AUTONEWS

Why do drivers in Europe pay twice as much?! The Nissan Tekton costs €17,000 in the Middle East

Major automotive brands are once again playing a double game in the global market! While European buyers have to shell out a fortune for newer crossovers, the Japanese giant Nissan has prepared something fantastic for other markets—the Tekton model! This attractive SUV—essentially a rebadged and heavily restyled Indian Renault Duster—is taking the world by storm.

It is now arriving in the Middle East in a left-hand-drive version, featuring a powerful turbo engine and equipment levels that Europeans in this price range can only dream of! After debuting in India this July, the Nissan Tekton continues its global expansion. The exterior design of the export version boasts a distinctive, aggressive stance inspired by the company's flagship off-roader, the iconic Patrol.

Signature LED lights, massive bumpers, concealed rear door handles, and a generous ground clearance of 212 mm give it a commanding road presence. With a length of 4,348 mm and 18-inch wheels, the Tekton makes it clear that it is no mere city poser.

Under the hood lies a serious asset: a 1.3-liter turbocharged four-cylinder petrol engine delivering 153 hp and 278 Nm of torque, paired with a modern six-speed dual-clutch automatic transmission. In India, it is offered with a manual transmission and a more modest 100 hp 1.0-liter turbo engine, featuring front-wheel drive exclusively.

The crossover's interior is a highlight in itself. The cabin is dominated by a unified display setup comprising a 10.2-inch digital instrument cluster and a 10.1-inch multimedia screen. The equipment list rivals that of much more expensive European premium models: features include a panoramic roof, ventilated front seats, dual-zone climate control, a power tailgate, wireless charging, and a cooled storage compartment for drinks in the center armrest. Safety is ensured by a 360-degree camera system, six airbags, adaptive cruise control, and advanced systems for automatic braking, blind-spot monitoring, and lane-keeping assist.

However, the biggest sticking point for buyers in Europe is the price! Sales of the new Nissan Tecton are set to begin in the Middle East before the end of the year with a starting price of around $19,500—or €17,000—an amount that wouldn't even buy the base version of a Dacia Duster in Serbia! For a vehicle of this size, boasting 153 horsepower, an automatic transmission, and a generous equipment package, this figure seems like science fiction compared to European price lists. Manufacturers are once again proving that they produce cars of equal quality—yet drastically lower prices—under new names for other markets, leaving Europe to pay a heavy "premium" and steep margins.

 

DODGE

Dodge Charger Scat Pack Arrives in Europe

In the era of electric propulsion, an output exceeding 500 horsepower is commonplace, even in cars that aren't sports cars by definition. Yet, in the case of the Dodge Charger Scat Pack—which we had the chance to test in the US—that horsepower is truly thoroughbred.

Like any major automotive group today, Stellantis seeks rationality in defining its model lineup and aims to share as many components as possible without compromising (or, at worst, destroying) the DNA of its constituent brands. Cars rolling out of Rüsselsheim, Turin, Paris, or Sochaux are often so predictable that even their sporty versions struggle to spark much interest among their target audience.

Models like the Opel Corsa GSE, Peugeot 208 GTI, or Alfa Romeo Junior Speciale offer more power, aggressive styling, and—at most—some electronic aids for better road grip, yet they always retain a distinct air of conservatism.

To truly let a car's personality shine, you have to go further. That’s where Dodge—an American distant relative within the Stellantis family—steps in with the Charger, now arriving in Europe in its eighth generation (having been available in North America since 2024). Even with its own distribution network (managed by KW Automotive, which oversees 200 Dodge and RAM dealerships in Europe), the brand is perfectly suited to handle this "troublesome cousin": a figure viewed with suspicion and one no one dares cross, yet whom every other family member secretly envies.

Historically, the Charger was the dream car for generations of Americans (and public enemy number one to the Ford Mustang—the original muscle car, not the electric SUV—and the Chevrolet Camaro), though it remains less well-known in Europe. Although it retains some of its most important characteristics (such as its design), it has changed significantly.

It is available with a three- or five-door body style—though Dodge refers to them as two- or four-door models, the trunk actually features a fastback-style liftgate with an integrated rear window rather than a standard sedan trunk lid—and comes with either an inline-six gasoline engine (the 420 hp and 550 hp "Sixpack" variants) or a fully electric, eco-friendly, and politically correct variant (the Charger Daytona, the lineup's most powerful model at 680 hp), always with all-wheel drive.

Measuring over five meters in length (5.25 m, to be exact) and two meters in width (2.02 m), with a substantial weight of 2.2 tonnes, the Charger is built on Stellantis Group’s STLA Large platform. These proportions and mass define the entire driving experience, making it clear that the Stellantis brand designed the Charger to prioritize imposing road presence, straight-line stability, and a thrilling drive, rather than the precision associated with European sports sedans—particularly those of the German school.

Two cylinders fewer...but it certainly doesn't lack power! While this inline-six engine from the Hurricane family may lack the evocative charm of the old Hemi V8, it certainly sounds great—at least from outside the car. Inside, the situation changes slightly, as the active exhaust system is complemented by a sound amplifier that projects low-frequency notes through the cabin speakers—a questionable solution: the natural sound was already pleasing to the ear, and the acoustic experience feels quite artificial in its futile attempt to mimic the sound of a V8 engine (production of which ended in late 2023).

A direct comparison with the previous V8-equipped Charger shows that losing two cylinders does not translate to inferior performance. In the case of the new R/T, power increases by 50 hp compared to the 5.7-liter HEMI V8, and 0–100 km/h acceleration is half a second quicker (4.9 s vs. 5.4 s). A similar trend applies to the Scat Pack when compared directly to the 6.4-liter HEMI V8; it boasts 65 hp more and is 0.3 seconds faster in the 0–100 km/h sprint (4.1 s vs. 4.4 s). Yet, as with so many things in life—even sports cars—much of the experience is defined more by the *how* than the *how much*. And the truth is, the organic vibration of a V8 remains unique.

For this test, we opted for the gasoline-powered Scat Pack version, which features a twin-turbo inline-six engine capable of producing 550 hp and 720 Nm of torque. The engine block is made of aluminum and features direct fuel injection and variable valve timing. At the driver's discretion, the all-wheel-drive system can be disengaged, sending all power to the rear wheels.

Speaking of exceptional dynamic features, the Line Lock system (optional on the R/T and standard on this Scat Pack) locks the front brakes, allowing the rear wheels to spin freely without the car moving forward (the official justification is that this tire friction helps clean and warm them up before a launch, though we all know the real purpose is bringing that irrational teenage fun into adulthood...). Had I driven on a track, I might have given it a try, but on California roads, such a maneuver is enough to risk a stint with free room and board—staring at the sun through barred windows in the company of the county sheriff...

All Chargers feature five driving modes (selectable via a steering wheel button): Auto, Eco, Wet/Snow, Sport, and Custom. Eco mode prioritizes efficiency, emphasizing rear-wheel drive. Wet/Snow mode maximizes traction on low-grip surfaces, distributing torque equally between the front and rear axles. Sport mode firms up the steering (2.5 turns lock-to-lock—acceptable for a sports car also used for daily driving), sharpens transmission behavior (delaying upshifts and hastening downshifts), amplifies the engine note, and quickens throttle response, giving the Charger a sportier overall feel.

In this mode, traction control is deactivated, though the driver can re-engage it via a button located below the climate controls. In Custom mode, the driver can select Street or Sport settings for the drivetrain, traction control, and steering, as well as enable or disable the steering-wheel-mounted paddle shifters.

How does it behave on the road? Regarding performance, the acceleration figures have already covered most of the story, and the top speed (285 km/h) confirms there is no shortage of power. Any extra pressure on the throttle pins the driver and passengers against their leather seats—delivering power with remarkable linearity when all four wheels are engaged, or allowing the rear end to come alive—in a manner that feels almost sensually wild—when power is sent solely to the rear wheels. This is precisely where you understand why it’s called a "muscle car."

The Americans equipped the Dodge with independent multi-link suspension at both the front and rear, adaptive dampers, and—in this more powerful version—Brembo brakes. The ride is quite firm, especially in the unit I drove, which sported imposing (and visually stunning) 305/40 ZR20 wheels and tires, with impressive (and capable) Brembo brake calipers gleaming behind them. Unless the asphalt is as smooth as a billiard table, you’re better off avoiding Sport mode. The eight-speed torque-converter transmission handles normal driving demands well in automatic mode but is slower in manual mode, especially compared to a dual-clutch transmission.

Spacious and solid, but the price...The overall impression of the interior is positive. Build quality is solid, though some trim details fall short of what a European buyer would expect from a car costing over €70,000 (specifically, the plastics in the door bins, the lack of soft-touch lining in the glovebox, the dashboard materials in lower, out-of-the-way areas where front occupants have less contact, the rear air vents, etc.).

Both rows of seats are spacious; the second row is suitable for two people and can accommodate a third, provided they aren't too tall and don't mind sharing foot space with the bulky central tunnel housing the driveshaft and exhaust pipes. A rear passenger around 1.80 meters tall will have about ten fingers' width of clearance in front of their knees before touching the front seatbacks, but very little headroom.

The large rear hatch adds a level of practicality its predecessor lacked, making this Dodge a more functional vehicle than many sports cars capable of carrying the whole family. Its cargo capacity—exceeding 600 liters—is more typical of an SUV trunk and can be expanded to over 1,000 liters by folding down the rear seatbacks to create a flat load floor.

The dashboard features two screens: a 16-inch digital instrument cluster and a 12.3-inch touchscreen for the infotainment system. It also includes a head-up display. There are enough physical buttons to make driving and simultaneously controlling key vehicle functions easy.

Performance pages on the central display provide real-time data on vehicle parameters—such as acceleration times, gauges, G-forces, and powertrain behavior—while the driving experience recorder can capture synchronized video, audio, and vehicle data for post-drive analysis.

After covering more than 1,000 kilometers between Los Angeles, Palm Springs, and San Diego, the Dodge Charger Sixpack Scat Pack’s average fuel consumption was 12.6 L/100 km—slightly (0.2 L/100 km) higher than the official figure. Given current gasoline prices, this results in a hefty monthly bill, but considering the Charger's power and performance, it is understandable. It is not something that greatly bothers American drivers, in a country where gasoline is still cheaper than water...

When paying over 75,000 euros for a car, one generally expects the highest level of fit, finish, and material quality—something the Charger does not offer. Yet, there is no doubt that it stands out on the road thanks to its commanding presence. The new Charger is captivating and lacks no personality, both visually and in terms of its on-road performance. This is true despite certain flaws—such as being too large for many European roads and too heavy to be truly agile or efficient—and the void left by the absence of the defunct V8 engine's naturally impressive sound.

Joaquim Oliveira, reporting from Los Angeles

 

TUNNING

Nissan Z Kaze Concept

Nissan's latest concept car pays tribute to both the celebrated era of modified car culture and the company's own heritage of open-top models.

According to Motor1, the Nissan Z Kaze concept stands out with its turquoise paint, retro wheels, and a feature long missing from the Z model: a "T-top" roof. The color is called Pacific Teal, and it suits the car perfectly, complementing the custom roof featuring removable glass panels.

However, the Z Kaze concept is much more than just its paint and roof. Components such as the hood, fender flares, hood vents, and additional aerodynamic elements are crafted from carbon fiber.

As for the wheels, Nissan sourced a set of NISMO LMGT2 rims and restored them for this "Z." The LMGT2 is a forged wheel originally manufactured by RAYS back in 1996.

The bodywork underwent a transformation equally rich in references to that era. Painted in the exclusive Pacific Teal hue, the Z Kaze features a vented carbon-fiber hood, widened fenders, and a carbon-fiber aero kit—including a front splitter, side skirts, a diffuser, and a more pronounced rear spoiler. Visually, the result is wider and more aggressive than the standard Z, yet the modifications were carefully crafted to preserve the model's original identity. One of the most interesting details lies in the wheels: Nissan sourced a set of original, multi-piece, forged 18-inch NISMO LMGT2 wheels—now out of production—and carried out a complete restoration and rebuild specifically for this prototype. Measuring 10 inches wide at the front and 11 inches at the rear, they complement the muscular stance created by the new fenders. This choice was deliberate: the LMGT2s share the same visual lineage as the LMGT1 wheels used on the rare 1990s Skyline GT-R NISMO 400R, creating a direct link between this concept and one of the most iconic periods in Nissan's sports car history.

The interior follows the same design philosophy but aims to contrast with the blue-green exterior. The seats feature exclusive white leather upholstery, accented by carbon-fiber trim on the center console and dashboard. Since the cabin is directly exposed to the elements when the T-top panels are removed, Nissan integrated Bose Personal Plus speakers into the headrests, ensuring the audio system remains clearly audible despite increased wind noise. This solution is particularly intriguing as it blends modern technology with a roof configuration associated with classic Z models. The removable panels incorporate glass elements and are designed to be taken off and stored within the vehicle itself; the mechanism was developed specifically for the concept and boasts a finish comparable to that of a production component. 

Mechanical aspects also received attention. The Z Kaze utilizes the twin-turbo 3.0L VR30DDTT V6 found in the 2027 Nissan Z, but Nissan collaborated with tuner GReddy to incorporate an Airinx intake system, a front-mounted air-to-air intercooler, and a Supreme SP exhaust system. The suspension was also modified with NISMO components, although Nissan did not release specific figures for horsepower, torque, acceleration, or top speed for the concept. This means the standard 2027 Z’s official output of 400 hp and 474 Nm serves only as a baseline for the stock engine; one cannot assume these are the Kaze's figures following the modifications made by GReddy. The NISMO version of the Z, meanwhile, produces 420 hp and 520 Nm, but there is no indication that the concept uses the NISMO engine or tuning.

Choosing the 2027 Z as the base is particularly fitting, as Nissan has just updated its sports car for the new model year, retaining the front-engine, rear-wheel-drive, and 6-speed manual transmission formula. The standard lineup uses the twin-turbo 3.0L V6 with 400 hp and 474 Nm, while the Z NISMO develops 420 hp and 520 Nm. The Kaze, however, was not conceived as a new production model or a replacement for the NISMO; it is a one-off show car created specifically to celebrate Z culture and explore a concept not currently offered in Nissan's lineup. To date, there has been no announcement regarding production of a T-top model or any version directly derived from the concept.

The tradition revived by this project is rich indeed. The T-top system first appeared in the Z lineage with the 280ZX and later the 300ZX, remaining a hallmark of the model through the 1990s; the 1996 300ZX is cited as the final production Z to feature this roof style. By bringing removable roof panels back to the modern Z, Nissan has established a direct visual link to that era—a time when Japanese sports cars were fostering a massive culture of customization and tuning. The Kaze brings together various symbols of that period in a single vehicle: T-tops, teal paint, NISMO LMGT2 wheels, carbon fiber, GReddy tuning components, NISMO suspension, and an audio system designed for open-top driving.

Following ZCON, the Nissan Z Kaze Concept will head to the Japanese Classic Car Show in Long Beach, California, on October 3, 2026, further showcasing the vehicle to an audience of enthusiasts. The choice of these two events highlights the car's purpose: it is not merely a design exercise, but a celebration of the community that has sustained the Z tradition for decades. By blending references from the 280ZX and 300ZX with the current Z, Nissan has created a visual bridge between different generations of the sports car.

The 2026 Nissan Z Kaze Concept is, therefore, a deliberately nostalgic take on the modern Z, executed using contemporary tools and materials. While the removable T-top is its most striking feature, the project goes further by reviving historic NISMO wheels, reinterpreting 1990s tuning culture, and incorporating GReddy and NISMO components into the mechanical setup. As a unique exercise created for ZCON, the Kaze does not signal a future production model from Nissan; instead, it offers a compelling vision of how the manufacturer can engage with its own history without simply replicating it. It is a 21st-century Z with its roof open to the past.

The Z Kaze concept is equipped with a range of NISMO suspension upgrades. It also features parts from GReddy, including a Supreme SP exhaust system and an upgraded intercooler.

Although the Japanese manufacturer has not released specific figures, the standard Z model produces 298 kW (406 hp) and 474 Nm of torque, while the Nismo version delivers 313 kW (426 hp) and 520 Nm.

Nissan is currently showcasing the car at the ZCON event in Phoenix, Arizona. Afterward, the Z Kaze will head to the Japanese Classic Car Show.

Autonews

AUTONEWS

A new AI framework could help cities plan for future traffic

AI traffic planning has spent two decades being reactive. A queue forms, a camera sees it, a control room retimes a signal, and the delay that already happened is shaved by a few seconds. A team at New York University’s Tandon School of Engineering has published a framework that tries to move the work upstream, forecasting where congestion will sit years out and letting a planner interrogate the result in plain English.

The paper, “Geospatial AI Applications for Reducing Traffic Congestion and Guiding Planning Decisions,” appears in Transactions in GIS, volume 30, issue 4, under DOI 10.1111/tgis.70327. Anton Rozhkov of NYU’s Center for Urban Science and Progress wrote it with Pranav Nitin Motarwar and Rudra Patil. Our AI models and tools hub tracks the model families this kind of applied work is built on, and the predictive analytics page covers the forecasting side in a business context.

This article sets out exactly what the framework does, what the numbers in it mean, where the arithmetic leads, and what the authors say it cannot yet do. The result is narrower than the headlines suggest and more useful because of it.

A forecasting layer with two competing models...The framework runs ARIMA, a conventional statistical time-series method, alongside an LSTM neural network, and compares them on the same held-out data. Keeping both is deliberate: the comparison is the evidence that the neural approach earns its complexity.

A spatial layer built on hexagons...AI traffic planning forecasts are laid onto Uber’s H3 hexagonal grid, which tiles the city into cells that can be zoomed in or out without changing shape. That lets the same data answer “which borough” and “which corridor” without a separate model for each question.

A clustering step that finds hotspots...On top of the hexagons, clustering groups cells that behave alike, which is how the framework locates congestion hotspots rather than reporting one flat number for the whole city.

A language interface grounded in the city’s own data...The last component is a customised portal built on Meta’s LLaMA model, connected to a project-specific traffic knowledge base and run zero-shot. Planners type a question; the portal answers from the project’s data rather than from whatever the base model absorbed in training. This is applied natural language processing doing an unglamorous job well.

Why it runs locally...Rozhkov’s stated motivation for AI traffic planning tooling was control over where the data sits. “We started with an idea: what if we developed our own AI platform, one that could be hosted locally and would be secure, intuitive and comfortable for planners to use in their day-to-day work,” he said — a platform agencies can run “behind their own firewall.”

Fifteen years in, four years out...The AI traffic planning framework was fitted on roughly 15 years of New York City traffic observations spanning 2009 to 2024, and evaluated on a 2021 to 2024 test set. Four years of testing against fifteen years of record is about 27% of the data held back, which is a generous split by forecasting standards.

The error gap, stated precisely...On that test set the LSTM returned a root mean square error of 342.56 vehicles per day. ARIMA returned 417.62. The difference is 75.06 vehicles per day, which is 17.97% of the ARIMA figure — the “roughly 18% improvement” the coverage quotes, and it checks out.

What that error looks like in context...Against the framework’s own 2025 baseline of 12,540 vehicles per day, an error of 342.56 is 2.73% of daily volume. ARIMA’s 417.62 is 3.33%. Both are small; the neural model is better by about six tenths of a percentage point of daily volume, not by an order of magnitude.

The forecast itself...The AI traffic planning framework projects average daily traffic volume rising from 12,540 vehicles in 2025 to 19,680 in 2029. That is 7,140 additional vehicles per day across four years, an average of 1,785 per year, and a 56.9% total increase.

The growth rate hiding inside it...Compounded rather than averaged, 12,540 to 19,680 over four years is about 11.9% per year. Treating that as a certainty would be a mistake — the researchers stress the forecasts carry substantial prediction intervals — but it is the number a capital plan would have to absorb.

Seasonality is real but partial...Motarwar’s summary of the baseline is blunt: “ARIMA gives you seasonality, which is true but not the whole story.” Weekly and annual rhythms are genuinely there, and a statistical model finds them cheaply.

The residual is where the gain lives...“The LSTM picks up the parts of the pattern that don’t repeat cleanly, and that’s where most of the improvement came from,” Motarwar said. That is an unusually precise attribution — the 18% is not diffuse model magic, it is the non-repeating remainder.

Which argues against replacing ARIMA...If the improvement lives in the residual, the statistical model is still doing most of the work on the bulk of the signal. Running both is not indecision; it is how you know which part of the forecast you should trust least.

The comparison is also the honesty check...A single-model paper reporting 342.56 vehicles per day of error would give a reader nothing to judge it against. The paired result is what makes the AI traffic planning claim falsifiable by the next team that tries it.

Where the approach is likely to travel...Any city with a long, dense count record and a mix of repeating and irregular demand has the same structure. Cities with short records, or with a single dominant commuting pattern, would likely see a smaller spread between the two models.

Averages hide the problem...“A citywide average doesn’t help the city planners,” Patil said. A single number rising by 56.9% tells an agency that something is coming, and nothing about where to put the money.

Corridors, not boroughs...“What they need to know is which corridors are badly impacted, and those turn out to be consistent year after year,” Patil said. Persistence is the operationally valuable finding: a hotspot that recurs is a hotspot you can plan against.

Hexagons make scale a dial, not a rebuild...H3 cells nest, so the same indexed forecast answers a borough-level question and a corridor-level question without re-modelling. That is a plumbing detail with real consequences for how often a planning team can actually ask something.

What the hotspot analysis surfaced...Manhattan came out as the highest-congestion borough, with Brooklyn and Queens also appearing in the analysis, and specific concentrations around Lower Manhattan and the approaches near LaGuardia Airport. None of that will surprise a New Yorker, which is the point — it is a sanity check on the method.

Figure	Value	Source
LSTM test error	342.56 vehicles/day	Published
ARIMA test error	417.62 vehicles/day	Published
Absolute gap	75.06 vehicles/day	Subtraction
Relative gap	17.97%	75.06 / 417.62
2025 baseline volume	12,540 vehicles/day	Published
2029 forecast volume	19,680 vehicles/day	Published
Four-year increase	7,140 vehicles/day, +56.9%	Subtraction, division
Implied annual growth	≈11.9% compounded	Fourth root of 1.569

Consistency is the testable claim...If the same corridors recur year after year, then next year’s data is a live test. That makes this spatial layer of AI traffic planning easier to validate than the five-year volume forecast, which nobody can check until 2029.

Adaptive signal control is reactive by design...Systems that retime signals from live detector feeds are the current state of practice, and they work. They also start from a queue that already exists, which is the ceiling this AI traffic planning work is trying to lift.

Four-step travel demand models are slow to question...The classic regional demand model is powerful and expensive to run. A scenario takes weeks, which quietly limits how many scenarios get considered before a decision is made.

Navigation apps optimise for the driver, not the network...Commercial routing data tells a city where delay happened yesterday from the point of view of individual drivers. It does not give an agency a structural account of why that corridor fails.

Congestion pricing needs a forecast it can defend...Any charging scheme has to withstand a public inquiry. A published error figure on a held-out test set is the sort of thing that survives that process, which is one practical reason the AI traffic planning literature is moving toward stated baselines.

The gap this fills is speed of enquiry...None of the above is replaced. What changes is that a planner can ask a spatial question and get an answer in minutes, which is the difference between testing one option and testing eight.

The query portal is the part most cities would notice...The forecasting half is standard AI traffic planning practice done carefully. The interface is the part that changes who can use the output.

The failure mode it is built against...“A general chatbot gives you a reasonable-sounding generic paragraph about congestion,” Rozhkov said. That sentence describes the exact risk of bolting a commercial assistant onto a planning workflow: fluent output with no connection to the agency’s own counts.

Grounding is the whole mechanism...The portal is tied to a project-specific traffic knowledge base. “Planners need an answer that comes from their own data,” Motarwar said. Grounding turns the model from an author into a retriever with a readable voice.

Zero-shot, deliberately...Running the portal zero-shot means no task-specific fine-tuning was needed, which lowers the maintenance burden for an agency that does not have a machine learning team on staff.

Local hosting solves a procurement problem, not just a privacy one...Transport agencies hold movement data that is sensitive in aggregate and politically awkward in detail. A platform that runs inside the firewall sidesteps a data-sharing review that can take longer than the modelling.

It lowers the cost of asking a second question...The practical effect of good AI traffic planning tooling is not one brilliant answer. It is that the fifth follow-up question costs the same as the first, so scenarios get explored rather than commissioned.

What the AI traffic planning study does not establish...The authors are explicit about the limits of the AI traffic planning work, and the limits are the most important paragraph in any applied paper.

It has not been trialled at scale...The AI traffic planning framework has not been through large-scale trials with actual planning departments. Everything above is a research result, not a deployment record.

It cannot reduce congestion by itself...The researchers say plainly that the platform cannot solve congestion on its own. A forecast changes what a capital programme knows; it does not add road capacity or move a single vehicle.

The forecast intervals are wide...The 2029 figure carries substantial prediction intervals. Quoting 19,680 as a point estimate, as most coverage has, drops the uncertainty that the authors attached to it.

One city, one data regime...Every result here is New York’s. A framework fitted on fifteen years of dense counts in a dense city says nothing yet about a mid-size city with sparse sensors.

A grounded model still inherits its base model’s habits...Grounding constrains what the portal retrieves. It does not eliminate the possibility of a confidently worded synthesis that overstates what the underlying cells support.

How to Read an AI Traffic Planning Result If You Buy Technology...For anyone evaluating vendor claims in this space, the paper is a useful AI traffic planning yardstick precisely because it is modest.

Ask for the baseline, not the headline...An 18% improvement is only meaningful next to the thing it improved on. A vendor quoting an accuracy number with no stated baseline has told you nothing.

Ask what the error means in units you use...342.56 vehicles per day is interpretable. “97% accurate” is not, unless you know accurate at what and against which alternative.

Ask where the model runs...Local hosting is a genuine differentiator for public bodies, and it is testable at procurement rather than after deployment.

Ask what the system refuses to answer...A grounded portal should decline questions its knowledge base cannot support. A system that always has an answer is the failure mode Rozhkov described.

New York University’s Tandon School of Engineering

 

AUTONEWS

Rivian R2 vs. Tesla Model Y: An electric SUV showdown

The Tesla Model Y has been the sales king of small electric SUVs since the moment it arrived. Many rivals have chased its success, but none have come close. Now it's Rivian's turn. The Rivian R2 is the California EV automaker's first small electric SUV, and it's gunning for the Model Y. Rivian's R1T truck and larger R1S SUV are excellent EVs, but their $80,000-ish price tags put them out of reach for most shoppers. At a starting price of about $45,000, the R2 is finally a Rivian many Americans can afford. To help you decide, Edmunds compared these two head-to-head to find out which is the better buy.

Range and charging...The Rivian R2's range spans from 275 miles in the base Standard model to 345 miles in the Standard Long Range. Most other versions are rated at 330 miles. In the real-world Edmunds EV Range Test, the R2 Performance covered 304 miles, falling short of its 330-mile EPA estimate.

The Tesla Model Y outperforms the R2, ranging from 294 miles in the all-wheel-drive base model to 357 miles in the rear-wheel-drive Premium. The rear-wheel-drive base Model Y Edmunds tested covered 337 miles, and the new three-row Model Y L managed an impressive 358 miles — both beating their EPA estimates. For both rivals, all-wheel-drive versions are rated lower than their rear-wheel-drive counterparts.

Both electric SUVs use Tesla-style (NACS) charging ports, giving them access to Tesla's vast Supercharger fast-charging network. Under ideal conditions, Tesla says the Model Y can add up to 162 miles in 15 minutes, edging out the R2's cited 150 miles in 15 minutes. Edmunds' testing confirmed that the Tesla charges slightly quicker.>>>Winner: Tesla Model Y

Tech features...The R2 and Model Y boast an impressive amount of standard tech, including a generous collection of advanced driver aids, wireless smartphone charging pads, large touchscreens with sharp-looking graphics, Google-based navigation, and camera systems that record your drive and monitor the vehicle's surroundings when parked.

A hands-free driving mode is also available on both EVs. Tesla's Full Self-Driving (Supervised) drives hands-free on highways and city streets, obeying stop signs and traffic lights and changing lanes on its own — in Edmunds' testing, it's been the most useful hands-free driving system available. Rivian's Autonomy+ also works hands-free on roads with painted lane markings, but it doesn't operate during city driving.

Overall, however, the R2's tech features are impressive. Unlike the Model Y, it has a digital driver display that keeps important info in your line of sight. Also, the R2's touchscreen system proved more responsive and enjoyable to use in Edmunds' testing.>>>Winner: Rivian R2

Interior comfort and utility...The Model Y rides more smoothly over bumps than the firmer R2, though both have comfortable, supportive seats. Passenger space is similar, though the R2 has a bit more rear headroom. Tesla one-ups Rivian, however, with an available third row in the Premium and in the extended-length Model Y L — though it's best for kids and small adults, and it eats up a lot of cargo space when raised.

As for cargo, the R2 tops the Model Y behind the rear seats when you include their underfloor storage compartments, and it offers far greater total capacity: 90.1 cubic feet versus the five-seat Model Y's 75.5 cubic feet. Its front trunk is slightly larger too, and its 4,400-pound max towing capacity tops the Model Y's 3,500 pounds.

The Model Y is more comfortable and offers third-row versatility, but the R2 delivers more utility and a roomier back seat>>>Winner: tie

Pricing and value...Including destination fees, the Rivian R2's starting prices range from $46,485 for the Standard trim to $59,485 for the top-level Performance trim. The Model Y spans $41,380 for the base trim to $63,380 for the three-row Model Y L. Note that for the R2, only the priciest Performance trim is available now; more affordable trim levels arrive in late 2026 and spring 2027.

In general, the R2 will likely cost you a little more to buy. But it justifies its premium with a richer-looking interior, distinctive styling, superior off-road capability, and uncommon touches like a drop-down rear window and dual glove boxes. Rivian also backs it with a one-year/12,000-mile adjustment warranty covering wheel alignment, factory defects and more — something Tesla doesn't offer>>>Winner: tie

Edmunds says...This electric SUV comparison ends in a tie — both earned the same overall rating score and are at the top of Edmunds' rankings for electric small SUVs. You can't go wrong with either, but if range and comfort top your priorities, the Model Y is for you. If you'd rather have a more capable, premium-feeling SUV, order the R2.

© 2026 The Associated Press

AUTONEWS

How raindrops can damage car paint: Scientists discover an unexpected mechanism

Rain generally poses no problem for a car, but research has shown that, under certain conditions, water droplets can trigger tiny electrical discharges and damage the surface's protective layer.

Researchers at the Max Planck Institute for Polymer Research in Mainz discovered that water droplets can become electrically charged and then trigger very small electrical discharges upon striking a car's protective coating. Under extreme conditions, these discharges can locally puncture the coating; scientists liken this effect to a form of miniature lightning, reports *Auto Bild*.

How do the droplets become charged? For the study, scientists used water droplets with a volume of approximately 35 microliters and allowed them to slide across various surfaces, including plant leaves, plastic, and glass.

An electrical charge was generated through friction as the droplets moved across the surface. Depending on the substrate, the droplets carried a charge ranging from 0.2 to two nanocoulombs. Although these are minute quantities, the resulting potential can reach several thousand volts. The scientists then conducted an experiment in which they allowed 3,000 charged droplets to fall onto a copper plate covered with a very thin layer of Teflon.

After 3,000 droplets, damage appeared...Under an electron microscope, the researchers observed what was invisible to the naked eye: the charged droplets had created tiny craters in the protective layer. In some places, the damage penetrated the entire protective coating, reaching the underlying metal. Once the metal is exposed, corrosion can set in. A control experiment yielded a different result: when droplets fell directly onto the coating without an initial electrical charge, the protective layer remained undamaged even after 3,000 impacts.

Does this mean that rain ruins car paint? Researchers did not demonstrate that an ordinary downpour would cause visible pitting or damage to automotive paint; the experiments were conducted under laboratory conditions using specially prepared protective coatings.

Nevertheless, the discovery could prove significant for the development of future automotive paints and protective coatings. Electrically charged water droplets can occur naturally—for instance, in clouds, near waterfalls, or when water flows over water-repellent surfaces.

Until now, protective coatings primarily had to withstand the effects of water, salt, acids, and other environmental factors. This new research suggests that another potential factor should be considered for the future: the very small electrical discharges that can occur within water droplets.

How rain damages car paint:

• Micro-Lightning Discharges: Recent research from the Max Planck Institute shows that water droplets sliding down surfaces (like trees or roofs) pick up an electrical charge. When these charged drops hit your car, they release microscopic electrical sparks that can slowly punch through and break down protective coatings.

• Mineral Water Spots: As rainwater sits and evaporates on your car, it leaves behind dissolved dirt, pollutants, and minerals. When the sun bakes these droplets, the leftover minerals etch into the clear coat and cause dull spots.

• Pollutants and Acids: Rain gathers dust, soot, and industrial chemicals from the air. If your car has chips or scratches, this dirty residue accelerates rust and corrosion on the exposed metal underneath

AUTONEWS

Speed and low-light conditions linked to drivers skipping seatbelts

In 2021, motor vehicle crashes were one of the leading causes of death in the United States, accounting for 42,939 deaths nationwide (Stewart, 2023). Many road fatalities are attributed to human factors, such as speeding, impaired or distracted driving, and failure to wear a seat belt (Stewart, 2023, National Highway Traffic Safety Administration, 2022). Studies have shown that seat belts are one of the most effective countermeasures for minimizing crash-related injuries (Blincoe et al., 2010, Lee and Schofer, 2003, National Center for Statistics and Analysis, 2016). Seat belts alone saved 14,955 lives in 2017 and may have protected an additional 2,549 if used (National Center for Statistics and Analysis, 2017). Seat belts for front-seat passengers can reduce fatal injuries by 45 % and moderate-to-severe injuries by 50 %, according to the National Highway Traffic Safety Administration (NHTSA) (National Center for Statistics and Analysis, 2016). 

Despite the safety benefits of seat belts, a considerable number of drivers and passengers continue to travel unrestrained. A recent nationwide seat belt data revealed that the nationwide non-compliance rate was 8.1 % for all occupants, 7.9 % for drivers, and 10.0 % for front-seat passengers (National Center for Statistics and Analysis, 2024). In the same year, 51 % of the 23,824 occupants killed in car crashes were not wearing seat belts (National Highway Traffic Safety Administration, 2022). Since not wearing a seat belt or non-compliance with seat belt laws is a critical safety issue, an in-depth analysis of seat belt compliance is needed.

Seat belts are among the most basic and effective in-vehicle safety measures. Yet, understanding seat belt compliance and seat belt use behavior remains a complex issue that spans multiple disciplines. Research on this topic involves contributions from healthcare, computer science, psychology, human factors, law, and other fields. Over the past two decades, numerous studies have investigated various aspects of seat belt use (Aidoo et al., 2021, Farooq et al., 2021, Hezaveh et al., 2019, Høye, 2016, Lerner et al., 2001, Raman et al., 2014, Shaaban and Abdelwarith, 2020, Steptoe et al., 2002). To address the problem of seat belt non-compliance, it is crucial to discuss each component of seat belt use behavior.

Previous research has taken different approaches to this issue. Some articles and reports have focused on the factors associated with seat belt use, while others have emphasized education and awareness campaigns. Certain studies have concentrated on detecting seat belt compliance, and others have used self-reported surveys and crash reports to investigate usage.

In terms of review studies, researchers have examined seat belt use from various perspectives. For example, a recent study by Agarwal, Kidambi, and Lange reviewed the evolution of seat belts, highlighting technological advancements, regulatory changes, and future trends, particularly with the advent of vehicle automation (Agarwal et al., 2021). Kargar et al. conducted a meta-analysis on the prevalence of seat belt use, examining factors influencing compliance and the effectiveness of interventions (Kargar et al., 2023).

Despite the ample research on seat belt compliance and seat belt use, a systematic review that encompasses all components associated with seat belt use and compliance behaviors is missing from the literature. These components include factors influencing seat belt usage behavior, various techniques to collect and analyze seat belt use, and measures to improve compliance. This study aims to fill this gap by synthesizing the comprehensive literature on seat belt use and compliance, identifying gaps, and recommending future research directions to improve this critical safety habit.

Another essential objective of this study is to compile a summary of findings from the extensive literature review. These findings highlight the state of knowledge on data collection, data analysis, contributing factors, and measures to improve seat belt compliance since the year 2001. This summary will be invaluable for engineers, practitioners, and policymakers in proposing appropriate measures to enhance seat belt compliance.

In contrast to previous review studies, this research specifically aims to provide a comprehensive review of “seat belt use and compliance behavior” over the last 23 years, focusing on driver demographics, roadway design, trip features, and temporal variables. This study also aims to explore modern data collection methods and advanced technologies like Deep Learning (DL) for compliance detection. This approach offers a broader understanding and practical recommendations for enhancing seat belt usage, distinct from the scope of earlier studies.

This paper is organized as follows. The first section is the introduction, which provides an overview of seat belt non-compliance behavior. It is followed by the ‘Research Methodology’ section, which describes the systematic contributors to seat belt compliance studies. The next section is the Results section, which includes multiple subsections. The first subsection focuses on summarizing the trend of seat belt compliance rates across the globe. The next subsection presents an overview of various studies focusing on collecting seat belt usage data. An overview of different data analysis methods used to explore seat belt use and compliance behavior is illustrated in the following subsection. 

The subsequent subsection describes the factors associated with seat belt use and compliance. A comprehensive review of the measures taken to ensure seat belt use is presented in the final subsection. Before the conclusion, a 'Discussion' section is provided to examine the current trends in seat belt compliance research, the interconnections between components of seat belt use and compliance research, gaps in the existing literature, possible policy recommendations, and future research opportunities. The last section is the conclusion, which highlights the summary of seat belt research and the gaps in the literature on seat belt use and compliance studies.

Drivers were less likely to wear seatbelts at night, at higher speeds, and on shorter trips, suggesting safety campaigns may be more effective when they target the situations in which people are most likely to skip buckling up.

Drivers who don’t wear their seatbelts were found to be doing it in situations when seatbelts would actually help the most, according to a new study by researchers from the Penn Medicine Nudge Unit. The analysis, published in Traffic Injury Prevention, highlighted that situations like driving after sundown or even at high speeds were more predictive of whether someone skipped a seatbelt than their demographics.

“Our study suggests that future seatbelt campaigns and other interventions may be more effective if they move beyond broad demographic targeting and, instead, focus on these moments when people are most likely to skip buckling,” said senior author M. Kit Delgado, MD, faculty director of the Nudge Unit and an associate professor of Emergency Medicine.

Seatbelts cut the risk of death and serious injury...Seatbelt usage has been found to cut people’s risk of death or serious injury in half, but one in 10 of those riding in the front seats of a vehicle don’t use them. As such, almost half of crash fatalities in the United States involve a person not wearing their seatbelt.

Delgado, lead author Jeffrey Ebert, PhD, director of Applied Behavioral Science at the Nudge Unit, and their colleagues decided to see if there were some commonalities in who didn’t use seatbelts and what situations non-usage of seatbelts occurred in.

Looking at data from 1,000 consenting participants over almost 130,000 trips­—gleaned from vehicle sensors, including ones in seatbelt latches—the researchers found that most people did use their seatbelts. Of all trips recorded, 84 percent included seatbelt use.

However, when a seatbelt was not used on a specific trip, the researchers determined that it was unrelated to the driver characteristics like sex, age, or where they lived. Instead, it was the details of the trip that mattered.

Drivers buckle up less at night and at higher speeds...The data showed that 30 percent of buckling behavior could be tied to “situational factors,” while not even 1 percent could be tied to demographics.

Drivers were 6 percent less likely to wear a seatbelt in the evening, and 13 percent less likely late at night.

For any given driver, a 10 mile-per-hour increase in speed was tied to a 2 percent drop in buckling.

Looking at when drivers did use their seatbelts, longer trips correlated with greater seatbelt use. The data showed that 74 percent of drivers taking a trip of five miles or less wore their seatbelts, but 95 percent buckled on longer trips.

Identifying a more targeted intervention...Currently, campaigns for seatbelt buckling are largely targeted widely, such as through electronic billboards and “Click-it or Ticket” advertisements.

But the Nudge Unit’s work points to an opportunity to more effectively reach people in situations where they might need the message.

At the same time, the team said that roughly 25 percent of individuals’ tendency to buckle up couldn’t be pinned down to a factor measured in the study. More research could potentially narrow down these unknown variables—which could include non-demographic considerations like personality traits—and lead to better interventions.

“The more we know about when people buckle up—and when they don’t—the more lives will be saved,” Ebert said.

The team has already had success in getting more drivers to buckle up. Offering cash incentives in a “game” challenging drivers to stack up seatbelt streaks resulted in more drivers using their seatbelts, even after the game ended.

“We have tested ways that seem effective in promoting this safer behavior,” Delgado said. “Combining all available information and our work in nudging appears especially promising.”

by: Frank Otto(Francis.Otto@pennmedicine.upenn.edu)

Penn Medicine Nudge Unit