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![Figure 6. Fleet-averaged VKT trends of LDPV from 2010 to 2020 [11,16,23].](publication/365796546/figure/fig5/AS:11431281103234546@1669639321030/Fleet-averaged-VKT-trends-of-LDPV-from-2010-to-2020-11-16-23_Q320.jpg)
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Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ow...
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... addition, the average mileage of the newer vehicles (<=6 years) was lower than the older vehicles (>6 years), which may be due to the fact that the owners of newer vehicles need an adaptation period, whereas the owners of vehicles over 6 years have usually formed relatively stable travel habits. We also analyzed the characteristics of the annual mileage of the ten selected cities, as shown in Figure 5. In order to explore the factors influencing the mileage differences, we categorized these cities in two ways. ...
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... Data reduction techniques address the challenge of handling diverse data formats by transforming, aggregating, or summarizing data to make it more manageable and standardized for analysis. This allows organizations to extract valuable insights from disparate data sources and formats [27,28]. • Veracity: Veracity refers to the accuracy, reliability, and trustworthiness of big data. ...
... Data reduction techniques help address variability by standardizing data formats, transforming data into a consistent structure, and ensuring that the data remains usable and relevant across different contexts. By reducing variability, organizations can improve the consistency and reliability of their data analytics processes, leading to more accurate and actionable insights [28][29][30]. • Visibility: Visibility refers to the ability to gain insights and understand the underlying patterns and relationships within big data. With the increasing complexity and volume of data, achieving visibility into the data landscape becomes crucial for organizations to make informed decisions and identify opportunities for improvement. ...
... Data reduction techniques play a key role in enhancing visibility by simplifying the data landscape, removing noise and irrelevant information, and highlighting critical insights. By reducing complexity and improving visibility, organizations can better understand their data assets, uncover actionable insights, and drive strategic decision-making [26,28,30]. ...
Data reduction plays a pivotal role in managing and analyzing big data, which is characterized by its volume, velocity, variety, veracity, value, variability, and visibility. However, several surveys have been conducted to summarize these techniques in the field of big data, and there are several concerns that require attention, such as limited discussions of reduction techniques. Also, most of these studies focused on applications and only described their techniques. In contrast, this survey provides a comprehensive overview of data reduction methods, challenges, and future directions in the context of big data analytics in general concepts. This survey begins discussing the significance of data reduction in addressing the scalability and complexity issues inherent in big data processing. Subsequently, a classification data reduction method in big data is provided. For each category, the underlying principles, popular algorithms, and applications in big data analytics are highlighted. Moreover, the key challenges associated with data reduction in the era of big data, such as scalability, computational complexity, quality preservation, and interpretability, are found and discussed, while the importance of addressing these challenges to ensure the effectiveness and reliability of data reduction techniques in large-scale data analytics are reviewed. This survey can serve as a comprehensive reference for researchers, practitioners, and stakeholders interested in understanding and using data reduction techniques to address the challenges and opportunities posed by big data. Finally, tangible results of this study can be listed as introducing techniques for improving storage efficiency and faster computational processing by minimizing dataset size, while these techniques can enhance data analysis by removing redundancy and noise, leading to more accurate and actionable insights.
... As set out in Table 9, for SAVs, this would work out to 57,904 km/year per vehicle, only a small increase from the initial 55,903 km/year, and less than the BAU taxi mileage of 70,213 km/year. For private vehicles, this may mean an increase in annual VKT from 2014 by 25% to around 9000 km (without SAVs and/or shedding) This is potentially conservative, as another study suggested that the current average Chinese private mileage was 10,300 km, and they taxi mileage was around 80,000 km/year [63], though we (and other MARS-based studies) did not account for the empty running of vehicles. ...
In this study, we consider the introduction of new mobility services and technologies into the megacity of Beijing, China, as per developed strategy and action plans, in order to investigate their potential contribution to sustainable mobility. This includes population relocation (decentral-ization), the construction of new rail lines, the introduction of shared bike services as a feeder to subway stations, the electrification of passenger vehicles and the adoption of automated and shared vehicles. The well-established, system dynamics-based MARS model is adapted to Beijing and further improved via the inclusion of these new services, technologies and policies. We find that decentralization can have a profound effect on overall sustainability if not considered in conjunction with other policies and that new rail lines and shared bikes may only have benefits in specific zones. Shared and automated vehicles could increase VKT by 60% and reduce active and public transport trips by a quarter. As such, nuanced integrated policy approaches will be required that are similar to those currently in place, such as imposed car shedding and taxi fleet control.
... Recent studies have assessed LDV electrication in eet-scale LCA methods for North America, 5,31,32 Europe, [33][34][35] and Asia. 36,37 Previous eet-level LCA and materials ow analysis (MFA) studies for the UK market 7,38 consider a limited set of vehicle technologies (ICEV and BEV, excluding PHEV which constitute a signicant share of the UK transport strategy 39 ), do not consider the increasing use of LFP batteries as intended by key manufacturers, 40 and have not accounted for the current UK ZEV mandate 41 and EU battery recycling regulation. 15 Thus, there is scope to update a UK-specic eet LCA investigation, including more representative vehicle and battery technology combinations, and in the context of current policy. ...
The UK zero-emissions vehicle (ZEV) mandate aims for battery electric vehicles (BEVs) to account for 100% of new sales by 2035. This study presents a fleet-scale life cycle assessment model of UK light duty vehicles through 2050, integrating a dynamic material flow analysis to evaluate the implications on critical battery materials. Rapid uptake of BEVs is projected to grow demand for primary materials within 15 years, particularly for lithium, nickel, and cobalt, exceeding current UK consumption by at least five-fold. In the longer-term, the successful creation of a closed-loop battery recycling ecosystem has the potential to mitigate further increases in demand for primary critical materials. With the adoption of efficient closed-loop, domestic recycling practice, the EU's regulations for battery recycled content requirements could be met for nickel and lithium, though cobalt remains a challenge as the recycled content targets could only be met two to three years later. The ZEV mandate is projected to be effective in reducing overall life cycle GHG emissions by 57% in 2050, relative to 2021. Even with an ambitious target like the UK's 2035 ZEV mandate, internal combustion engine vehicles will continue to operate on the road for years to come given that the fleet average is a 15 years vehicle lifetime. Thus, it is prudent to also consider low-carbon fuels as a complementary strategy to deliver the UK's net-zero target.
... For instance, Sun et al. (2018) utilized big data analysis to develop an inventory for exhaust emissions in the Qingdao port, enabling the prediction of annual emissions and a better understanding of associated maritime pollution. Similarly, Deng et al. (2020) and Ma et al. (2022) employed big data approaches to update vehicle emission inventories, specifically for light-duty passenger vehicles in China. Studies have also been conducted to develop high-resolution emission inventories by incorporating big data and updating estimation methods, emission factors, activity data, and allocation profiles (Huang et al., 2021;Lin et al., 2022). ...
The current atmospheric emission inventories do not fully meet the spatial and temporal resolution requirements
of air quality modelling applications. Considering Portugal as a case study and focusing on combustion point
emission sources (i.e., public power, refineries, manufacturers, and construction activities), this work proposes a
methodological approach and dataset to estimate anthropogenic emissions suitable for different spatial scales
(from regional to local). The obtained results were similar to the annual values reported by the Portuguese
Environment Agency with the maximum emissions being estimated for manufacturing and construction activities.
No significant differences were recorded between the temporal profiles developed in this and previous
studies. However, the country-specific proxies from the developed database allowed us to better represent the
temporal and spatial patterns of the Portuguese atmospheric emissions. The combination of the BigAir database
with a comprehensive and standardized approach could help policymakers define mitigation and/or plan
measures to reduce emissions from point sources, support countries worldwide (with a lack of data) to develop
high-resolution emission inventories, and improve the current global and European inventories.
... However, luxury brands were seldom observed in the dataset. There was also evidence showing that the average odometer readings collected by the XiaoXiongYouHao application were slightly higher than the average mileage of the private passenger car fleet in China [36,42]. It can be inferred that users of the application tended to be more sensitive to fuel costs. ...
A widening gap between official and real-world fuel consumption of passenger cars has been reported worldwide. However, previous policy evaluation has not adequately incorporated real-world performance. To comprehensively evaluate China’s orporate Average Fuel Consumption (CAFC) policy, we update the fuel
consumption gap with millions of consumer-reported records and calculate CAFC and evaluate the national average fuel consumption (NAFC) under four scenarios. The results show that China’s fuel consumption gap has reached 37%. The average CAFC decreases from 6.72 L/100 km in 2016 to 5.91 L/100 km in 2020, far slower than the rated performance. The real-world NAFC non-compliance is disaggregated into on-road discrepancy (2.5 L/100 km), new-energy discounts (0.4 L/100 km), lightweight impacts (0.3 L/100 km), and additional technology improvements (0.8 L/100 km). This study can improve the state-of-the-art understanding of the realworld fuel consumption of passenger cars in China, thus calling for a more real-world-featured regulation system.
... However, we use the results of similar works, albeit more recent and conducted in countries other than Colombia, to put our orders of magnitude into perspective. For example, Chinese drivers have recently been reported to travel an average of 28 km per day [33], French drivers 33 [34], and Americans 59 [35]. In comparison, our results indicate that Cali residents travel on average between 20 km (without selection of users with complete data) and 60 km per day (with selection). ...
Over the last two decades, mobile phone data have appeared to be a promising data source for mobility analysis. The structure, abundance, and accessibility of call detail records (CDRs) make them particularly suitable for such use. However, their exploitation is often limited to estimating origin–destination matrices of a restricted part of the population: regular travellers. Although these studies provide valuable information for policymakers, their scope remains limited to this subpopulation analysis. In the present work, we develop a collective mobility reconstruction method adapted to nonregular travellers. The method relies on the notion of the detour ratio, which makes it robust to the lack of mobile phone data as well as its application to large instances (large and dense telecommunication networks). It is used to conduct a longitudinal analysis of the macroscopic mobility patterns in Santiago de Cali, Colombia, thanks to call detail data shared by communication provider CLARO as part of a research project conducted by Citepa, Paris, the Green City Big Data Project.
Vehicle emissions in China have been decoupled from the rapid motorization owing to the comprehensive control strategies. China’s increasingly ambitious goals for better air quality are calling for deep emission mitigation, posing a need to develop an up-to-date emission inventory that can reflect the fast-developing policies on vehicle emission control. Herein, large-sample vehicle-emission measurements have been collected to update the vehicle emission inventory. For instance, ambient temperature correction modules were developed to depict the remarkable regional and seasonal emission variations, showing that the monthly emission disparities for total hydrocarbon content (THC) and nitrogen oxide (NOX) in January and July could be up to 1.7 times in northern China. Thus, the emissions ratios of THC and NOX can vary dramatically among various seasons and provinces, which have not been well considered by previous simulations regarding the nonlinear atmospheric chemistry of ozone (O3) and fine particulate matter (PM2.5) formation. The new emission results indicate that vehicular carbon monoxide (CO), THC, and PM2.5 emissions decreased by 69%, 51%, and 61%, respectively, during 2010-2019. However, the controls of NOX and ammonia (NH3) emissions were not as efficient as other pollutants. Under the most likely future scenario (PC [1]), CO, THC, NOX, PM2.5, and NH3 emissions were anticipated to reduce by 35%, 36%, 35%, 45%, and 4%, respectively, from 2019 to 2025. These reductions will be expedited with expected decreases of 56%, 58%, 74%, 53%, and 51% from 2025 to 2035, which are substantially promoted by the massive deployment of new energy vehicles and more stringent emission standards. The updated vehicle emission inventory can serve as an important tool to develop season- and location-specific mitigation strategies of vehicular emission precursors for alleviate haze and O3 problems.
