Welcome to my blog! This is my launch article and it covers what smart motorways are, their more than troubling track record, and, ultimately, how they can be improved to become what they were envisioned to be.

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Smart motorways were designed to be the future of Britain’s road network. Instead of the DfT spending significant amounts of money on widening motorways, engineers could increase capacity whilst leveraging existing infrastructure through the use of technology. This would allow them to vary speed limits, manage congestion, and even open the hard shoulder during periods of heavy traffic. They currently make up 10% of the motorway network. The principle was sound: squeezing as much capacity out of existing carriageways as possible.

Yet after almost twenty years of operation, smart motorways are seen as dangerous by the public. High-profile accidents, delayed safety measures, and patchy technology have left drivers questioning whether the risks are worth it, causing the DfT to suspend all smart motorway conversions.

The concept

Smart motorways are based on two principles: dynamic speed management and flexible carriageway use.

Dynamic speed management

Dynamic speed management is enabled through the use of screens suspended from gantries. Variable speed limits ranging from 40 to 60mph are displayed (with 70mph applying when no restriction is displayed). Information signs are also placed regularly along the carriageway, warning drivers about road conditions, closures and delays. In theory, this flexibility should improve safety as well as capacity, by slowing traffic ahead of incidents and maintaining the momentum of traffic. It also allows for the reduction in noise at certain times by reducing the speed limit, which is done during school hours on certain parts of the network.

Flexible carriageway use

Flexible carriageway use allows for the implementation of dynamic hard shoulder (DHS) and all-lane running (ALR) motorways. With DHS, the hard shoulder becomes an active traffic lane during anticipated periods of high traffic volumes. ALR goes further, permanently converting the hard shoulder into a live lane (this is the controversial one).

Technology underpins Smart Motorways. Stopped Vehicle Detection (SVD) systems, as the name implies, use radar to identify stationary vehicles. These are designed to alert control room staff, who could then close affected lanes.

Data collected from cameras and sensors is also fed into the National Traffic Information Service (NTIS), which provides real-time information to traffic management systems and navigation apps, providing accurate travel time estimates.

So where did smart motorways fail?

The implementation: shortcomings

Stopped Vehicle Detection (or lack thereof)

SVD is arguably the most important technology for Smart Motorways to function. With ALR being the preferred deployment in later years, this technology should’ve been accurate, reliable and operational upon deployment.

When Highways England appeared before the Transport Select Committee in 2016, Chief Highway Engineer Mike Wilson assured MPs that SVD “works” and would be “part of the standard roll-out of smart motorways going forward”, setting expectations that all new ALR schemes would include SVD from day one with existing sections retrofitted.

The reality fell far short. SVD didn’t become standard until 2018, two years late. By 2019, only 18% of ALR motorways had it installed. Full rollout wasn’t completed until September 2022, six years after the original commitment.

This delay had consequences. Without SVD, control room operators relied on CCTV cameras and MIDAS to detect stopped vehicles, a process taking an average of 17 minutes (which Highways England initially claimed was the “longest time” recorded, not the average). That’s 17 critical minutes where stranded vehicles sat exposed in live lanes with no protection or warning signage for other drivers.

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Radar-based SVD sensor

Radar-based SVD, finally installed from 2022, showed dramatic promise: detecting stopped vehicles in around 20 seconds compared to the 17-minute baseline. Yet deployment challenges meant many sections operated for years without this safety net. Former Highways England CEO Jim O’Sullivan acknowledged to MPs in 2019 that “a number of lives would have been saved had the technology been in place sooner.” Even when deployed, problems persisted. Office of Rail and Road reports revealed many installations failed performance requirements, and as late as 2023, one in four SVD installations still failed core performance standards when re-tested.

Refuge area spacing

With a patchy detection system, surely accessing an emergency refuge area (ERA) wouldn’t be difficult?

The original M42 pilot had ERAs spaced at 500-800m apart, with the hard shoulder only opened at peak times. This close spacing was deemed essential for safety and contributed to the pilot’s success. AA President Edmund King noted the M42 pilot “worked quite well”.

Despite this evidence, the specification was changed to 2.5km without consultation, becoming the ALR standard from 2013 onwards. One could say that austerity even got to Smart Motorways… The consequences were predictable: AA research found 32% of drivers would only drive 400m to reach an ERA before stopping in a live lane, with a further 23% willing to travel just 800m. Only 42% of drivers who broke down actually reached an ERA. The majority were left stranded in live traffic.

Following safety concerns, Highways England trialled improvements on the M3 (J2-4a) in July 2017: bright orange surfacing, clearer SOS signage, and distance countdown markers. Though ERA spacing remained at 2.5km, the trial was successful and has been rolled out network-wide.

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An enhanced ERA being installed on the M3

In March 2020, Transport Secretary Grant Shapps admitted ERAs “were spaced way too far apart” and committed to maximum 1.6km spacing, ideally 1.2km where feasible. A £390m programme to retrofit 150+ additional ERAs to existing ALR motorways was completed by April 2025.

Highways England claims ERAs are safer than hard shoulders. They’re wider, set back from traffic and protected by barriers. But this is misleading: they’re only safer if reachable. With ERAs at 1.5km spacing, a driver could be 750m from the nearest refuge. At 70mph, just 24 seconds of warning before mechanical failure, assuming the driver knows an ERA is coming and their vehicle cooperates. With a continuous hard shoulder, reaching safety requires zero advance warning. Even if a driver reaches an ERA, they still depend on control room operators spotting them to close the lane, a process that, without SVD, took an average of 17 minutes.

Operations

Smart infrastructure requires smart resourcing; instead, the network expanded far faster than the people managing it.

“The number of staff in the [control] room has always been woefully low”, an unidentified former control room operative told ITV News Meridian. Andrew Morgan testified to the Transport Select Committee that the East Midlands Control Room had identical staffing levels in 2008 and 2021, despite massive workload increases from ALR schemes, describing the safety of the roads as “questionable”. During peak pressure (7:30-11pm roadworks setup), the workload became “unbearable, things can get missed”. National Highways claimed a 22% increase in operators (308 to 382), but this hasn’t kept pace with network expansion.

So, the control rooms are chronically understaffed. What about the technology they’re using?

In March 2020, BBC SouthEast reported Highways England admitted not all CCTV screens could be observed at all times. The agency refused to confirm or deny this, but the technical reality supports it: Pan-Tilt-Zoom (PTZ) cameras can only look in one direction, requiring manual operation. With chronic understaffing, continuous monitoring across hundreds of miles becomes physically impossible.

Equipment reliability compounded the problem. Morgan testified that two M6 Matrix signals remained faulty for two years after he identified them. CCTV quality was “often unacceptable”: foggy lenses, poor positioning, faulty cameras even in early weeks of new ALR sections. Maintenance was “at best inconsistent”. FoI requests revealed ongoing CCTV service outages on M25 sections, with fault logs showing communication link failures between cameras and Regional Control Centres.

Even when technology worked, closing lanes introduced delays. Control rooms must manually verify and set Red X signs, with no direct communication line for recovery operators and no mandated response times. Recovery operators report difficulties getting protection, whilst emergency services report similar delays.

But even set Red X signs only work if drivers obey them. Legislation enabled camera-based prosecution in June 2019, but enforcement didn’t begin until 2021, two years later. Only half of HADECS 3 cameras were operational then, with full upgrade not completed until July 2023, four years after the law changed. Between 2021-2023, over 53,000 motorists were caught ignoring Red X signs, and those were only from limited operational cameras. National Highways claims “above 90% of drivers” observe Red X signals, but the reality for stranded drivers is stark: their safety depends on other drivers obeying a sign that went unenforced for years.

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Drivers ignoring a Red X

The cascading failure: understaffed control rooms struggle to monitor all cameras, equipment failures mean missed incidents, delays in setting Red X signs leave stranded vehicles exposed, thousands of drivers ignore Red X signs when set, and recovery operators work in live lanes whilst negotiating for basic protection. A situation Highways England says should never occur. Yet evidence to the Transport Select Committee and multiple inquests shows it has happened repeatedly.

Accidents

The statistics tell a complicated story. Between 2015 and 2019, 24 people died on ALR motorways, compared to 368 on conventional motorways. National Highways points to this as evidence that smart motorways are safer per mile travelled. The 2020 Stocktake found an 18% casualty rate improvement for the first nine ALR schemes following conversion.

But these figures mask a darker reality. In both 2018 and 2019, ALR fatality rates were higher than conventional motorways (0.19 vs 0.14 per hundred million vehicle miles in 2018, 0.14 vs 0.13 in 2019). More critically, live lane collisions increased from 3 per year before ALR to 19 per year afterwards, with fatal collisions rising from zero to 2.8 annually. The nature of risk had fundamentally changed.

The “safer per mile” argument also introduces selection bias: smart motorways are located at the busiest sections, which already had lower accident rates before conversion.

In June 2019, Jason Mercer, 44, and Alexandru Murgeanu, 22, were killed when a lorry struck their vehicles on the M1 near Sheffield. They had stopped for six minutes to exchange details after a minor collision. Sheffield Coroner David Urpeth found that “a lack of hard shoulder contributed to this tragedy” and that “Smart Motorways present an ongoing risk of future deaths”. Even the lorry driver testified that “if there had been a hard shoulder, the collision would have been avoidable”.

Fifteen months earlier, Nargis Begum, 62, died after her husband’s car broke down on the M1 near Woodall Services. 153 drivers passed without reporting the stranded vehicle. Senior Coroner Nicola Mundy found that if drivers had reported it, lane closures could have prevented her death. 16 minutes elapsed before collision, with another 6 minutes before warning signs activated. The coroner noted that the public wrongly believes cameras are constantly monitored, when National Highways confirmed this isn’t practicable.

Public confidence has collapsed. 73% of drivers now avoid the leftmost lane (up from 56% in 2019), and only 23% trust that authorities can detect and respond to stopped vehicles. When three-quarters of drivers avoid the lane that was meant to add capacity, the fundamental premise unravels.

The question isn’t whether smart motorways are statistically safer in aggregate. It’s whether we’ve created a system where the consequences of inevitable breakdowns are acceptable. On a conventional motorway, mechanical failure means pulling onto the hard shoulder. On a smart motorway, it means gambling that detection works, control rooms respond, Red X signs activate, and other drivers obey them. For Jason Mercer, Alexandru Murgeanu, and Nargis Begum, that gamble failed.

Putting the smart in Smart Motorway

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Swiss Cheese Model

If you’re familiar with the Swiss Cheese Model, you can probably see how, if enough things go wrong at once, a Smart Motorway can turn into a death trap. Each safety net represents a slice of cheese and its weaknesses are represented by its holes. Accidents happen not from a single flaw but when these random holes align across multiple layers, these being the SVD, ERAs and operational functions.

So what can be done to address these issues so Smart Motorways live up to their name?

Improved SVD and emergency response

SVD is the first layer of defence and should be the most reliable system. What Highways England achieved with a 20-second detection time is a vast improvement over the initial 17-minute baseline, but this remains contingent on system reliability, false positives, and operators verifying incidents before closing lanes.

For a safety-critical system, redundancy must be built in. Multiple sensor types (radar, CCTV, induction loops) would allow cross-validation, reducing false positives whilst enabling automated Red X activation when a stopped vehicle is confirmed. This removes the immediate burden from control room operators whilst prioritising road user safety. An automated verification pipeline could incorporate PTZ camera auto-positioning and image segmentation: when radar SVD detects a stationary vehicle, cameras automatically point to that location and AI confirms a vehicle is present, triggering an immediate lane closure. Operators would retain override capability but wouldn’t need to verify every incident manually. Performance targets should mandate 99% detection within 20 seconds, with quarterly audits and public reporting.

Automated Stopped Vehicle Detection Verification Pipeline Radar SVD detects stop PTZ camera auto-positions AI confirms vehicle present Red X auto- activates lane Operator notified (override available) Radar SVD detects stop PTZ camera auto-positions AI confirms vehicle present Red X auto- activates lane Operator notified (override available)

Proposed automated SVD verification pipeline (20-second target)

When SVD systems fail or are offline for maintenance, this must be communicated to drivers via overhead matrices, along with a memorable location identifier and contact number. A dedicated incident reporting line should be established, with guaranteed 30-second response times. Driver awareness campaigns must emphasise that reporting stranded vehicles saves lives. It would have taken only one of the 153 drivers who passed Nargis Begum’s car to alert Highways England. The public cannot be expected to rely on systems they cannot verify are working.

Operational Enhancement

Technology alone cannot fix smart motorways. The people operating these systems need adequate resources and proper tools.

Staffing must scale with network expansion. Rather than regional control rooms managing hundreds of miles, a dedicated operator should monitor each major ALR section (20-30 miles per operator). This allows genuine attention to CCTV feeds rather than superficial scanning. Control room operators need direct communication channels with recovery services and emergency responders, with mandated 60-second response times from incident detection to lane closure.

CCTV infrastructure requires systematic overhaul. Replace ageing PTZ cameras with fixed high-resolution cameras at every ERA and 500m intervals. These provide continuous coverage without manual positioning and can feed directly into AI verification systems. Automated alerts should trigger when vehicles enter ERAs, removing reliance on operators spotting every incident manually.

Training should become specialised. Smart motorway operations require different skills from general traffic management. Operators need specific training on SVD systems, automated verification pipelines, and worst-case scenario protocols. Regular simulation exercises testing response to multiple simultaneous incidents would identify systemic weaknesses before they cause fatalities.

ML traffic prediction and speed limit setting

Current variable speed limits are reactive, set after congestion begins. Machine learning could make them predictive.

From a control systems perspective, traffic flow is the output we’re trying to optimise, with variable speed limits as the primary control input. Current smart motorways are essentially reactive feedback systems: speed limits react to measured congestion rather than anticipating it. This introduces significant lag. By the time operators detect congestion and adjust limits, the system state has already degraded.

Machine learning enables predictive control on top of that feedback. By analysing historical traffic patterns, weather data, time of day, and real-time flow measurements, ML models could forecast congestion 15-30 minutes ahead. The system then adjusts speed limits proactively, smoothing traffic flow before bottlenecks form. This prevents the sudden braking that causes secondary collisions and maintains higher throughput even during peak periods.

The technology exists. National Highways already collects vast quantities of traffic data through MIDAS and SVD sensors: the sensor network is deployed. What’s missing is the analytical infrastructure to process this data predictively rather than reactively. Model Predictive Control (MPC) algorithms, already proven in industrial automation, could be adapted for motorway traffic management. Similar systems have demonstrated significant results: in Amsterdam, variable speed limit systems reduced overall accident rates by 23%, whilst on Germany’s A5 autobahn, accident rates fell by 20% in areas using variable speed limits and lane control.

Weather integration would enhance this further. Rain detected upstream becomes a disturbance input, triggering gradual speed limit reductions before drivers reach wet conditions. Forecast fog automatically lowers limits before visibility degrades. These aren’t hypothetical scenarios: the Netherlands uses visibility sensors to drop limits to 80km/h or 60km/h when fog is detected. Germany’s motorway control systems adjust limits based on time, weather, and traffic conditions across 9% of their autobahn network. The difference between reactive and predictive control is the difference between treating symptoms and preventing problems. In traffic management, that difference saves lives.

Integrating variable speed limits with navigation apps

Navigation apps currently do not display speed limits on Smart Motorways. Moreover, they also cannot provide accurate estimates without this data. The fix is straightforward: connect existing systems.

National Highways already publishes variable speed limit data through its NTIS, which pushes updates to subscribers within one minute of collection. National Highways operates developer APIs for road closures and digital signage. Extending this to include current variable speed limits would allow navigation apps to calculate journey times using actual conditions rather than theoretical maximums. Apps already have the capability to display speed limits and provide alerts; they just need real-time data.

Better still, the ML traffic prediction system proposed earlier could feed its forecasts into the same API. Navigation apps would then account for both current variable limits and predicted congestion, giving users genuinely accurate journey planning. A connected system is a smarter system. The infrastructure exists on both ends; it just needs integration.

Smart, or just cheap?

Smart motorways were sold as an intelligent solution to capacity constraints without building new lanes. The underlying principle was sound: dynamic traffic management extracting more capacity from existing infrastructure. In some respects, they delivered; higher vehicle throughput, smoother flows during congestion, reduced rear-end collisions when properly managed. The M42 pilot proved the concept worked.

But they were implemented as a cost-saving measure, not as the safety-critical system they needed to be. Intelligence requires systems that work reliably, connect seamlessly, and adapt predictively. Instead, we got a tightly coupled system where single points of failure (a missed CCTV feed, an SVD outage, a delayed Red X sign, drivers who’ve lost trust in the lane) can turn routine breakdowns into fatal collisions.

The evidence demonstrates that when everything functions as designed, ALR motorways can match or exceed conventional motorway safety whilst carrying significantly more traffic. But a system is only as reliable as its weakest link. The £390m ERA retrofit took years. SVD deployment was delayed six years. Staffing never scaled with expansion. Red X enforcement took four years from legislation to implementation. These aren’t minor details; they’re fundamental safety systems deployed in half-measures whilst the network kept growing.

The solutions outlined here aren’t revolutionary: automated SVD verification, proper ERA spacing, adequate staffing, predictive traffic control, navigation integration. They’re the basics of intelligent infrastructure done properly. Other countries already deploy weather-responsive speed limits and predictive traffic management. The sensor networks exist. The data exists. What’s missing is the analytical infrastructure and systems integration to treat “smart” as synonymous with “safe and well-designed”, rather than “cheaper than widening”.

The choice is stark: commit to running smart motorways to genuinely safety-critical standards, or restore hard shoulders and accept reduced capacity. The current approach (partial implementations, delayed features, and hoping nothing aligns wrongly) is no longer acceptable. Jason Mercer, Alexandru Murgeanu, and Nargis Begum paid the price for that gamble. The technology exists to prevent their deaths from being repeated. The only question is whether there’s the will to deploy it properly.

In future posts I’ll examine the wider transport network: why fast, affordable, reliable connections matter for productivity, opportunity, and quality of life, and what happens when we get it wrong. My next posts will be about Universal Studios Great Britain and East West Rail. But for smart motorways, the time for muddling through has passed. If it’s not done properly, we shouldn’t be doing it at all.