Finding the Urban Bottlenecks Before They Become Capital Projects

Capriotti’s recent announcement of 30 new development deals and 12 new restaurant openings is, on its surface, a retail growth story. But for transportation planners, it also reflects a familiar urban pattern: new commercial nodes appear along already busy corridors, trip generation shifts, turning movements intensify, and congestion often materializes in places that were not previously considered critical.

That is where the transportation question begins. A new restaurant, shopping center, school, warehouse, or mixed-use development rarely creates congestion by itself. More often, it changes how demand loads onto a network that already has fragile points: a left-turn pocket that fills too early, a signal phase that cannot clear queues, a driveway too close to an intersection, or an arterial segment where recurring delay spreads upstream. Bottleneck identification is the discipline of finding those points with enough precision to decide whether an intervention is needed, and what kind.

Ticon’s work starts from the premise that congestion is not only a volume problem. It is a network performance problem. In Ticon’s research on mobility improvement, the company examined traffic flows at 126 road intersections across nine U.S. states, analyzing about 200 million datapoints. The COVID-era reduction in demand created a natural experiment: traffic demand fell by 30 percent or more in many locations, yet delay on signalized roads often fell by much less. In some cases, traffic demand was reduced almost by half while delay barely changed. The implication is important for cities: reducing demand alone does not necessarily remove a bottleneck if signal timing, saturation, lane utilization, access geometry, or network coordination remains poorly matched to real traffic behavior.

This is why bottleneck identification requires more than a count at one location. Ticon’s methodology combines permanent and portable traffic detectors, traffic counters, GPS data, connected vehicle data, GIS information, demographics, traffic organization, event information, and other sources into a high-resolution traffic performance dataset. The platform provides more than 97 percent coverage of roads at functional road class 6 and above, with 100 percent temporal coverage. After cross-verification, filtration, and processing through Ticon’s proprietary algorithm, the system estimates speeds, volumes, and related traffic performance measures for 95 percent of roadways, including short road segments as fine as 35 feet, with an average segment length of about 225 feet. Time resolution reaches up to 5-minute intervals, and in many cases up to 15 seconds.

For urban bottleneck analysis, that resolution matters. A corridor may appear acceptable at the daily or hourly level while failing during a narrow 20-minute arrival pulse. A retail entrance may operate smoothly most of the day but interfere with arterial progression during the evening peak. A signalized intersection may have sufficient theoretical capacity, yet one approach may saturate because turning demand is concentrated in a specific 15-minute period. Ticon’s tools, including TrafficZoom and TrafficScope, are designed to expose those conditions through speed and volume analysis, saturation analysis, automated level-of-service calculation, street and intersection performance ranking, cumulative delay estimation, and Network Bandwidth Utilization.

The practical result is a shift from asking, “Where is congestion visible?” to asking, “Where does congestion form, how does it propagate, and which intervention is likely to improve network performance?” Those are different engineering questions. A queue observed at one intersection may originate downstream. A low-speed segment may be a symptom of an upstream signal cycle. A bottleneck may appear only on Fridays, during school release, during seasonal retail peaks, or when a nearby event changes arrival distributions. Ticon’s year-round observation is especially relevant here because seasonal traffic fluctuations can exceed 50 percent, and adjacent streets can show different seasonal patterns. Relying on a nearby counter can introduce large uncertainty: Ticon’s documentation notes that using nearby traffic counts for AADT or volume estimation, even at a 1-mile distance, can lead to errors from 30 percent to 150 percent.

For intervention planning, the key is not simply ranking roads by delay. It is diagnosing whether the bottleneck is controllable through operations or whether it requires geometric or capital improvement. Ticon’s mobility workflow explicitly supports that distinction. The platform helps municipalities determine critical road sections, evaluate whether capacity can be increased through control optimization or local measures, identify where major construction may be required, prioritize projects, measure ITS performance, and tune deployed equipment after implementation.

This distinction is also financial. Adaptive signal control technology can be valuable, but Ticon’s research cautions that it is not appropriate for every saturated condition. Typical ASCT costs are in the range of $40,000 to $55,000 per average intersection. Across the United States, only about 150 ASCT installations were added at intersections over a recent 10-year period, compared with approximately 355,000 signalized intersections nationwide. If a city cannot identify which intersections will benefit most, investment may be diluted or, in some cases, may not improve delay where saturation dynamics require a different strategy. Ticon’s before-and-after analysis capabilities are intended to show not only whether a project improved a corridor overall, but also which segments and time slots degraded and need adjustment.

Traffic signal timing remains one of the clearest examples of why bottleneck diagnosis should precede construction. Based on Ticon’s experience, travel delay reductions of up to 50 percent can be achieved through signal timing optimization alone. That does not mean every corridor can be improved by 50 percent, nor that signal timing solves every bottleneck. It means that cities should first quantify whether the limiting factor is operational. If saturation analysis shows unused capacity that can be recovered through phasing, coordination, time-of-day plans, or progression adjustment, a lower-cost intervention may deliver measurable improvement before widening or reconstruction is considered.

Intersection-level demand is central to this process. In the white paper Ticon Turns: Verification of Accuracy, Ticon describes a proprietary turning movement estimation algorithm based on multivariate analysis of GIS, traffic events and management, demographics, connected vehicle information, location-based services, traffic detection, GPS and navigation data, and traffic organization. The output estimates each turning movement for each 15-minute period over 24 hours, with aggregation available by day of week, weekday versus weekend, month, season, year, peak period, or off-peak period. In a Maine validation study comparing Ticon estimates with portable detector measurements at two three-leg and two four-leg intersections, discrepancies were within 7 percent to 22 percent. The study also notes that short-term measurements themselves can shift by approximately plus or minus 25 percent, which is why continuous observation provides a stronger basis for planning.

That level of turning movement detail is essential when new commercial development changes how drivers enter and exit a corridor. A restaurant opening may add modest total volume but a concentrated right-turn demand at lunch, a left-turn demand during the evening peak, or queues that block a through lane. A shopping center may not overwhelm a road in annual average terms, but it can intensify directional flows at one driveway or produce weekend peaks that conventional weekday counts miss. Ticon’s ability to estimate turning movements at 15-minute resolution helps engineers distinguish a true capacity deficiency from a poorly timed phase, an access management issue, or a localized storage problem.

The same logic applies to AADT and baseline demand estimation. In AADT Estimation by Various Methods: Accuracy and Reliability, Gregory Brodski and Alex Chaihorsky describe Ticon as a Traffic Information Consolidator, combining traffic engineering methodology, multivariate analysis, and large-scale traffic information sources. The evaluation covered Georgia, Nevada, and California, beginning with 695 counting points and retaining 637 after excluding detector errors and GPS data gaps. Because most points were bidirectional, the algorithm produced more than 1,200 estimations. The paper reports that Ticon can keep expected AADT estimation error within 20 percent boundaries at a 90 percent confidence level, with acceptable results generally possible when penetration exceeds 1 percent. For bottleneck identification, this matters because poor volume estimation can misclassify both the severity and the cause of congestion.

A well-structured bottleneck assessment therefore moves through several layers. First, it establishes baseline demand and temporal patterns across the network. Second, it identifies where speed deterioration, queuing, and cumulative delay recur. Third, it examines saturation and level of service by segment, approach, and time interval. Fourth, it evaluates turning movements and access interactions at intersections and driveways. Finally, it tests whether the bottleneck is likely to respond to signal timing, time-of-day coordination, access management, lane assignment changes, ITS deployment, or physical reconstruction.

The most important engineering insight is that bottlenecks are not always where congestion is most visible. They are where network capacity, demand timing, control logic, and driver behavior first fall out of alignment. By combining high spatial coverage, continuous temporal coverage, saturation analysis, turning movement estimation, and before-and-after performance monitoring, Ticon gives planners a way to identify those points before committing public funds to the wrong intervention.

As cities absorb new commercial growth, changing commute patterns, freight demand, and redevelopment pressure, the ability to rank bottlenecks empirically will become a core planning capability. The goal is not to eliminate every queue. The goal is to know which queues indicate correctable inefficiency, which indicate structural capacity limits, and which interventions will produce measurable improvement for the corridor and the wider network.