The recent $5.3 million sale of a fully leased retail strip center in Fontana, California, is not primarily a transportation story. Yet it reflects a pattern that urban traffic engineers see every day: compact commercial sites, quick-service restaurants, grocery anchors, seasonal retail, and EV charging activity can concentrate short, frequent trips at driveway connections and nearby intersections. In fast-growing suburban and urban corridors, that mix can turn an ordinary access point into a recurring operational bottleneck.
For planners, the question is not simply whether a corridor has “high traffic.” The more important question is where traffic demand, turning friction, signal control, and limited downstream capacity combine to create delay. That is where Ticon’s road traffic analytics platform is most relevant: it helps identify the specific road segments and intersections where interventions may produce the greatest mobility benefit.
A bottleneck is often visible to drivers as a queue, but its engineering cause may be less obvious. The source can be an undersized left-turn bay, poorly coordinated signals, driveway friction from retail access, heavy truck movements, school peaks, event traffic, or a saturated arterial segment that prevents upstream intersections from clearing. In many cases, the location where delay is observed is not the location where congestion begins.
Ticon’s mobility research emphasizes this distinction. In the report “Traffic congestion, what works, what doesn’t,” Ticon notes that municipalities often struggle because they lack clarity on “where exactly the ITS equipment should be deployed to ensure maximal impact for overall mobility of the road network.” That is precisely the bottleneck identification problem: agencies need to distinguish between road sections that are merely busy and road sections that constrain the performance of the wider network.
This distinction matters financially. Ticon cites that, as of 2019, congestion-related lost productivity in the United States exceeded $87 billion. Yet broad traffic restriction policies and generalized congestion programs do not necessarily produce proportional reductions in delay. During the pandemic period, Ticon analyzed traffic flows across 126 road sections in nine U.S. states, using about 200 million data points. The study found that reductions in traffic delay on signalized roads were often much smaller than reductions in traffic volume, with some cases showing almost no change in delay even when demand fell by nearly half.
That finding has a clear engineering implication: congestion is not only a demand problem. It is also a control, geometry, saturation, and network organization problem.
Ticon’s methodology is designed to identify bottlenecks at the segment and intersection level, rather than relying on coarse corridor averages. The platform provides more than 97% road network coverage for roads classified as FRC 6 and above, along with continuous temporal coverage. It combines permanent and portable detectors, traffic counters, GPS data, connected vehicle data, GIS information, demographics, traffic organization data, events, and other sources.
Through cross-verification, filtering, and proprietary processing, Ticon estimates speeds, volumes, and related traffic flow derivatives for 95% of roadways. The spatial resolution is detailed enough for short road segments, down to 35 feet in some cases and about 225 feet on average. Time intervals can be as fine as 5 minutes, and in many cases as fine as 15 seconds.
That level of granularity is critical for bottleneck work. A congestion problem at a retail driveway near a grocery anchor, for example, may not appear in a daily average traffic count. It may emerge during a 15-minute lunch period, a Friday afternoon shopping wave, or a charging-related dwell pattern near an EV station. If the analysis is averaged across a mile-long segment or a full day, the operational issue can disappear statistically while still being obvious on the street.
Ticon’s analytical approach includes speed and volume analysis, saturation analysis, automated level-of-service calculation, travel delay estimation, cumulative delay estimation, and street performance ranking. Its TrafficZoom and TrafficScope tools are built to show both the current performance of road sections and how congestion forms over time. This is important because the practical intervention may differ depending on whether a corridor is affected by recurring saturation, poor signal progression, turning movement imbalance, or localized access friction.
AADT remains one of the most familiar traffic engineering metrics, but it is not sufficient for bottleneck diagnosis on its own. A road with moderate AADT can fail during a narrow peak, while a high-AADT arterial may function acceptably if signals, lanes, and turning movements are well organized.
Ticon’s research on volume estimation provides the foundation for more detailed operational analysis. In Brodski and Chaihorsky, “AADT Estimation by Various Methods: Accuracy and Reliability,” Ticon reports that its AADT estimation keeps expected error within 20% boundaries with 90% confidence. The same research cites a median average percentage error of 4.78% and a relative root mean square error of 11.97% for AADT-level estimates.
For bottleneck identification, the more important extension is intraday analysis. Ticon’s “Intraday Traffic Volumes Estimation” white paper explains that congestion management and signal control often require volume fluctuation estimates at 15-minute intervals. Ticon reports that 15-minute traffic flow volumes were estimated with a median absolute percentage error of 11.24% in field study conditions, comparable to a pre-calibrated video detector that showed 6.5% MAPE under the same circumstances. Across more than 200 business-site case studies, Ticon compared 15-minute estimated volumes with detector and DOT-provided measurements, finding that median percentage discrepancy for hourly traffic volumes varied between 5% and 20%, depending on the availability of infrastructure data.
For engineers, these figures matter because intervention decisions depend on whether a location is consistently overloaded or only intermittently constrained. A site access intersection near a grocery-anchored retail center may need modified signal timing, a dedicated turn lane, driveway consolidation, access management, or no capital improvement at all. The right answer depends on the temporal pattern of demand and the actual location of delay formation.
Urban bottlenecks often occur at intersections, and intersections are governed by directional demand. Two locations can have similar approach volumes but different performance if one has a heavy protected left-turn demand, a high right-turn pedestrian conflict rate, or uneven through movement distribution.
Ticon’s turning movement methodology estimates each turning movement traffic demand for every 15-minute period during a 24-hour day. Results can be aggregated by day of week, weekday versus weekend, month, season, year, peak period, or off-peak period. The methodology applies multivariate analysis across GIS, events and management data, demographics, connected vehicle data, traffic organization, location-based services, traffic detection, and GPS/navigation data.
This is directly applicable to bottleneck identification. If congestion forms near a retail pad site, an analyst needs to know whether the main problem is inbound left turns blocking through traffic, outbound queues spilling back into parking aisles, downstream signal progression, or a mismatch between commercial peak activity and commuter peak flow. Turning movement estimation helps separate these mechanisms.
It also helps prevent misdirected investment. Adding general through capacity may not solve a bottleneck caused by turning friction. Conversely, modifying a turn phase may have limited value if the entire downstream segment is saturated. Ticon’s saturation analysis and intersection-level directional flow estimation allow agencies to connect the observed delay to the operational cause.
A common mistake in congestion management is to prioritize the most visible queue rather than the location with the highest network impact. Ticon’s virtual transportation model addresses this by generating numerical rankings of road sections by traffic delay, saturation degree, total driver time loss, and each section’s effect on area-wide mobility.
This ranking capability is essential for intervention planning. A short delay on a high-volume arterial may create more cumulative driver time loss than a longer delay on a lightly traveled local street. Similarly, a saturated intersection at the entrance to a commercial cluster may cause spillback that disrupts signal progression across multiple blocks. In those cases, local improvements can produce corridor-level benefits.
Ticon’s mobility workflow enables municipalities to determine critical road sections, evaluate whether capacity can be increased through signal optimization or other local measures, assess whether construction should be considered, prioritize projects, measure ITS performance, and tune equipment after implementation. The platform also supports network bandwidth utilization analysis, which helps estimate how much of the network’s practical capacity is already being used.
This matters because not every bottleneck requires a large capital project. Ticon’s experience indicates that up to 50% reduction in travel delay is achievable through signal timing optimization alone. At the same time, Ticon’s research cautions that adaptive signal control is not always the best intervention, especially under saturated conditions. The company notes that ASCT deployments can cost approximately $40,000 to $55,000 per average intersection, and that improvement for saturated segments may not always be achieved within an in-cycle adaptive control framework. In some cases, a multi-regime time-of-day operation mode, optimized for observed traffic patterns, may be more appropriate.
The Fontana retail transaction is a small example of a broader urban challenge. Commercial activity, EV charging, quick-service restaurants, grocery anchors, logistics growth, and residential expansion all reshape local traffic patterns. The resulting bottlenecks are not always visible in traditional traffic counts, and they are rarely solved by a single metric.
For agencies and developers, the practical path is to identify where delay forms, when it forms, which movements create it, and how much it affects the wider network. Ticon’s combination of near-complete network coverage, high-resolution temporal analysis, AADT estimation, intraday volume modeling, turning movement estimation, saturation analysis, and performance ranking gives engineers a way to move from congestion observation to intervention priority.
The strongest bottleneck programs do not begin with a preferred construction project. They begin with empirical diagnosis. Once the network reveals which segments constrain mobility, cities can choose targeted measures, such as signal retiming, access management, turn lane changes, ITS deployment, or geometric redesign, with a clearer view of cost, benefit, and operational fit.
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