INRIX Signals Scorecard Turns Connected Vehicle Data into Traffic Signal Benchmarks

INRIX Signals Scorecard Turns Connected Vehicle Data into Traffic Signal Benchmarks

Traffic signals rank among the most heavily used and least scrutinised assets on the road network. Agencies commit substantial sums to installing, timing and maintaining them, yet the instruments available to judge whether that spending genuinely improves journeys have long been tethered to hardware fixed at individual junctions.

The result is a persistent blind spot, because a city can retime a corridor or commission an adaptive system, but proving the effect across an entire network, and comparing it against neighbouring authorities, has remained awkward and costly. That gap between investment and demonstrable outcome frames the launch of the INRIX Signals Scorecard, released in July 2026, which grades signal performance across the United States using movement data gathered from vehicles already in traffic rather than sensors mounted at the roadside.

The report’s central figure offers the sector a shared reference point. Across the signals examined, INRIX puts the average control delay at 20.1 seconds for each visit to a signalised intersection, a measure that captures the time a driver loses decelerating, queuing and accelerating back to speed because of the signal. On its own, a national average is a blunt instrument, and its value lies in what it enables rather than in the number itself. For engineers, planners and the contractors who deliver signal schemes, the more consequential development is methodological, since deriving performance from anonymised connected-vehicle data allows a whole network to be assessed without the detector loops, cameras and controller upgrades that conventional performance measurement depends upon.

That distinction matters commercially as much as technically. Signal timing has always been one of the cheapest ways to buy capacity out of an existing road, but agencies have struggled to direct constrained budgets towards the corridors where the returns are greatest, and to evidence those returns afterwards. A consistent, network-wide benchmark reshapes that conversation, giving transport authorities a means to rank problems, target spending and report outcomes in terms that finance directors and elected members can follow.

Briefing

• The INRIX Signals Scorecard analysed 276,880 traffic signals across all 50 US states and the District of Columbia, establishing an average control delay of 20.1 seconds per signal visit as a national baseline.
• Performance is derived from anonymised, aggregated connected-vehicle data drawn from passenger cars, commercial fleets, delivery vehicles and mobile devices, rather than from intersection hardware.
• The Scorecard evaluates average intersection delay, peak periods, arrival on green, level of service and quarterly trends, allowing agencies to compare corridors and benchmark against peers.
• Signal retiming remains among the highest-return interventions in transport, with the US Federal Highway Administration citing benefit-cost ratios that can reach 40 to 1.
• The launch lands in an intelligent transportation systems market estimated at between roughly 34 and 58 billion US dollars in 2025, within which traffic management is the single largest application segment.

A National Baseline For A Long-Standing Blind Spot

The premise behind the Scorecard is that agencies have never lacked ambition on signal operations, only the ability to see their whole network at once. Individual intersections can be studied in detail, and well-instrumented corridors can be monitored closely, but the moment an authority tries to compare hundreds or thousands of junctions on a like-for-like basis, the exercise runs into inconsistent coverage and incompatible data.

Built on the INRIX Signals Analytics platform, the report packages network-scale metrics into an executive-ready format that quantifies corridor performance, flags where improvement is possible and tracks how conditions change over time. The five measures it reports, average intersection delay, peak periods, arrival on green, level of service and quarterly trends, are the vocabulary traffic engineers already use, which lowers the barrier to acting on the findings.

Steve Remias, Head of Product, Signals for INRIX, framed the problem in terms of accountability rather than technology. “Cities invest millions of dollars in signal timing, adaptive systems, and corridor improvements, but many lack an efficient way to prioritize improvements on their entire network and measure the results,” he said. That observation cuts to why a benchmark of this kind has commercial weight: capital committed without a way to demonstrate its effect is capital that is harder to renew at the next budget round. According to Remias: “The Signals Scorecard provides an easy-to-understand benchmark that helps agencies identify opportunities, measure outcomes, and communicate the value of traffic signal investments to stakeholders.” The emphasis on communication is deliberate, because the audience for signal performance data increasingly includes treasurers, auditors and the public, not only operations teams.

For context on the trajectory, INRIX’s earlier national analysis in 2022 measured performance at more than 240,000 signals and reported an average of just over 18 seconds of delay per signal visit. The two figures are not a clean like-for-like comparison, given differences in coverage and method, but they point to a consistent picture in which everyday signal delay accumulates into a significant national cost. Establishing a repeatable baseline is what allows that cost to be tracked, and what turns a one-off study into a management tool.

Measuring Performance Without The Hardware

The technical heart of the story is the data source. Traditional signal performance measurement leans on automated traffic signal performance measures, commonly abbreviated to ATSPMs, which draw high-resolution data from controllers and detection equipment at the intersection itself. That approach is powerful where it is deployed, but its reach ends at the junctions that happen to be instrumented, and many are not. By reading performance from anonymised connected-vehicle movement instead, INRIX Signals Analytics can assess virtually any signalised corridor, filling the blind spots left by patchy hardware coverage and allowing systemwide evaluation even where detection is inconsistent or absent.

This is not a rejection of intersection instrumentation so much as a complement to it, and the two data philosophies are converging. The US Federal Highway Administration has actively promoted ATSPM adoption through its Every Day Counts programme, encouraging agencies to move from reactive, complaint-driven signal management towards proactive, performance-based operations. Probe-based analytics extend that same logic to networks that could never justify hardware at every junction.

INRIX has been building out the underlying platform steadily, adding features through 2025 and 2026 that include before-and-after analysis for evaluating the impact of timing changes, delay-trend views that break performance down by time of day and day of week, expanded vehicle observations for greater statistical confidence, and direct application programming interface access so agencies and consultants can pull the metrics into their own dashboards.

The practical consequence is a shift in what a signal study costs and how often it can be repeated. Conventional retiming has traditionally been a periodic, labour-intensive exercise, with engineering guidance recommending that timing plans be refreshed every few years and typical retiming running into several thousand dollars per signal.

Continuous, hardware-free measurement lowers the cost of simply knowing how a network is performing, which in turn makes it feasible to check the effect of an intervention shortly after it is made rather than waiting for the next scheduled study. That cadence is what separates ongoing performance management from the older model of occasional, project-by-project optimisation.

The Economics Of Better Signal Timing

If the method is the technical story, the economics are the commercial one, and here the case for signal work is unusually strong. The Federal Highway Administration has long characterised signal retiming as one of the most cost-effective actions an agency can take, citing benefit-cost ratios that can reach 40 to 1, and documented projects have recorded ratios higher still once fuel, time and emissions savings are counted.

Compared with widening a road or rebuilding a junction, retiming buys throughput at a fraction of the capital cost and on a far shorter timescale. The constraint has rarely been the return on the intervention; it has been the difficulty of identifying which corridors merit attention first and proving the benefit afterwards, which is precisely the gap a network benchmark addresses.

That efficiency argument carries added force in a period of tight public budgets and high construction-cost inflation. When concrete-heavy schemes become more expensive to deliver, the relative appeal of software-defined improvements rises, and signal optimisation sits squarely in that category. An authority that can rank its worst-performing corridors on objective data, direct a modest retiming budget at them and then show the delay reduction has a far more defensible spending case than one relying on anecdote or resident complaints. For the contractors, consultants and technology suppliers who serve this market, a common performance language also makes it easier to scope work and demonstrate results, which tends to shorten procurement cycles.

The environmental dividend reinforces the financial one. Idling vehicles waste fuel and generate avoidable emissions, and INRIX’s earlier national work estimated that shaving a single second from delay per vehicle across the country would eliminate more than 1.5 million metric tons of carbon dioxide and save close to 3.9 million barrels of oil each year.

The platform now includes a green calculator that translates timing improvements into fuel and emissions terms, which matters at a moment when transport authorities are under pressure to evidence progress against decarbonisation commitments. Signal optimisation is one of the few levers that reduces congestion and emissions together, without new construction, and does so within existing operational budgets.

A Widening Market For Mobility Intelligence

The Scorecard also needs to be read as a positioning move within a fast-growing analytics market. Estimates of the intelligent transportation systems sector vary by scope and analyst, ranging from around 34 billion to 58 billion US dollars in 2025, but the direction of travel is consistent, with forecasts pointing to sustained growth into the next decade.

Within that total, traffic management is repeatedly identified as the largest single application, and the fastest structural change is the move from hardware-centric deployments towards cloud-based, data-driven services. Agencies are increasingly treating the data generated by connected vehicles as an asset to be analysed rather than a by-product to be ignored, and suppliers are competing to turn that data into decision-ready insight.

This is where a benchmark report earns its keep commercially. By quantifying a national problem and offering a state-by-state breakdown, INRIX establishes its Signals Analytics platform as a reference point for public-sector buyers weighing how to spend limited operations budgets.

The choice to publish an accessible scorecard, rather than to keep the analysis behind a subscription alone, broadens awareness among the municipal and regional authorities that make up the customer base. It also reflects a wider pattern in which non-traditional entrants, firms whose value lies in analytics and software rather than physical assets, are capturing a growing share of transport spending.

Recurring data and software revenue, tied to long-term relationships with agencies, is a more durable business than one-off hardware sales, and benchmarking is an effective way to seed those relationships.

What It Means Beyond The United States

Although the Scorecard is a US dataset, the method behind it travels well, and that is where its relevance to international readers lies. Connected-vehicle and mobile-device data are generated wherever modern vehicles operate, so the same hardware-free approach to measuring signal performance is applicable to authorities in the United Kingdom, Europe and beyond, many of which face identical constraints of ageing detection, uneven coverage and limited engineering capacity.

For agencies wrestling with congestion, air-quality obligations and net-zero targets, a low-cost way to see across an entire signalised network, prioritise interventions and prove their effect is directly transferable, whatever the jurisdiction.

The broader significance is a change in how the industry thinks about signal operations, from a set of periodic engineering projects to a form of continuous asset management underpinned by objective data. That shift favours agencies willing to adopt performance-based management, and it rewards suppliers that can deliver credible, transparent metrics at scale.

As probe data grows richer and analytical tools mature, the expectation that signal spending should be justified and measured like any other infrastructure investment will only harden. The authorities that build that discipline early, and the technology partners that equip them to do it, are the ones most likely to set the terms for the next phase of intelligent traffic management.

Key Industry Questions

1. What is control delay, and why does the 20.1-second figure matter? Control delay is the portion of a driver’s delay caused specifically by the traffic signal, encompassing the time lost decelerating on approach, waiting in the queue and accelerating back to running speed once the light turns green. It is a standard traffic-engineering measure because it isolates the signal’s contribution from other sources of delay. The 20.1-second national average reported by INRIX matters less as an absolute number than as a repeatable baseline. Once an agency knows the typical delay across its network, it can identify corridors performing well above that level, target them for retiming and then measure whether the intervention has closed the gap, turning a single statistic into an ongoing management benchmark.
2. How can connected-vehicle data measure signal performance without sensors at the intersection? Modern vehicles and mobile devices continuously generate anonymised location and speed observations as they travel. By aggregating billions of these observations, analysts can reconstruct how vehicles behave as they approach, wait at and clear a signalised junction, inferring metrics such as delay and arrival on green from the movement itself. Because the data comes from the traffic stream rather than fixed equipment, coverage is not limited to instrumented intersections, which is the principal advantage over hardware-dependent methods. The trade-off is that probe data reflects a sample of total traffic, so statistical confidence depends on observation volume, which is why suppliers have prioritised expanding the underlying dataset to strengthen reliability.
3. How does probe-based analysis compare with traditional ATSPM systems? Automated traffic signal performance measures draw high-resolution data directly from controllers and detectors, giving detailed, near-real-time insight at the specific intersections where they are installed. Connected-vehicle analytics offer broader reach, covering junctions that have no instrumentation at all, but rely on a sample of passing vehicles rather than a complete count. In practice the two approaches are complementary rather than competing. ATSPMs suit high-priority corridors that justify hardware investment and require granular diagnostics, while probe-based benchmarking suits systemwide screening, prioritisation and before-and-after evaluation across networks too large to instrument fully. Many agencies are likely to blend both, using network analytics to decide where deeper, hardware-based study is warranted.
4. What is arrival on green, and why do engineers track it? Arrival on green is the proportion of vehicles that reach an intersection during the green phase and therefore pass through without stopping. It is a useful indicator of how well a corridor’s signals are coordinated, because good progression allows platoons of traffic to move through successive junctions with minimal stopping. A low arrival-on-green figure suggests vehicles are frequently meeting red lights, which points to poor coordination or outdated timing plans. Tracking the measure across a network helps engineers spot corridors where retiming would deliver the greatest reduction in stops, delay and emissions, and it provides a clear, intuitive metric for demonstrating improvement after changes are made.
5. How cost-effective is signal retiming compared with building new capacity? Signal retiming is consistently ranked among the most cost-effective interventions in transport. The US Federal Highway Administration has cited benefit-cost ratios that can reach 40 to 1, and individual documented projects have recorded even higher returns once time, fuel and emissions savings are included. The reason is straightforward: retiming extracts more capacity from existing infrastructure without major construction, at a fraction of the cost and in a fraction of the time of widening a road or rebuilding a junction. The historical obstacle has been prioritisation and proof, since agencies lacked an efficient way to find the corridors most in need and to evidence the benefit, which is the gap network benchmarking is designed to fill.
6. Can transport authorities outside the United States use this approach? Yes, because the underlying data is generated wherever connected vehicles and mobile devices operate, the hardware-free method is not specific to any one country. Authorities in the United Kingdom, Europe and elsewhere face similar challenges of ageing detection equipment, inconsistent coverage and limited engineering resources, and the same probe-based analytics can provide network-wide visibility without new roadside installation. The specific INRIX Signals Scorecard is a US dataset, but the platform and methodology behind it are applicable internationally. For agencies managing congestion and pursuing air-quality and net-zero goals, a low-cost way to benchmark an entire signalised network and prove the effect of interventions is directly relevant regardless of jurisdiction.
7. What does the Scorecard mean for procurement and investment decisions? For public buyers, an objective performance benchmark strengthens the business case for signal-operations spending by allowing corridors to be ranked on data and outcomes to be measured afterwards, which makes budget requests more defensible. For the wider supply chain, a shared performance language simplifies scoping and helps demonstrate results, which tends to shorten procurement cycles. For investors, the growth of analytics-led services signals an expanding addressable market within intelligent transportation systems, where recurring software and data revenue tied to long-term agency relationships is more durable than one-off hardware sales. The trend rewards technology providers positioned around data and insight rather than physical assets alone.
8. How does better signal timing support decarbonisation targets? Vehicles idling at poorly timed signals waste fuel and emit avoidable carbon dioxide, so reducing delay cuts emissions directly. INRIX’s earlier national analysis estimated that trimming just one second from delay per vehicle across the United States would eliminate more than 1.5 million metric tons of carbon dioxide and save close to 3.9 million barrels of oil annually. Because signal optimisation reduces congestion and emissions together, without new construction and within existing operational budgets, it is one of the few levers available to agencies under pressure to show progress against climate commitments. Tools that translate timing improvements into quantified fuel and emissions savings make that contribution easier to evidence in reporting.

Strategic Takeaways

1. Connected-vehicle data is shifting signal performance measurement from a hardware-bound, intersection-by-intersection exercise to continuous, network-wide management, and agencies that adopt performance-based operations early will hold an advantage in both efficiency and accountability.
2. A consistent national benchmark reframes signal timing as a measurable, defensible investment, strengthening budget cases at a time when construction-cost inflation is pushing authorities towards software-defined improvements over capital-heavy schemes.
3. Signal retiming’s exceptional benefit-cost ratios, cited by the Federal Highway Administration as high as 40 to 1, remain underexploited largely because of poor prioritisation, a constraint that network analytics is now well placed to remove.
4. The intelligent transportation systems market’s move towards cloud-based, data-driven services favours suppliers built around analytics and recurring revenue, and benchmarking reports are an effective route to seeding long-term public-sector relationships.
5. The method behind the US Scorecard is internationally transferable, giving UK and European authorities a low-cost path to network visibility and a rare lever that reduces congestion and emissions simultaneously.