delivered as periodic datasets in BigQuery for historical analysis or as near-real-time updates through Pub/Sub to support ongoing monitoring and rapid response.
BigQuery helps organizations analyze historical data and understand how traffic conditions have changed over time.
Pub/Sub provides access to current information about road conditions.
Google Cloud analytics and AI solutions help detect patterns and anomalies, forecast road conditions, and build models that support decision-making.
RMI data may include route geometry, travel times, and segment-level traffic conditions, making it possible to determine where slowdowns or significant delays are occurring. Historical data establishes a baseline for how the network typically performs. This allows operators to assess whether current conditions require action.
In BigQuery, mobility data can be combined with information about road maintenance, incidents, roadwork, weather, planned events, traffic management measures, and infrastructure investments. This integration enables organizations to create automated alerts, dashboards, analytical models, and repeatable decision-making processes.
Analyzing traffic across the entire network
RMI makes it possible to analyze traffic along defined routes and road corridors. When the analysis covers a larger area, the solution helps organizations understand how the entire road network is performing and how problems on one segment affect other routes.
For example, congestion at an intersection may originate several miles upstream, where traffic flows merge. Analyzing a broader area helps determine whether the congestion actually originated at that location or is the result of a problem earlier along the route.
RMI can help identify:
- recurring congestion and underperforming routes,
- bottlenecks affecting traffic on surrounding roads,
- changes resulting from roadwork or infrastructure upgrades,
- locations where an investment could benefit the entire network.
This helps road operators view roads as a single interconnected system, rather than as a collection of separate measurement points.
From traffic analysis to faster action
Analyzing historical data across different times of day and seasons helps identify recurring bottlenecks, unpredictable travel times, event-related congestion, and segments that are particularly vulnerable to disruption.
Understanding these patterns allows organizations to implement traffic management measures earlier, schedule maintenance during periods when it’ll cause the least disruption, and evaluate roadwork arrangements against historical data. Predictive models can also forecast conditions before congestion becomes a serious problem.
Near-real-time data provides current context. A sudden slowdown on a road segment that is normally free-flowing can be detected without waiting for a predefined threshold to be exceeded or for an incident to be reported.
Combining this signal with information about weather, roadwork, events, incidents, sensors, and maintenance activities gives teams more time to analyze the situation, coordinate their response, inform drivers, and adjust traffic management measures.
Supporting improvements in road safety
Crash records show where incidents have occurred. Mobility data, however, can reveal unusual slowdowns, unstable traffic flow, and recurring patterns that may indicate an elevated level of risk.
RMI can help identify:
- locations where traffic conditions may indicate frequent, sudden slowdowns,
- routes where traffic flow becomes unstable under certain conditions,
- areas that regularly experience both congestion and a high number of road incidents,
- segments where targeted measures could improve safety and traffic flow.
Better maintenance and infrastructure investment planning
Infrastructure investments require difficult decisions. Budgets are limited, roadwork causes disruption, and the outcome of a project may depend on conditions elsewhere in the network.
RMI can support maintenance and modernization planning by helping road operators:
1. Establish a baseline for typical road conditions.
2. Prioritize areas where recurring problems have the greatest impact on the network as a whole.
3. Choose periods when maintenance and roadwork will cause the least disruption.
4. Compare network performance before and after a particular measure is implemented.
5. Use the observed results to make better decisions about future investments.
This creates a closed feedback loop in which each intervention provides insights that help improve the planning of future measures. Operators no longer have to rely primarily on assumptions or isolated measurements. They can evaluate how completed projects affect the real-world performance of the entire network.
Abertis puts proactive management into practice
Abertis, a global transport infrastructure operator, is testing Roads Management Insights on Barcelona’s C-32 highway and across the network managed by A4 Holding in Italy.
As part of its Future Road Lab initiative, Abertis combines RMI with BigQuery and Google Cloud analytics tools. This makes it possible to identify traffic patterns, detect unusual conditions, and develop predictive models. The solution enables faster responses to recurring congestion and disruptions, as well as better-informed decisions about traffic safety and efficiency.
The project demonstrates that proactive management requires both high-quality mobility data and specialized knowledge of roads, their users, regulations, and operational constraints.
More sustainable mobility
Better traffic management isn’t only about reducing travel times.
Stop-and-go traffic, idling engines, recurring bottlenecks, and poorly coordinated roadwork can increase fuel consumption and pollutant emissions. By identifying inefficient patterns and enabling earlier action, RMI can support more sustainable mobility.
Analyzing historical and near-real-time data can help road operators:
- optimize traffic flow,
- reduce disruption during roadwork,
- examine the relationship between congestion and emissions,
- assess whether infrastructure investments deliver lasting efficiency improvements.
From reporting to proactive management
Roads Management Insights supports the transition:
- from isolated measurements to a network-wide understanding,
- from reporting incidents after the fact to providing earlier warnings,
- from reactive responses to precisely targeted interventions,
- from assumption-based planning to data-driven decision-making.
By combining mobility data with operational information in BigQuery, road operators can make better-informed decisions that improve safety, reduce congestion, support infrastructure investment, and contribute to more sustainable mobility.
Contact us to learn how Roads Management Insights can help your organization move from reactive monitoring to proactive road infrastructure management.