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This paper aims at delivering new empirical evidence as to the effectiveness of 20 mph speed limits in rural areas. For this purpose, speed and traffic data were drawn from the area of the Scottish Borders, UK, where the local Council led the rollout of a 20 mph speed limit trial in 97 villages and towns from October 2020. This intervention is considered as one of the first of its kind in the UK and overseas, as it was carried out on a large scale, in predominantly rural areas. To evaluate the impact of the 20 mph speed limit on vehicle speeds, we conducted a "before–after" quantitative analysis using traffic and speed data collected in different waves before and after the intervention. The descriptive analysis showed that both mean and 85th percentile speeds reduced directly after the introduction of the 20 mph speed limit (by 3.1 mph and 3.2 mph, respectively), and that such speed reductions were largely maintained even up to eight months after the onset of the intervention. The largest speed reductions were observed in locations with high-speed patterns before the intervention, and especially in those having mean speeds greater than 25 mph before the intervention. Both non-parametric and parametric statistical tests, which were conducted using approximately five million speed observations, showed that the observed speed changes were statistically significant for the vast majority of cases. Linear regression models were also estimated confirming the significant impact of the 20 mph limit on vehicle speeds, while controlling for the influence of traffic volume. Overall, the findings of this study will likely assist in filling an evidence gap regarding the effectiveness of 20 mph speed limits in rural settlements. They can also provide encouragement to those local authorities in the UK and abroad that are currently actively examining the possibility of setting the 20 mph as the default limit in built-up areas. [ABSTRACT FROM AUTHOR]

This paper estimates the negative effects of motorised road traffic on the travel and walking behaviour of local residents in Great Britain and the cost of the wider negative impacts on local economic activity, external effects of motorised transport, social exclusion, neighbourhood social capital, self-rated health, and subjective wellbeing. We use the results of a survey (n = 3038) and models linking the characteristics of roads and pedestrian crossing facilities in each participant's local area, travel and walking behaviour, and the level and monetary value of the wider impacts of travel behaviour. The costs of road traffic borne by local communities were estimated as £31.9 billion per year, i.e. 1.6% of the Gross Domestic Product, or £631 per person. This value varies within the interval £569-£698/person/year, depending on the assumptions made. The highest estimated costs of motorised road traffic are reduced neighbourhood social capital (£236) and reduced subjective wellbeing (£196/person/year). The costs for some population segments, such as London residents, city residents, and people aged 25–34 are higher than the average. The characteristic of roads causing the highest costs is volume of motorised traffic above low (£264/person/year), followed by multiple lanes (£148), traffic speeds above low (£119), and absence of a median strip (£60). By quantifying for the first time, at the national level, the costs of the negative community effects of motor vehicles, this study provides evidence supporting policies to reduce traffic volumes and speeds and reallocate roadspace to pedestrians. [ABSTRACT FROM AUTHOR]

Texture is required on pavements to provide safe and comfortable ride performance for users. This paper provides the first meaningful analysis of a long-term study of texture data obtained using TRACS (TRAffic Speed Condition Survey) at a site in the UK. TRACS data were collected annually, over a 2 km stretch of motorway from 1995 to 2019. A new data analysis approach utilising time series data with spectral analysis and spatial filtering procedures is presented. The results reveal that the approach enables legacy TRACS laser profile Sensor Measured Texture Depth (SMTD) data to be used to determine long term rates of change in road surface macrotexture. Thus, the technique has unlocked the potential for SMTD data collected annually for 7000 km of the Strategic Road Network in the UK, to inform road maintenance programmes by extrapolation. Additionally, results expose a systematic periodicity occurring each year within the SMTD data studied, corresponding to longitudinal oscillations with wavelengths between 33 and 62 m. The time-invariant periodicity of these oscillations suggests that it is 'imprinted' in the early life of the pavement. 'Imprinting' may theoretically arise with cyclic tyre loading applied by the suspension systems of heavy vehicles or during road construction. [ABSTRACT FROM AUTHOR]

Although the fundamental traffic diagram provides the characteristics of a typical road traffic speed-flow relationship, little consideration has been given to the impact of adverse weather conditions on the relationship and the subsequent impact on local speed-flow. For the first time, this study uses precipitation radar along with a state-of-the art traffic information system to ascertain the relationship between speed-flow and precipitation on a UK transport corridor at the local (junction to junction) scale. It is evident that precipitation causes a significant reduction in speed and maximum flow on many links of the corridor as well as a downward reduction in the overall speed-flow relationship. With increased instances of heavy precipitation predicted in the UK as a result of climate change, these findings highlight the subsequent impact on journey travel times and associated economic costs. [ABSTRACT FROM AUTHOR]

An essay is presented that discusses pedestrian conditions in light of mass motorization as exemplified by the activism of the British activist movement Pedestrians' Association (PA). Topics include motor vehicle accident deaths among pedestrians in Great Britain, the development of traffic laws, and problems with urban traffic congestion and high traffic speeds. The periodical "The Pedestrian" is noted.

The article presents an analysis of the factors that contributed to road accidents in Great Britain from 2005-2009. The analysis showed that 68 percent of all accidents at all levels of accident severity were attributed to driver or rider error or reaction, followed by injudicious action such as following too close, travelling too fast and exceeding the speed limit, at 26 percent. For fatal, serious and slight accidents, the factors include loss of control and failure to look properly.

Road freight transport is one of the major contributors to greenhouse gas and air pollutant emissions. Hence, it is increasingly regulated in urban areas to reduce its impact on the environment and human health. The rich data available from telematics has the potential to provide high-resolution information, yet research has not been conducted to understand, evaluate, and ultimately improve the operation and impacts of urban road freight. This paper demonstrates the role of vehicle telematics data in enabling quantitative assessment of the impacts of urban freight transport for the effective management of relevant policies. We present a comprehensive data-driven approach that provides a robust quantitative evaluation and applies it to a case study of the London Lorry Control Scheme (LLCS) policy in UK. We show that, for the studied freight operator, the LLCS policy affects their drivers' route choice both inside and outside the restricted hours. The spatio-temporal distributions of different parameters including traffic speeds, fuel economy and emissions at different times of the day are compared and analyzed. The results indicate that the unintended consequences of urban freight transport policies can include an extra 15% vehicle-km traveled per trip and 12% liter of fuel consumed per trip. [ABSTRACT FROM AUTHOR]

Several models have been created over the years that are used in simulation packages to predict pedestrian speed and flow on sidewalks. However, the predictive power of these models has been weak. In this paper the results of a modeling exercise in the United Kingdom is reported. A number of video surveys of pedestrian movement on sidewalks, which are representative of different levels of service, have been made. These videos have been analyzed to yield data on pedestrian flows and speeds and a variety of other variables. From this data regression models have been constructed and the predictive performance of these models assessed and found to perform reasonably well. The value of the models to designers is then discussed. [ABSTRACT FROM AUTHOR]

Texture is required on road pavements for safe vehicle braking and manoeuvres. This paper provides a unique analysis of long-term texture obtained using traffic speed condition survey (TRACS) data from 14 sites, located along a north to south transect spanning the longest highway in the UK. A total of 19 years of sensor measured texture depth (SMTD) data have been analyzed using spatial filtering techniques and compared with meteorological and traffic datasets. The results for hot rolled asphalt (HRA) surfaces reveal that changes to SMTD follow a linearly increasing trend with time. The "rate of change" is influenced by the order of magnitude of annual average daily traffic (AADT), when factored for the percentage of heavy goods vehicles. This linear trend is disrupted by environmental parameters, such as rainfall events and seasonal conditioning. In the summer, this signal is evident as a transient peak in the "rate of change" of texture greater than 0.04 mm, and in the winter as a reduction. The transient changes in texture corresponded to above average rainfall occurring in the week prior to SMTD measurement. The signal observed demonstrates an inverse pattern to the classically understood seasonal variation of skid resistance in the UK, where values are low in the summer and high in the winter. The findings demonstrate for the first time that texture measurements experience a seasonal signal, and provide compelling evidence pointing toward surface processes (such as polishing and the wetting and drying of surface contaminants) causing changes to texture that are affecting seasonal variation in skid resistance. [ABSTRACT FROM AUTHOR]

• A centralised digital architecture is developed to handle large imbalanced data. • A Regional–Convolution Neural Network (R-CNN) model is used to generate conflict data. • A Deep Neural Network (DNN) model is employed to predict real-time traffic conflicts. • Traffic variables and Safety Surrogate Measures (SSM) are used as inputs to DNN model. • This traffic conflict detection technique is suitable for ADAS, CVs and AVs. Recently, technologies for predicting traffic conflicts in real-time have been gaining momentum due to their proactive nature of application and the growing implementation of ADAS technology in intelligent vehicles. In ADAS, machine learning classifiers are utilised to predict potential traffic conflicts by analysing data from in-vehicle sensors. In most cases, a condition is classified as a traffic conflict when a safety surrogate (e.g. time-to-collision, TTC) crosses a pre-defined threshold. This approach, however, largely ignores other factors that influence traffic conflicts such as speed variance, traffic density, speed and weather conditions. Considering all these factors in detecting traffic conflicts is rather complex as it requires an integration and mining of heterodox data, the unavailability of traffic conflicts and conflict prediction models capable of extracting meaningful and accurate information in a timely manner. In addition, the model has to effectively handle large imbalanced data. To overcome these limitations, this paper presents a centralised digital architecture and employs a Deep Learning methodology to predict traffic conflicts. Highly disaggregated traffic data and in-vehicle sensors data from an instrumented vehicle are collected from a section of the UK M1 motorway to build the model. Traffic conflicts are identified by a Regional–Convolution Neural Network (R-CNN) model which detects lane markings and tracks vehicles from images captured by a single front-facing camera. This data is then integrated with traffic variables and calculated safety surrogate measures (SSMs) via a centralised digital architecture to develop a series of Deep Neural Network (DNN) models to predict these traffic conflicts. The results indicate that TTC, as expected, varies by speed, weather and traffic density and the best DNN model provides an accuracy of 94% making it reliable to employ in ADAS technology as proactive safety management strategies. Furthermore, by exchanging this traffic conflict awareness data, connected vehicles (CVs) can mitigate the risk of traffic collisions. [ABSTRACT FROM AUTHOR]