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UK has one of the world's largest automobile markets, with a growing share of alternative fuel vehicles (AFVs), and a great importance to the economy. However, an extensive insight into the price drivers of conventional vehicles (CVs) and AFVs is lacking in the literature. Therefore, this paper estimates the determinants of automobile prices, applying a hedonic pricing model to a novel, extensive dataset of 2008–19. Using adaptive Lasso and weighted quantile regressions, the key drivers of prices are performance and size, with the effect growing stronger with prices. More specifically, one standard deviation increase of engine power and weight increases prices by 16.9% and 14.7% respectively. AFVs display a significantly greater sensitivity to changes in car characteristics compared to CVs, especially to changes in performance, emissions, and equipment. Furthermore, hedonic price indices show that about 65% of the price increase in 2008–19 was caused solely by improvements in vehicle quality. • Hedonic pricing model is applied to a new and extensive UK car market dataset. • Key drivers of prices are performance and size, with the effect growing with price. • One standard deviation rise in engine power & weight rises price by 16.9% & 14.7%. • AFV prices significantly more sensitive, especially to performance and emissions. • About 65% of the price increase in 2008–2019 caused by improvements to car quality. [ABSTRACT FROM AUTHOR]

It is important to understand real-world performance of fuel-saving technologies for heavy goods vehicles (HGVs). This paper concerns applying data mining to quantify the fuel-saving benefit of a low-rolling-resistance (LRR) tyre using in-service operational data. Two HGVs of the same specification were employed, with one using LRR tyres and the other conventional tyres. A smartphone-based data logger was developed to collect data from the HGVs' operations on publics roads for three months. Data from vehicle Controller Area Network, smartphone sensors, weather and elevation databases were collected. A data mining methodology was developed to mine data segments representing dynamically similar driving conditions between the two HGVs, and compare their fuel consumption rates through fitting data to a theoretical vehicle fuel consumption model. It was found that at a 95% confidence level the LRR tyres exclusively brought about a fuel-saving benefit between 6.89% and 8.37% in typical UK motorway driving conditions. [ABSTRACT FROM AUTHOR]

Figure. Graphical abstract (LCS = low-cost sensors; Ref = reference; AQ = air quality; Met = meteorology; GAM = generalised additive model; RF = random forest; QRM = quantile regression model; NO 2 = nitrogen dioxide). Air quality is monitored before intervention, then the intervention is implemented, and finally air quality is monitored again, which is analysed and modelled employing different machine learning approaches. [Display omitted] In this paper, the impact of smart traffic interventions on air quality was assessed in Thatcham, West Berkshire, UK. The intervention linked NO 2 levels with the cycle time of the traffic lights. When NO 2 levels exceeded a certain threshold, the strategy was triggered, which reduced the traffic congestion by turning the traffic lights green. Eight Earthsense Zephyrs air quality sensors and nine inductive-loop traffic detectors were installed in Thatcham to simultaneously monitor the air quality and traffic flows, respectively. Compared to the pre-intervention period, the observed NO 2 concentrations decreased in June, July and August and increased in September 2021, however, this does not reveal the true effect of smart traffic intervention. Using the observed data on the days with- and without-exceedances, we developed two machine learning models to predict the Business-as-usual (BAU) air quality level, i.e., a generalised additive model for average concentration and a quantile regression model for peak concentration. Our results demonstrated that average predicted concentrations (BAU) were lower than the observed concentrations (with intervention) by 12.45 %. However, we found that peak concentrations decreased by 20.54 %. [ABSTRACT FROM AUTHOR]

This paper examines the impact of geographical location on the energy consumption of double deck battery electric buses operating in four UK cities. By considering seasonal variation in ambient temperature and an assumed battery degradation process, the implications on operational cost and carbon emissions were investigated. Using a MATLAB/Simulink model the total energy demand of a vehicle operating on the UKBC was simulated. Energy consumption was combined with hourly seasonal averages of carbon intensity (gCO 2 /kWh) and wholesale electricity costs (£) to assess charging at different times of the day and year. At the beginning of life, the range could decrease by up to 17.3% due to heating load requirements. From beginning of life to end of life, range was predicted to decrease by up to 26%. Whilst the average operational costs are relatively consistent across the four regions, the average carbon emissions can be five times higher depending on location. [ABSTRACT FROM AUTHOR]

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]

• Elaborate all the indicators influencing airport climate resilience. • Develop a climate resilience evaluation method using evidential reasoning. • Use big data to quantify the airport climate resilience. • Conduct an empirical study to analyse the climate resilience of the UK airports. Due to increased extreme weather events, climate adaptation has become an essential issue to be addressed by all transport infrastructures, including airports. This paper aims to develop a Climate Resilience Indicator (CRI) framework for assessing airport climate resilience, which for the first time, considers: climate exposure, vulnerability and adaptive capacity simultaneously and advances the development of climate risk analysis of airports to a point where their adaptation and resilience can be quantified under uncertainty in data. Climate-related data was collected from multiple sources to evaluate an airport's performance against each indicator. An evidential reasoning (ER) approach is used to evaluate each airport by integrating all the indicators to derive its final CRI score. The findings provide valuable insights into how urgently an airport needs to deal with climate change and reveal information to help with resource allocation for different airports nationally through proactive adaptation planning. [ABSTRACT FROM AUTHOR]

• Managing the organisational capabilities to ensure transport asset resilience is a challenge. • Need to determine systemic nature of organisable capabilities to develop a resilience strategy. • Use Social Network Analysis to examine seven transport projects impacted by adverse weather. • Major inter-related factors influencing transport asset resilience are identified. • A framework providing an insight into managing organisational capabilities is propagated. The management of the organisational capabilities needed to ensure the resilience of transport infrastructure assets is a challenge for governments worldwide. However, an absence of critical research in this area has exacerbated this challenge. The upshot, in this instance, has been the inability of governments to engender and enact an effective transport resilience strategy. This paper aims to fill this void and address the following research questions: (1) How do government organisational capabilities interact with one another to determine the resilience of transport projects? and (2) What is the best way to manage these organisational capabilities to aid a resilience strategy? Using Social Network Analysis, seven transport infrastructure case studies that were significantly impacted by natural hazards in the United Kingdom were used to answer the proposed research questions. The analysis revealed five inter-related factors are required to ensure the resilience of transport assets: (1) leadership; (2) reflexive (organisational) learning; (3) support from senior management; (4) a culture adept to resilience; and (5) continuous improvement (also investment) in asset absorbability, adaptability and vulnerability. The original contribution of this research is threefold: (1) a network providing a systematic visualisation of the interactions between organisational capabilities influencing asset resilience; (2) the prioritisation of governmental capabilities; and (3) the development of management framework providing a pathway that can accommodate environmental changes and asset resilience. [ABSTRACT FROM AUTHOR]