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This article compares the Land Rover Defender 130 and the Lexus GX550 in terms of their off-road capabilities and luxury features. The Land Rover Defender 130 Outbound has a shadowy aesthetic enhancement and features a super- and turbocharged 3.0-liter inline-six engine. The Lexus GX550 is built on the redesigned Toyota Land Cruiser and offers adventure-themed Overtrail models with rugged upgrades. The article concludes that the Defender is the better choice due to its superior off-road capabilities and refinement, although the GX550 is praised for its comfortable interior and spaciousness. The article provides test results and scores for various aspects of the vehicles. [Extracted from the article]

The article "Police pursuit not a solo race" reports on a case of police pursuit. A defendant was accused of driving at excessive speed in traffic in order to flee from the police. However, the district court only convicted him of a minor offense. The prosecution's appeal and revision were unsuccessful. The higher regional court determined that the defendant did not fulfill the criteria of the Criminal Code, as his intention was not to reach the highest possible speed in order to successfully escape from the police. It was also established that the defendant did not participate in a motor vehicle race. [Extracted from the article]

• A novel framework named Event-Aware Graph Attention Fusion Network is proposed. • A SEE block is proposed to learn the direct spatial correlations of event. • A TE-Attention block is proposed to learn the temporal correlations of event. • A gated fusion mechanism is proposed to fuse the features of road traffic and event. • A dynamic event graph constructor is proposed to construct dynamic graph for event. Accurate road traffic speed prediction has a critical role in intelligent transportation systems and smart cities. This task is very challenging because of the complexity of road network structures, as well as various other unpredictable and ad hoc factors. Most existing traffic speed approaches handle external factors such as weather, holidays, and traffic accidents to enhance prediction accuracy. However, they ignore the impacts of social events or only simply embed them instead of learning the spatio-temporal representations. To address this issue, we design a novel framework named an event-aware graph attention fusion network (EGAF-Net) to effectively capture the spatiotemporal dependencies, including event impacts, in road networks based on an encoder-decoder architecture for traffic speed prediction. First, we utilize an ST-Speed attention block to model the spatial correlations among road segments and capture traffic speed changes. Second, we develop a spatial event embedding block exploiting a novel algorithm based on the node2vec approach, a new dynamic event graph constructor which produces learnable graphs utilized in graph convolution layers, and a temporal event attention block to learn the spatial and temporal representations of events. Finally, we propose a gated fusion mechanism to fuse the spatio-temporal correlations in road networks and the representations of events. Extensive experiments conducted based on the Q-Traffic, Q-Eastern-Traffic and Q-Western-Traffic datasets demonstrate the effectiveness of EGAF-Net over robust baselines. [ABSTRACT FROM AUTHOR]

• We use three different features to calculate the dynamic adjacency matrix correlated with the dynamic correlation matrix. • We design a novel deep learning-based framework to learn dynamic spatio-temporal dependencies. • We conduct experiments on two real-world datasets in predicting urban traffic flow and traffic speed, respectively. • We collect two cities' traffic data, and make predictions for traffic flow and speed, respectively. Moreover, we conduct ablation study to prove the effectiveness of each module. Accurate urban traffic prediction is a critical issue in Intelligent Transportation Systems (ITS). It is challenging since urban traffic usually indicates high dynamic spatio-temporal correlations, leading to uncertainty and complexity of traffic status. Since the transportation network is a graph structure practically, existing works have applied Graph Convolutional Network (GCN) on urban traffic prediction with a pre-defined adjacency matrix based on node distance or connectivity. However, in many urban traffic scenarios, spatio-temporal dependencies among traffic data usually change over time, so using a fixed adjacency matrix cannot describe the dynamic dependencies. To track the dynamic spatio-temporal dependencies among traffic data, we propose a novel deep learning framework, Dynamic Traffic Correlation-based Spatio-Temporal Graph Convolutional network (DTC-STGCN), to forecast traffic flow and speed accurately. DTC-STGCN extracts a dynamic adjacency matrix from different traffic characters to describe dynamic spatio-temporal correlations. Moreover, an attention and dynamic adjacency matrix-based GCNs framework is proposed to capture urban traffic dynamic spatial features, while a long-short-term memory network (LSTM) is used to capture urban traffic temporal features, respectively. Finally, we feed the spatio-temporal features generated by GCN and LSTM, with real road segments into a hybrid graph convolution framework to simultaneously model the dynamic spatial and temporal dependencies for traffic predictions. The experiments on two real-world datasets demonstrate that the proposed DTC-STGCN model consistently outperforms the state-of-the-art traffic prediction baselines on MAE and RMSE over 10%, and achieve a stable performance for two specific tasks (long-term traffic prediction and peak time prediction). And ablation study validates the effectiveness of dynamic adjacency matrix, attention mechanism, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, various disturbances in urban transportation data acquisition/processing/storage lead to the inevitable data missing problem, which undermines the valuable traffic information and greatly threats the reliability of existing benchmark traffic prediction models. Inspired from the powerful generative learning ability of GANs, we propose an integrated spatiotemporal Data imputation Graph Attention Generative Adversarial Networks (Di-GraphGAN) for accurate and efficient spatial-temporal traffic forecasting under data missing scenarios. Specifically, we first propose a traffic data imputation module named DI-LSTM, which adopts the architecture of LSTM Network with an extra Time Damping unit to accurately estimating the missing values. Then, we facilitate Di-GraphGAN with an original developed Task-Efficient Graph Attention Networks (TE-GAT) for better graph representation learning and a Temporal Contextual Attention (TCA) mechanism to capture the dynamic spatiotemporal traffic patterns. Finally, extensive evaluations are conducted on two real-world traffic speed datasets from China, demonstrating that Di-GraphGAN achieves state-of-the-art performance in both traffic forecasting and spatiotemporal data imputation tasks. • We propose Di-GraphGAN, an enhanced framework for end-to-end traffic data imputation and prediction in various missing scenarios. • DI-LSTM, an RNN-based data imputation module with Time Damping unit, is proposed to impute missing traffic values and enhance prediction accuracy. • We introduce TE-GAT, which employs Filter Gate to retain only important edges for neighbor aggregation, reducing the computational overhead. • Comprehensive experiments on datasets from Hangzhou and Guangzhou show Di-GraphGAN consistently outperforms benchmarks. [ABSTRACT FROM AUTHOR]

It is common that there are large trucks mixed in the road, and the traffic situation has become more complicated. Moreover, the existence of large trucks and adaptive cruise control (ACC) vehicles will affect the traffic speed in mixed traffic. Based on the hybrid vehicles including ACC vehicles and conventional vehicles, we in this paper establish a heterogeneous continuum model with large trucks. The stability conditions and KdV-Burgers equation of heterogeneous vehicle groups with large trucks are derived. Through the numerical simulation of the evolution of heterogeneous car groups, it is known that the congestion of traffic system will be obviously restrained owing to increasing the proportion of large trucks and reducing the optimization speed of car groups in the road. In addition, due to the higher permeability of ACC vehicles, the traffic system becomes more steady, implying that ACC vehicles also promote the improvement of the transportation system. [ABSTRACT FROM AUTHOR]

The article discusses the experience of living with a Citroen C4 X for six months. Initially, the author was unsure about the car's styling but grew to appreciate its unique design. The car's interior is described as somber but spacious, making it suitable for family use. The Citroen C4 X is praised for its comfort and ease of use, particularly on long journeys. While it may not be the most exciting car to drive, it offers good fuel economy and reliability. Overall, the Citroen C4 X is recommended as a comfortable and practical alternative to SUVs. [Extracted from the article]

This article introduces a non-linear grey Fourier model utilizing the kernel method to better characterize seasonal traffic speed with fluctuating trends. The linear trend term in the grey Fourier model is replaced with feature mapping, resulting in a non-linear form. Utilizing Lagrange multipliers and a kernel function, the parameter estimation is transformed into a linear equation-solving problem. Order selection is carried out via power spectrum analysis, and hyper-parameters are determined using Bayesian optimization. Two numerical examples from real problems are presented to validate the model's performance. The results indicate that the developed model outperforms nine models across four categories. Specifically, the model is applied to predict traffic speed in Guangzhou, and comparative results with four neural networks demonstrate its commendable performance. • A non-linear grey Fourier model is developed by using kernel method to characterize non-linearity. • The hyper-parameters are efficiently optimized using Bayesian optimization algorithm. • The model performance is verified through two numerical examples from real problems. • The model utility is confirmed through large-scale traffic speed predictions. [ABSTRACT FROM AUTHOR]

To estimate travel time through a composite ring road, a viscoelastic traffic flow model is developed by assuming traffic sound speed on empty road is just equal to free flow speed. Based on the viscoelastic model, numerical tests of traffic flows were conducted to provide node traffic speed for estimating travel time. The composite ring road with three ramp intersections has five parts, each part is composed of a tunnel, a horizontal, an uphill and a downhill segment. The length of uphill segment is the same as the length of downhill segment, both are 1 km, while the tunnel length can be 1, 0.5, and 0.1km. To validate the reliability and feasibility of the viscoelastic traffic flow model, the Navier–Stokes like model Zhang (2003) is extended and adopted to provide the counterpart numerical results for comparison. It was found that in case without ramp effects any tunnel inlet becomes a starting point of traffic congestion region when initial density normalized by its jam value is not below 0.2. But in case with ramp effects, even if initial density is 0.15, downstream an on-ramp intersection, any tunnel inlet can also induce traffic shock when the tunnel is positioned upstream another off-ramp intersection. The off ramp flow can shorten mean travel time and increase its root mean square value significantly. The fitted expression of mean travel time has the form σ tf = A ρ 0 m + b where A = 8. 9686 , m = 1. 6260 , b = 0. 8424 , ρ 0 is the initial density varying from 0.1 to 0.625. [ABSTRACT FROM AUTHOR]

Number one: if you were asked to create a luxury SUV that drove with the alacrity the Aston Martin name demands, you might seek to pull together a team and a parts toolkit not dissimilar to those behind this car, the most important model in the marques history. It's here, where the DBX's enormous air-sprung shocks dwarf those of the Aston sports car hung nearby, that the enormity of the project - "New platform, new factory, new everything", says Becker - is made abundantly clear. For instance, when we drove a DBX prototype in Oman, the front suspension behaviour dented confidence on turn-in. [Extracted from the article]

/ ORIGINS / The Hayabusa made you consider things you'd never considered before. The Hayabusa ripped through fifth gear like most bikes did third. Performance progress is so typically "last year, plus 3 percent" that when a bike like the Hayabusa comes along, it sends a shock through the system that tends to change the performance landscape. [Extracted from the article]

The pandemic torpedoed Aston's original DBX launch plans (set for the perfect light and wide open spaces of California), leaving ever so slightly less sun-drenched Silverstone to host our first meeting with the car in finished, production-ready guise. MOMENT OF clarity number one: if you were asked to create a luxury SUV that drove with the alacrity that the Aston Martin name demands, you might seek to pull together a team and a parts toolkit not dissimilar to those behind this car, the most important Aston in the marque's history. The active centre transfer case, for example, (to which Aston hooks up a carbonfibre prop shaft) is borrowed from the AMG E63 S courtesy of Aston's Stuttgart connection. [Extracted from the article]

The article reports that aerial cable systems send tourists to the top of Mount Carmel and to the ancient fortress of Masada. Topics inlcude the city of Haifa plans a public aerial cable car route from the central bus station to the Technion, the nation's premier engineering school, and to the University of Haifa, the visitors to the Palestinian territories can ride a cable car from Jericho up the Mount of Temptation, and the traditional scene of Christ's encounter with the devil.

Tesla Inc. has issued a recall for over 2 million vehicles due to safety flaws in its Autopilot driver-assistance system. The National Highway Traffic Safety Administration (NHTSA) conducted a defect investigation and found that Tesla's means of keeping drivers engaged were inadequate. This is the second recall this year for Tesla's automated-driving systems, which have faced increased scrutiny following numerous crashes. Tesla's Autopilot system comes standard on all new vehicles and uses cameras to assist with steering and matching vehicle speed with surrounding traffic. The recall is expected to address these safety concerns and improve the system's functionality. [Extracted from the article]

The article discusses the significant role of cars in American life. It highlights the impact on suburban living, urban design, and accessibility. It emphasizes that American cities prioritize car-centric designs, leading to faster traffic and larger, more accessible areas. It reports challenges like costs and transportation disparities, and it mentions that the American lifestyle's reliance on cars might persist despite alternative options.

Highlights • Good performance of self-stabilizing effect on macroscopic traffic flow. • Avoiding the demand for real-time communication and data accuracy. • Relieving traffic congestion in theoretical derivation and simulation experiments. • Reducing the harm caused by the fuel consumption and emissions to the environment. Abstract In recent years, more and more scholars tend to use vehicle information to achieve traffic flow stability in the study of micro traffic flow. Any change of the micro-control mode can also have an important impact on macro traffic flow. In order to study the effect of self-stabilizing on macroscopic traffic flow, a new continuum traffic flow model is proposed in this paper. By utilizing the historical speed of each vehicle itself, we discuss the stability of macro traffic flow to clarify the impact of the self-stabilizing control strategy of historical speed on the macro traffic flow. Through both linear and nonlinear analysis, it is found that the self-stabilizing control effect can play a significant stabilizing role on macro traffic flow which can be proved by simulations. In addition, we also investigated the impact of the speed self-stabilizing control mode on other properties of macro traffic flow such as shock-wave propagation patterns with such good performance in the simulation. Meanwhile, we discuss the fuel consumption and emissions carried out by the self-stabilizing control strategy effect. It indicates that the self-stabilizing control strategy in macroscopic traffic flow not only can ease the traffic congestion but also can reduce the harm caused by the fuel consumption and emissions to the environment. [ABSTRACT FROM AUTHOR]

The apparatus further includes a vehicle route planner to plan permissible routes for the vehicle, using the vehicle dynamics, the predicted positions, and a route selector to check whether or not the vehicle routes have collision risks and to select one of the vehicle routes for the autonomous vehicles. The actively operating autonomous vehicles are centrally controlled in a control management center to satisfy the ride requests using vehicle data, for example, vehicle speed data, vehicle acceleration data, vehicle trajectory data, geographical position data for the vehicle, vehicle navigation data. The vehicle features include permissible vehicle dimensions and permissible vehicle dynamics of the multi-featured autonomous vehicles of the roads. [Extracted from the article]

Accurate traffic speed forecasting is challenging because of complex spatiotemporal correlations of traffic data. Some studies have recognized that correlations among sensors change gradually and have constructed dynamic graphs to reflect this change. However, they ignored the temporal dependencies among dynamic graphs, resulting in an inability to capture the deep dynamic dependencies among sensors. Few studies have explored the hybrid interaction patterns of static and dynamic graphs, hindering the adequate exploration of spatial dependencies. Therefore, a novel deep learning model is proposed in this study to address these problems and produce accurate traffic speed forecasting. First, a new graph generation method is proposed to capture the deep dynamic dependencies among sensors, which exploits historical information of dynamic graphs to develop temporal dependencies among dynamic graphs. Then, a new fusion strategy is proposed to investigate the hybrid interaction patterns of static and dynamic graphs, which effectively mines hidden information in graphs to fully extract spatial dependencies. Finally, a new spatiotemporal network architecture is proposed, which unifies the proposed graph generation method and fusion strategy into a consistent framework and yields the final forecasting results. Experimental results on two real-world traffic datasets indicate that the proposed model outperforms state-of-the-art models. [ABSTRACT FROM AUTHOR]

Traffic data imputation plays a crucial role in Intelligent Transportation System (ITS) applications when handling missing data. Previous methods have primarily focused on restoring traffic states for road links equipped with sensors but sometimes suffer from the issue of large-scale missing data. However, these approaches are inadequate for addressing the special scenario where multiple road links lack sensors, resulting in a complete data absence. To tackle this issue, this study proposes an Integrated Deep Learning for Traffic State Reconstruction (IDL-TSR) framework, which aims to reconstruct the network-wide traffic state using sensor data from a limited number of links. Specifically, we employ the Structural Deep Network Embedding (SDNE) to first embed high-dimensional and sparse traffic data (i.e., spatiotemporal images) into a low-dimensional space. This embedding process assists in alleviating the negative impact of data sparsity on the generative capacity of the Generative Adversarial Network (GAN) and enhances the spatiotemporal mining capability of the IDL-TSR. Then, a typical GAN with Wasserstein divergence (WGAN-div) is used to reconstruct traffic states for road links without sensors by leveraging the observed traffic data from the other links. To evaluate the performance of the proposed IDL-TSR framework, we conducted numerical experiments using traffic speed data obtained from Didi Chuxing in Chengdu, China. The results demonstrate that the IDL-TSR framework effectively reconstructs traffic speed states at a network level, surpassing other counterparts even under extreme missing rates. [ABSTRACT FROM AUTHOR]

On a ride home from the vet clinic one day, my trailer blew a tire. My car had front-wheel drive (four-wheel drive cars were a thing of the future), and I was a good driver - so I decided to go. I appreciate the service truck drivers who will change a loaded horse trailer tire on the side of a busy highway. [Extracted from the article]

A well-known optimal velocity (OV) model describes vehicle motion along a single lane road, which reduces to a perturbed modified Korteweg–de Vries (mKdV) equation within the unstable regime. Steady travelling wave solutions to this equation are then derived with a multi-scale perturbation technique, where the travelling wave propagation coordinate depends upon slow and fast variables. The leading order solution in the hierarchy is then written in terms of these multi-scaled variables. At the following order, a system of differential equations is highlighted that govern the slowly evolving properties of the leading solution. Next, it is shown that the critical points of this system signify travelling waves without slow variation. As a result, a family of steady waves with constant amplitude and period are identified. When periodic boundary conditions are satisfied, these solutions’ parameters, including the wave speed, are associated with the driver’s sensitivity, a ˆ , which appears in the OV model. For some given a ˆ , solutions of both an upward and downward form exist, with the downward type corresponding to traffic congestion. Numerical simulations are used to validate the asymptotic analysis and also to examine the long-time behaviour of our solutions. [ABSTRACT FROM AUTHOR]

The reports on the need for congestion zone in New York. It mentions the city has the worst traffic in America, average speed has decreased between 2010 and 2019 in the proposed zone. It also mentions to avoid paying tolls in Manhattan, lorries are likely to use the Cross-Bronx expressway, a congested motorway.

The article focuses on the impact of street designs on the transit of low income communities. It includes the views of village manager of Richton Park in Chicago, Illinois Regan Stockstell on complete street policy. It also discusses the need of designing street infrastructure to reduce traffic speeds in crowded areas for the safety of walkers and bikers.

The article discusses the author's experience living with a Ford Ranger pick-up truck. The author highlights the practicality and versatility of the truck's load bed, which has been used to transport various items, including kitchen appliances and furniture. The author also mentions some drawbacks, such as the clunky downshifting and acceleration of the adaptive cruise system, as well as the need to wait for the drivetrain to engage after starting the vehicle. However, overall, the author praises the Ranger for its quiet engine, good power delivery, fuel efficiency, and comfortable ride. The article concludes by stating that the Ranger is a top choice in the pick-up truck category. [Extracted from the article]

Italian construction companies Webuild and Salcef have won a €291 million ($289.6m) contract to upgrade a section of Romanian railway Caransebes-Timisoara-Arad to improve connections between Bucharest and the western part of the country. The section crosses an urban area of Timisoara and includes the construction of: 5 bridges 4 overpasses 3 stations The consortium's makeup is: Webuild - 72.1% Salcef - 27.9% The Rhine-Danube Corridor project, including Lot 3, is being financed by the Operational Programme for Large Infrastructure (POIM) for Romania and is aiming to increase traffic speed and capacity along the Caransebes-Timisoara-Arad railway between Bucharest and Timisoara. [Extracted from the article]

Roads with 20mph limits did, however, experience less traffic, according to the research. Researchers analysed 76 roads in Belfast before and after 20mph limits were introduced, comparing them with other roads in Northern Ireland where 30mph or 40mph zones remained. REDUCING speed limits to 20mph has little effect on road safety, according to a study in the Journal of Epidemiology and Community Health. [Extracted from the article]

• DDPG is used to dynamically select the Pareto solution of MOICA. • TCN and SRU are used as the main predictors. • The proposed model is compared with nineteen mainstream forecasting models. Traffic congestion is a difficult problem that restricts the construction of urbanization. Spatiotemporal traffic speed forecasting technologies can provide effective technical support for alleviating traffic congestion and ensuring vehicle travel safety. The ensemble learning algorithm is a hot topic in traffic speed modeling. In this field, previous ensemble learning methods mainly adopt the principle of static modeling, which limits the learning ability of the model to dynamic features. To solve this problem, in this paper, a new dynamic ensemble deep deterministic policy gradient recursive network is presented for traffic speed forecasting, which comprises three main modeling steps. In step I, the simple recursive network (SRU) and temporal convolution network (TCN) methods are used as the main predictors to build the traffic speed forecasting model. In step II, the multi-objective imperialist competitive algorithm (MOICA) integrates these neural networks by optimizing the weight coefficients and generating the Pareto solution set. In step III, the deep deterministic policy gradient (DDPG) method dynamically selects the Pareto optimal solution of the MOICA according to the changes in the traffic speed data. The MOICA and DDPG dynamically integrate the forecasting results from the SRU and TCN to obtain the final results. Based on the experimental analysis results, several conclusions can be given as follows: (a) the model presented in this paper can obtain accurate traffic speed forecasting results with MAPE values below 4% on all data sets. (b) the proposed model can achieve better results than thirteen alternative models and four proposed models from other researchers. (c) the proposed model can improve the prediction performance of traditional predictors by about 6%. [ABSTRACT FROM AUTHOR]

We tested them against Ford's new crossover competitor, the Bronco Sport. FOR YEARS, IF YOU WANTED TO MOVE PEOPLE AND stuff, you bought an SUV, and you accepted the body roll and expensive gas mileage that came with a body-on-frame truck. Ford also packed the Sport with standard tech, including lane-centering, cruise control that adapts to traffic speeds, and automatic evasive steering. [Extracted from the article]

Circle City The Indy Autonomous Challenge THE FUTURE IS NOW. Mitchell says the IAC is about much more than driverless cars. The Indy Autonomous Challenge (IAC), the world's first head-to-head, high-speed autonomous race, arrives at IMS this month to prepare for its turn on the bricks on October 23. [Extracted from the article]

Sygic, a navigation app developer, has introduced a new feature that allows smartphone users to use their phone's camera to read road signs, including LED temporary limits on motorways. However, the feature has limitations and does not work well in less than ideal conditions. Google Maps, on the other hand, offers a free and more user-friendly navigation experience, using data from millions of users to track traffic speeds and locate addresses and businesses accurately. While Google Maps generally provides precise speed limit information, it may not update 'smart' or temporary limits, so drivers should still pay attention to road signs. [Extracted from the article]

(Bloomberg) -- An explosion in the growth of crypto networks vying to take market share from Ethereum is unlikely to threaten the dominance of the world's most used blockchain, according to Joey Krug, co-chief investment officer at digital-asset investment firm Pantera Capital. Pantera is one of the earliest digital-asset investment firms and ranks in the top five of crypto-focused funds with $5.8 billion in assets. Ethereum Beats Rivals with "Trade-Offs", Pantera's Krug Says. [Extracted from the article]

Posh-car tech for UK-built SUV NISSAN has flung the doors of the next Qashqai wide open to give us an in-depth look at the upcoming crossover's all-new interior and on-board technology. So, one side is trimmed to match the carpet in the SUV's boot, while the other features a wipe-down plastic coating, which Nissan says will make the boot floor easier to clean. [Extracted from the article]

IF YOU'VE EVER FELT CAUGHT BETWEEN THE SCYLLA OF AN HGV looming in your rear-view mirror and the Charybdis of average speed cameras in a set of motorway roadworks, help may be at hand. Yet while Highways England is raising limits in motorway roadworks, local authorities are rushing to lower them on A- and B-roads. Clearly, motorways are unique in the way they operate, but human beings behave the same way whether they're on a motorway or a B-road, and there are plenty of parallels in the way drivers are affected by each environment. [Extracted from the article]

The article analyzes the impact of the economic crisis on the speed and intensity of traffic on the approaches to large urban centers, describing the Barcelona case, which has a system for monitoring traffic speeds in the arterial network. The economic recession since 2008 has involved a 9.2% reduction in traffic intensities and a parallel decrease in levels of recurrent congestion, which results in increases in traffic flow speeds rush hour at reaching in some places 75% higher values than those recorded in 2007 on a working day. [ABSTRACT FROM AUTHOR]

The article offers information on several reports issued by the U.S. Federal Highway Administration's (FHWA) Office of Research, Development & Technology during 2015. One report is on geosynthetic reinforced soil (GRS) technology. One report is on factors affecting operating speeds and safety on rural and suburban roads in the U.S. Another report is on improvements in pedestrian crossings in the U.S., especially the rectangular rapid-flashing beacons.

This PoP will make phoenixNAP part of DE-CIX's global interconnection ecosystem, which includes the largest carrier and data centre neutral ecosystem in North America as well as multiple IXs in Europe, the Middle East, North America, and Asia. Phoenix is one of the fastest-growing US commerce hubs, with demand for interconnectivity services on the rise. phoenixNAP provides diverse carrier connectivity options and network redundancy through its global network of 17 data centre locations. [Extracted from the article]

In fact, many car companies have given up trying to beat smartphone apps and are making integration a key part of their new vehicles' infotainment systems, usually in the form of Apple CarPlay for iPhones, or Android Auto for non-Apple devices. How we tested them WE set the apps the same navigational task, using an iPhone X connected to a Renault Zoe via Apple CarPlay, if the app was compatible. Yes/yes Contact: wego.here.com THE Here WeGo app has done well in past tests, and it continues to impress, especially since it's free to use and has seamless CarPlay and Android Auto integration. [Extracted from the article]

Sygic's latest app has two new features and has more than halved in price. "Sygic's latest map app has two new features and has more than halved in price" Mini test UPDATED CHALLENGER Sygic Car Navigation Price: £12.99 (Lifetime, free seven-day trial) Apple CarPlay/Android Auto?. [Extracted from the article]

Described as having the ability to transform "a traditional monolithic architecture through decomposition, containerisation and function productisation", Vodafone Idea said it is the first telecom to deploy a charging solution with cloud-native architecture. Confirming the development, Vishant Vora, chief technology officer at VIL India, said: "We are shifting 100% of our IT application stack onto the VIL Universal Cloud, the industry's largest private cloud deployment. [Extracted from the article]

As a rule of thumb, tyre-road interaction is the main source of noise at above 25-35mph for cars and at above about 40-43mph for lorries, with engine noise predominating at lower speeds. Tyre-road noise in all vehicles increases strongly with speed: a vehicle travelling at 60mph is twice as noisy as one going at 30mph. There is, though, one important caveat: traffic noise will not fall automatically with a drop in vehicle numbers if that simply allows the remaining traffic to speed up. [Extracted from the article]