Chicago’s recent Accessible Pedestrian Signal rollout, which is reportedly ahead of installation targets while still facing technical gaps, is a useful reminder that urban mobility problems rarely end with installation. At intersections, a device can be present but still fail to deliver the intended safety, accessibility, or traffic performance outcome if timing, placement, maintenance, and network effects are not continuously evaluated.
That same principle applies to congestion management. Cities often know where drivers complain, where queues are visible, or where crash risk is perceived to be higher. The harder engineering question is more precise: which road segments and intersections are actually constraining the network, during which time periods, and what type of intervention is likely to improve performance without simply shifting delay downstream?
This is where bottleneck identification becomes an analytical discipline rather than a field observation exercise.
Ticon’s approach starts with the premise that an urban road network must be evaluated as a system of interacting segments, approaches, intersections, turning movements, signal controls, pedestrian crossings, and temporal demand patterns. A bottleneck is not always the place where traffic appears slowest. It may be the upstream signal with an inefficient split, the left-turn movement that consumes green time, the arterial segment where volume exceeds practical capacity for only 30 minutes each day, or the pedestrian phase that is necessary for safety but poorly coordinated with adjacent intersections.
In the Ticon Methodology, the platform combines permanent and portable traffic detectors, traffic counters, GPS data, connected vehicle data, GIS information, demographics, traffic organization records, events, and related sources. Through cross-verification, filtration, and proprietary processing, Ticon estimates speeds, volumes, and derived performance measures for 95% of roadways. Coverage extends to more than 97% of roads at FRC 6 and above, with 100% time coverage. The resulting resolution is granular enough for operational analysis: road segments can be as short as 35 feet, with an average near 225 feet, and time intervals can be as small as 5 minutes, or in many cases up to 15 seconds.
That resolution matters because urban bottlenecks are spatially and temporally narrow. A corridor may look acceptable when evaluated by daily average speed, yet fail repeatedly between 7:45 and 8:20 a.m. at one approach to one intersection. Similarly, a retail arterial may operate smoothly most of the day but experience recurring saturation during weekend peaks, school dismissal, event traffic, or delivery windows. Averaging over long segments or short field counts can hide precisely the locations where intervention is needed.
Ticon’s mobility improvement research, summarized in “Traffic congestion, what works, what doesn’t,” shows why this distinction is important. During the Covid-era traffic restrictions, Ticon studied traffic flows at 126 road intersections across nine U.S. states and analyzed about 200 million data points. In some cases, traffic demand dropped by 30% or more, yet delay reduction on signalized roads was far smaller than the volume reduction. Ticon even observed cases where traffic demand was reduced by almost half with almost no change in delay.
For engineers, that finding is important. It indicates that congestion is not only a demand problem. It is often an organization problem. If intersection control, coordination, phasing, turn allocation, or corridor timing is misaligned with actual demand, reducing volume alone may not produce proportional improvements. Conversely, improving traffic organization can reduce delay without requiring major construction.
Ticon’s experience indicates that up to 50% reduction in travel delay is achievable through signal timing optimization alone. That does not mean every bottleneck can be solved with timing changes. It means that before cities widen roads, restrict access, alter curb use, or add infrastructure, they should first identify whether the limiting factor is demand, control logic, turning movement imbalance, pedestrian phase interaction, lane utilization, or a downstream spillback effect.
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