The recent Cities Today article, “How Coral Gables put AI to work,” reflects a pattern now visible across municipal transportation: cities are moving from isolated technology pilots toward integrated operational intelligence. Traffic management, incident response, public safety, and continuity planning are increasingly being treated as connected functions rather than separate city services.
For transportation engineers, however, the central question is not whether AI or ITS can improve mobility. The question is more precise: which mobility improvement measure should be applied, where should it be deployed, and what efficiency can be expected before the city commits funds? This is where Ticon’s road traffic analytics methodology becomes especially relevant. The value of intelligent infrastructure depends less on the label attached to the technology and more on whether the selected measure matches the actual traffic condition of the corridor, intersection, or network.
Mobility improvement projects are costly and operationally complex. A typical project may involve one contractor selecting locations, another collecting traffic data, another supplying ITS equipment, and another fine-tuning or operating that equipment. Each participant may have its own performance indicators, but the municipality and the local community ultimately care about one measurable outcome: reduction in travel delay. That reduction translates directly into lower fuel consumption, lower emissions, less time loss, and improved network reliability.
Ticon’s work on mobility improvement emphasizes that this outcome cannot be assumed from technology type alone. Adaptive signal control, time-of-day signal plans, turn restriction changes, lane reconfiguration, access management, and construction-based capacity expansion can all improve operations under the right conditions. They can also fail, or even increase delay, when applied to the wrong bottleneck.
That is why Ticon separates two technical tasks that are often conflated. The first is selection of effective mobility improvement measures. The second is pre-assessment of expected efficiency before implementation. Together, they form an evidence-based workflow for deciding whether a problem can be solved through control optimization or whether a more structural intervention is required.
Ticon’s methodology is built around a high-resolution traffic dataset with 100% temporal and spatial coverage for the area under study. In its traffic data collection and analysis methodology, Ticon reports coverage of more than 97% of roads with functional road class 6 and higher, combining information from permanent and portable detectors, traffic counters, GPS data, connected vehicles, GIS, demographics, traffic organization, events, and other sources.
After cross-verification, filtration, and proprietary processing, Ticon produces estimates of speeds, volumes, and derived traffic performance metrics for about 95% of roadways. The platform supports very fine spatial and temporal analysis, including short road segments and time intervals down to minutes, and in some cases finer than that. In Ticon’s mobility analysis materials, road segments can be represented as short as 10 meters, with an average segment length of about 40 meters.
This matters because many mobility failures are local and time-specific. A corridor may look acceptable on a daily average, while a single approach at one intersection repeatedly saturates between 4:45 p.m. and 5:30 p.m. A retail-heavy arterial may show weekend patterns that differ from weekday commuter behavior. A school zone, stadium, hospital, or construction diversion may create a recurring pattern that would be invisible in a conventional count program.
Ticon’s TrafficZoom and TrafficScope tools were developed to expose those conditions. They cover speed and volume analysis, saturation analysis, all types of roads including highways, arterials, major intersections, major roads, and neighborhood streets, high-granularity periods within a year, and performance indicators describing how the network accommodates traffic demand.
A common weakness in mobility planning is to begin with a preferred solution. A city may consider adaptive signal control because it is visible, modern, and marketable. Another may prioritize widening because congestion appears severe. But the correct first step is to rank the network by the engineering parameters that determine delay formation.
Ticon’s virtual transportation model can generate numerical rankings of road sections by traffic delay, travel time delay, saturation degree, total driver time loss, level of service, and network bandwidth utilization. These metrics allow engineers to distinguish between three different cases.
In the first case, a corridor has unused capacity but poor signal coordination, poor phase allocation, or mismatched timing by period of day. In this situation, signal timing optimization or a multi-regime time-of-day plan may deliver large gains without construction.
In the second case, the location is near saturation during limited periods. Here, targeted measures such as turn movement management, phase sequence adjustment, local storage improvements, or selective adaptive control may be justified, but only if the expected benefit is concentrated enough to justify the cost.
In the third case, demand exceeds practical capacity for extended periods. In this condition, certain ITS measures may only redistribute queues or shift delay to adjacent intersections. Construction, access redesign, demand management, or network-level routing changes may be necessary.
Ticon’s congestion research makes this distinction concrete. In “Traffic congestion, what works, what doesn’t,” Ticon analyzed traffic flows at 126 signalized intersections across nine U.S. states, using about 200 million data points during the COVID-era traffic demand reduction. The study observed demand decreases of 30% or more, and in some cases, traffic demand fell almost by half, yet traffic delay did not always fall proportionally. In some locations, delay changed little despite the traffic volume reduction. The engineering implication is clear: congestion is not only a volume problem. It is also a traffic organization problem.
The cost,
Join the network to access Ticon case studies and to receive Ticon newsletters
@2023 Ticon
