Annual Mileage Research Articles

Overestimation of on-road driving performance is associated with reduced driving safety in older drivers

ObjectivesOlder drivers often show less precise self-ratings with a tendency to overestimate themselves. It is unclear, however, how overestimators differ from underestimators or drivers with adequate self-ratings. Methods59 healthy older drivers participated in this on-road study. Besides standardized on-road driving assessment, the study protocol included the collection of neuropsychological and driving-related data as well as different self-ratings. Statistical analyses involved correlations between different subjective and objective ratings as well as statistical comparisons between drivers who overestimated and drivers who adequately rated their on-road driving performance (no drivers underestimated their performance). ResultsDespite positive correlations between different self- and expert ratings, our results revealed that 25 % of the participants overestimated their on-road driving skills. Among other things, overestimators showed poorer on-road driving performances, more prospective near and minor at-fault accidents, poorer cognitive performances in specific driving-related domains and reduced annual mileage. DiscussionOur results suggest that older drivers who overestimate their driving skills show poorer performances within a broad range of skills that directly reflect or are closely related to driving safety. Against the background that an adequate self-assessment is required by German traffic regulations, our findings suggest that overestimators represent a risk group among the population of older drivers.

Driving anger dimensions and their relationship with aberrant driver behavior in Lebanon: Results from a national self-reported survey.

Driving anger may vary across countries due to culture. This might affect driver behavior, which, in turn, impacts the driving outcomes. This study aims to investigate the relationship between socio-demographic variables, driving anger, and the self-reported aberrant behavior among Lebanese drivers and to determine which anger dimension is linked to driving behavior. A cross-sectional study was conducted among eligible Lebanese drivers from all Lebanese governorates. Data were collected using an anonymous Arabic self-reported questionnaire that included demographic information, driving-related variables, and two scales: the Driver Behavior Questionnaire (DBQ) and the Driver Anger Scale (DAS). Four hierarchical regressions were performed taking the DBQ subscales as the dependent variable and the DAS subscales as independent variables. Out of 1102 surveyed drivers, 68.4% were males, having a mean age of 34.6 ± 12.3 years and an average driving experience of 13.5 ± 10.8 years. DBQ, DAS, and their subscales showed good reliability. Older age and female gender were negatively associated with the tendency of committing aggressive violations. However, being a professional driver and increasing annual mileage were positively associated with a higher tendency to commit aggressive violations. In addition to these factors, a higher educational level was found associated with a lower risk of driver's involvement in traffic violations. However, increased driving experience was associated with a higher tendency to commit aggressive violations. Reported driving errors were also found positively associated with older age, increasing mileage, and being a professional driver. However, larger driving experience and higher education were found protectors from erroneous behavior. Hierarchical regression showed that anger prompted by hostile gesture, discourtesy, police presence, traffic obstruction, and slow driving were positively associated with aggressive violations. All the DAS subscales were found to be positively associated with ordinary violations. traffic obstruction was also found associated with a higher tendency of drivers to commit lapses, as well as anger, which originated from police presence and slow driving which were found also positively associated with errors. Driver anger dimensions were found positively associated with aberrant driver behavior. To overcome road anger, there is a need to train drivers on coping strategies to restrain aberrant driving behavior.

Driver License Renewal Requirements Predict Driving Mobility Among Older Drivers With Health-Related Travel Difficulties

Given the importance of driving mobility for older adults in the United States, it is important to examine how license renewal policies are related to driving mobility and whether the driving mobility of older adults with health limitations may be especially affected by such policies. The present study examined the association between license renewal requirements and driving mobility in a national sample of 89,757 older adults aged 62 and older in the United States ( M age = 71.57, standard deviation age = 7.48). Using generalized estimating equations, the associations between two driver licensing requirements (in-person renewal and length of renewal cycle) and two indicators of driving mobility (driving status and annual driving mileage) were examined. Results indicated that in-person license renewal was significantly associated with lower driving mileage but not driving status. The association between in-person license renewal and lower driving mileage was greater for people who reported a health-related travel difficulty: in states requiring in-person license renewal, drivers with a health-related travel difficulty had driven 590 fewer miles in the previous year compared with drivers without such difficulties. Among people who experienced in-person license renewal requirements, a longer time between renewals was not significantly associated with driving status or annual mileage. The findings of the current study, combined with previous work showing a lack of safety benefits of in-person license renewal, suggest that in-person license renewal requirements may unnecessarily restrict the driving mobility of some older adults.

Estimation of integrated photovoltaics potential for solar city bus in different climate conditions in Europe

In order to improve primary energy saving and reduce greenhouse emissions, vehicle integrated photovoltaics have an ongoing interest. Studies on the benefits from vehicle solar roof, which take into account all the losses and the monthly variation in different climate conditions, are required. Therefore, we developed a simulation tool of the mileage covered by VIPV. This tool takes into account various use profiles and different characteristics of the vehicles and of the PV system. Focusing on city bus, simulations show that many parameters influence the outputs of the model, mainly: the geographic location, the shading losses, the electric architecture and the battery saturation. With projections of the technology in 2030, VIPV cover up to 9739 km annual mileage. This represents up to 24 % of the total distance. For the best month, it can get up to 47 km/day. For average Europe case, with 30 % shading losses, the VIPV cover only 3711 km annual mileage. The upgrade of the technology from 2022 to 2030 improves the benefits of VIPV by approximately 34 %. Life cycle assessment of solar city bus shows neutral to high gains. The carbon footprint is up to 28 T CO2-equivalent avoided emissions on 20 years lifespan.

Optimization of a rural bus service integrated with e-commerce deliveries guided by a new sustainable policy in China

Electronic business has expanded, resulting in flourishing rural e-commerce globally, and particularly in China. This paper introduces a novel rural public transit system that integrates passenger and freight services—a rural electric bus service that provides services for passengers and e-commerce packages. This blended service reduces logistical costs and generates additional income for the transit operator. The objective is to minimise cost for the transit user and operator by using a mixed-integer linear programming model that solves bus routing and bus trip scheduling for multiple rural towns; in addition, the model is tested by the Gurobi solver at small-scale instances. The developed solution is a hybrid heuristic algorithm based on adaptive large neighborhood search and tabu search approaches. Finally, a case study in China is conducted to examine the proposed cost-effective methodology and ensure that the integrated scheme will significantly save operational system costs without causing any passenger inconvenience. The research also shows how to optimally design the size of rural electric buses to reduce purchasing and maintenance costs. The optimal results of the case study, comprising 5 towns and 78 villages, show an annual cost saving of approximately ¥847,000 (approximately $121,000) and an annual mileage saving of approximately 66,000 km for the proposed scheme. In addition, the case study explicates key parameters selected for transfer package requirements and the effective deployment of the proposed scheme.

Correlation between visuo-cognitive tests and simulator performance of commercial drivers in the United States

BackgroundDriving commercial vehicles requires intact visuo-cognitive skills. Approximately 13% of all fatal motor vehicle crashes in the United States involve commercial drivers. The ability to accurately predict risk factors for unsafe commercial driving is essential for public safety. Accurate prediction tools will advance the field of commercial driver science, provide policy guidance for driver testing and assist healthcare providers during testing. Prior studies have correlated clinical tools to roadway safety; translating these results to commercial drivers has not yet been done. ObjectiveThis study aimed to identify specific demographic, driving history and visuo-cognitive test results that correlate with driving simulator performance. Using the Cumulative Simulator Score (CSS) as a surrogate for driving ability, the objective was to correlate both sets of data (self-reported and visuo-cognitive testing) with the CSS to identify screening tools for unsafe driving in commercial drivers.Principal Results.Baseline assessments of 120 variables were collected from October 2020 to January 2022. Of the 31 participants, 3 were female and 28 were male with a mean age of 53 years. Average BMI was 32, blood pressure 136/84, 32 years of CDL driving experience, 36,500 annual CDL mileage, 11,000 annual personal mileage, 14 years of education, average number of medications: 2, average number of medical conditions: 2, six participants with personal and/or commercial crashes or tickets in past five years, MOCA 27/30, Trails B time 66 s, UFOV Speed of Processing 15 ms, Stroke Disease Severity Assessment pass rate 94 %.The Cumulative Simulator Score (CSS), correlated significantly with education (r = 0.42; p = 0.02), commercial driving experience (r = 0.42; 0 = 0.02), and number of tickets as a commercial driver (Spearman rho = 0.40; p = 0.02). In a stepwise multivariable linear regression analysis, the number of tickets as a CDL driver in the past five years and years of education were retained as significant variables in the multivariable linear regression model, explaining 38 % of the variance of total scores on the CSS.Major Conclusions.Descriptive and self-reported driving characteristics correlate better with the Cumulative Simulator Score in CDL drivers than visuo-cognitive tests. Since simulator performance has been shown to be a reliable surrogate for driving performance, the number of tickets as a CDL driver in the past five years and years of education can be considered as additions to annual physicals for policy makers and health care providers to help assess their on-the-road safety.

A Sustainable Road Transport Decarbonisation: The Scenario Analysis of New Energy Vehicle in China

Due to the prosperous development of the economy, the emissions of carbon dioxide (CO2) and other greenhouse gases (GHGs) have intensified and attracted attention worldwide. China has set the "dual-carbon" aim to pursue sustainable development in the transport sector. Thus, this study created a generalised Bass model to forecast new energy vehicle (NEV) ownership by introducing a new factor, charging piles, to reflect the infrastructure effects. Using the improved model with the hypothesis of annual mileage, an empirical analysis was conducted with the subject of NEVs in China by using the NEV-related panel data from 2010 to 2020, and the forecast result is outstanding with a goodness-of-fit of 99.7%. With the forecasts, carbon emission reduction was calculated with a bottom-up method. To further discuss the pathway to achieve carbon neutrality in the transport sector of China, a scenario analysis was conducted with ideal, enhanced, and radical constraints. The results show that if all factors remain "as is" until 2050, China will be far from carbon neutrality. Thus, this paper proposes relevant policy implications to assist the government to obtain effective methods to assess carbon reduction benefits and find viable pathways to a sustainable road transport system.

Sensitivity Analysis of Battery Aging for Model-Based PHEV Use Scenarios

Battery lifetime is an important parameter in the life cycle assessment (LCA) of a plug-in hybrid-electric vehicle (PHEV). This paper aims to study the impact of various parameters on the battery aging of a PHEV. For this purpose, model-based use cases are generated, the outputs of which are the daily driven distances for a period of one year, recharge scenarios, and battery temperature. A combined aging model (calendar and cycling aging) is used to calculate the capacity lost by the battery at the end of one year of use. The thermal model of the battery is using an electro-thermal coupling equation, for which the ambient temperature is modeled using daily minimum and maximum temperature data varying throughout the year for different cities. Finally, a sensitivity analysis is carried out using the conditioned variance method to identify the most important input parameters which largely affect the output of this study. The results of this study show that battery size, annual mileage, external temperature, and charging behavior are the most important parameters to be considered in the aging study of the battery of a PHEV personal car.

Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective

Pricing using a Generalised Linear Model is the gold standard in the auto insurance industry and rate regulation. Generalised Additive Model applications in insurance pricing are receiving increasing attention from academic researchers and actuarial pricing professionals. The actuarial practice has constantly shown evidence of significantly different premium rates among the different rating territories. In this work, we build predictive models for claim frequency and severity using the synthetic Usage Based Insurance (UBI) dataset variables. First, we conduct territorial clustering based on each location’s claim counts and amounts by grouping those locations into a smaller set, defined as a cluster for rating purposes. After clustering, we incorporate these clusters into our predictive model to determine the risk relativity for each factor level. Through predictive modelling, we have successfully identified key factors that may be helpful for the rate regulation of UBI. Our work aims to fill the gap between individual-level pricing and rate regulation using the UBI database and provides insights on consistency in using traditional rating variables for UBI pricing. Our main contribution is to outline how GAM can address a more complicated functionality of risk factors and the interactions among them. We also contribute to demonstrating the territory clustering problem in UBI to construct the rating territories for pricing and rate regulation. We find that relativity for high annual mileage driven is almost three times that associated with low annual mileage level, which implies its importance in premium calculation. Overall, we provide insights into how UBI can be regulated through traditional pricing factors, additional factors from UBI datasets and rating territories derived from basic rating units and the driver’s location.

Резервы уменьшения затрат на эксплуатацию автомобиля

Relevant are studies to identify reserves to reduce the cost of car ownership, one of which is to minimize the cost of operation, and, in particular, maintenance. The aim of the study is to reduce the cost of car ownership by reducing the unit cost of maintenance based on a scientifically based increase in the frequency and choice of consumables. A particular task within the framework of this article is to establish the patterns of formation of unit costs for car maintenance and to analyze the impact on them of the frequency and cost of engine oil. A detailed analysis of the cost structure for the maintenance of a truck was carried out using the example of the KAMAZ 5490 NEO brand with two frequency values – regulated 60 thousand km and increased 80 thousand km. It has been established that such an increase in frequency reduces unit costs by 21,6% (by 331 rubles/1000 km). The results of studies confirming the proportional dependence of the cost of engine oil on its quality, which determines the replacement period, are presented. By modeling, the dependences of the unit costs for the maintenance of a truck on the example of the KAMAZ 5490 NEO brand on the frequency and cost of one liter of oil were obtained. It has been established that the strategy with the use of flexible oil cost (depending on frequency) compared to a fixed cost (independent of frequency) gives a noticeable effect (up to 190 rubles / 1000 km or 10,2%) in the area of lower values of frequency, where the indicator «price – quality» of oil is used. It is concluded that the reserve for reducing operating costs, and, consequently, the cost of owning a car, is an increase in the frequency of maintenance, bringing it closer to the average annual mileage, and a rational choice of motor oils, taking into account the replacement period and cost. Of scientific and practical interest are further studies of the regularities of the influence of various factors on the value of the frequency of maintenance, the definition of restrictions on its increase, the effect of an increase in the frequency on the cost of owning a car and the efficiency of its commercial operation.

The Characteristics of Light-Duty Passenger Vehicle Mileage and Impact Analysis in China from a Big Data Perspective

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 ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future.

Real-world charging behavior and preferences of electric vehicles users in Germany

As energy consumption and greenhouse gas emissions in the transport sector have increased continuously in recent years, electric vehicles have become a potential solution to achieve environmental goals in this sector. The uptake of electric vehicle is strongly dependent on sufficient charging infrastructure. While much research has been conducted on electric vehicle charging simulations and projections, there is a research gap on real-world charging by actual users. Therefore, we conducted a survey on charging behavior and preferences in Germany in 2020 with around 4,000 electric vehicle users. The survey included a stated choice experiment and a willingness-to-pay-analysis. We found that participants’ charging behavior is dominated by home charging and that public infrastructure is perceived to be insufficient. Next to charging prices, measures of comfort through the occupancy rate of charging infrastructure and additional waiting time are driving factors when making the decision to charge. Participants specified an acceptable time for additional walking distances when parking at charging facilities between 5 and 10 minutes. High abstention rates in answering some questions indicate the need for sufficient education strategies considering existing and new charging technologies. Drivers with high annual mileage and battery electric vehicle users are willing to pay more for additional charging power. Users are open to smart charging, as long as flexibility for spontaneous trips is maintained. This research offers timely insights into real-world charging behavior and preferences, while providing recommendations for the future roll out of charging infrastructure in Germany.

The Efficiency of Sampling Techniques for NTD Reporting

This paper examines the minimum sample size required by each of six sampling techniques for estimating annual passenger miles traveled to meet the Federal Transit Administration’s 95% confidence and 10% precision levels for the National Transit Database. It first describes these sampling techniques in non-technical terms and hypothesizes how they are expected to compare in their minimum sample sizes. It then determines the minimum sample size for 83 actual sample datasets that cover 6 modes and 65 transit agencies. Finally, it summarizes the results in minimum sample size to compare the relative efficiency of these sampling techniques. The potential for improved efficiency from using these sampling techniques is great, but the exact degree of improvement depends highly on individual agencies, modes, and services.

Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining

This study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles of naturalistic driving data resulting from the Strategic Highway Research Program 2 (SHRP2). Association rule mining (ARM) is applied to extract the rules for frequently occurring events. The generated association rules are filtered by four metrics (support, confidence, lift, and conviction) and validated by the lift increase criterion. A three-step analysis is performed to obtain a comprehensive understanding of the rules of C/NC events. The 20 most frequent items are first selected to investigate their relationship with the C/NC events. Subsequently, the association rules are used to identify the factors contributing to C/NC events. Finally, correlations between contributing factors and different severities of crashes (I—most severe, II—police-reportable, III—minor crash, and IV—low-risk tire strike) are analyzed by ARM. The results demonstrate that C/NC events occur most frequently on straight and level road segments with no controlled intersections or traffic control devices when drivers are performing secondary tasks. Thus, the reasons for these crashes are carelessness and overconfidence. In addition, a median strip or barrier and a wider road can significantly reduce the frequency and severity of crash events. Moreover, gender, age, average annual mileage, and secondary tasks are highly correlated with the frequency and severity of C/NC events. Drivers with visual-spatial disabilities or crash records are more likely to be involved in the most severe crash events. Near-crash events occur more frequently at higher traffic density and on roads with traffic control devices and controlled intersections. These conditions may keep drivers alert, preventing crashes.

Tunnel measurements reveal significant reduction in traffic-related light-absorbing aerosol emissions in China

Light-absorbing aerosols (LAA), including black carbon (BC) and brown carbon (BrC), profoundly impact regional and global climate. Vehicle emission is an important source of LAA in urban areas, but real-world emission features of LAA from the urban vehicle fleet are not fully understood. This study evaluates traffic-related BC and BrC emission factors (EFs) and their vehicular emission inventories via road tunnel measurements in Tianjin, China, in 2017 and 2021. The distance-based and fuel-based EFs of BC for the mixed fleet were 1.05 ± 1.28 mg km−1 veh−1 and 0.057 ± 0.057 g (kg-fuel)−1, respectively, in 2021, with a dramatic decrease of 80.6 % compared to those in 2017. The BC EFs for gasoline vehicles (GVs, including traditional gasoline and hybrid vehicles) and diesel vehicles (DVs) were 0.55 ± 0.065 mg km−1 veh−1 and 10.5 ± 2.52 mg km−1 veh−1, respectively, in 2021. Compared to 2017, the BrC EFs also decreased significantly in 2021, by 10.8–53.6 %, with 0.32 ± 0.45 mg km−1 veh−1 and 0.018 ± 0.020 g (kg-fuel)−1 of distance-based and fuel-based EFs for mixed fleet. The BrC EFs for GVs and DVs were 0.091 ± 0.024 mg km−1 veh−1 and 3.06 ± 0.91 mg km−1 veh−1, respectively, in 2021. Based on the BC and BrC EFs for GVs and DVs and annual mileage for each vehicle category, the annual vehicular LAA emission inventories were estimated. From 2017 to 2021, the annual vehicular LAA emissions in Tianjin have been significantly reduced, by 69 % for BC and 10 % for BrC. DVs account for a small amount of the vehicle population (8.4 %), but contribute to most of the BC (83 %) and BrC (86 %). Our study demonstrates the significant reduction of vehicular light-absorbing aerosols emission due to vehicle pollution prevention and control in China.

Comparison of impact accelerations between injury-resistant and recently injured recreational runners.

Previous injury has consistently been shown to be one of the greatest risk factors for running-related injuries (RRIs). Runners returning to participation following injury may still demonstrate injury-related mechanics (e.g. repetitive high impact loading), potentially exposing them to further injuries. The aim of this study was to determine if the magnitude (Peakaccel) and rate of loading (Rateaccel) at the tibia and sacrum differ between runners who have never been injured, those who have acquired injury resistance (runners who have not been injured in the past 2 years) and those who have been recently injured (RRI sustained 3-12 months ago). Runners completed an online survey capturing details of their RRI history over the previous 2 years. Never injured runners were matched by sex, quarterly annual mileage and typical training speed to runners who had acquired injury resistance and to runners who had been recently injured. Differences in Peakaccel and Rateaccel of the tibia and sacrum were assessed between the three groups during a treadmill run at a set speed, with consideration for sex. A total of 147 runners made up the three injury status groups (n: 49 per group). There was a significant main effect of injury status for Peakaccel and Rateaccel at the sacrum, with recently injured runners demonstrating significantly greater Rateaccel than never injured and acquired injury resistant runners. There was also a significant main effect for sex, with females demonstrating greater tibial Peakaccel, sacrum Peakaccel and Rateaccel than males. Rateaccel at the sacrum distinguishes recently injured runners from never injured runners and runners who may have acquired injury resistance, potentially highlighting poor impact acceleration attenuation in recently injured runners.

Distributional effects of carbon pricing by transport fuel taxation

We introduce a new microsimulation model built on household transport data to study the distributional effects of carbon-based fuel taxation of private road transport in Germany. Our data includes car-specific annual mileage, fuel efficiency, and the distinction between fuel types, allowing for a very detailed analysis. The model enables focusing on different household types as well as identifying effect heterogeneity across the income distribution. We compare the recent fuel tax scheme with three policy reform scenarios to empirically test several hypotheses regarding distributional effects of carbon pricing. We find that the legal status quo of the fuel tax has overall regressive effects, with the tax on petrol being regressive and the tax on diesel being progressive. A transformation of the current tax into a revenue-neutral carbon-harmonised fuel tax yields a progressive distributional effect, while an introduction of a new carbon tax on transport fuels is neither clearly regressive nor progressive. Combining both tax schemes also has non-regressive effects. On aggregate, the distributional effects are modest. Subgroup analyses, however, reveal that the individual burden can be substantial. In sum, our results suggest that policy makers face various options for pricing road transport greenhouse gas emissions without causing an overall disproportionate tax burden on low-income households.

Modeling Probabilistic Driving and Charging Profiles of Commercial Electric Vehicles

The electrification of road transport requires a massive integration of charging infrastructure into the electrical distribution grids. In order to investigate their impact on the grid expansion demand or to develop optimization methods for charging management, it is necessary to model the uncertainties of the driving and charging behavior of electric vehicles in the form of probabilistic time series but also as a simultaneity factor. Most scientific papers focus on modeling privately owned electric vehicles. However, the influence of commercial electric vehicles has hardly been considered so far, even though high electric loads are to be expected here as well in the future. In this paper we present our method for modeling the driving and charging behavior of commercially owned electric passenger cars and light commercial vehicles. In our evaluation, we show the plausibility for a fleet of 5000 vehicles in terms of average annual mileage, weekday-dependent trips, and seasonal energy demand, among others. Finally, we determine the maximum simultaneity of the charging power.

Exhaust and non-exhaust airborne particles from diesel and electric buses in Xi'an: A comparative analysis

Switching diesel buses (DBs) to electric buses (EBs) has been a global trend to reduce the use of fossil fuels and improve air quality. However, buses electrification may lead to additional vehicle weight, which may emit more non-exhaust particulate matter (PM) emissions. It remains debatable whether buses' electrification will successfully improve air quality as excepted. To assess the effect of the buses' electrification on the levels of PM emissions, PM emission factors (EFs) were evaluated from EBs and equivalent DBs. In addition, the total mass of PM emissions from EBs and equivalent DBs in 2021 was calculated in Xi'an using the real-world number and mileage of EBs. The non-exhaust PM EFs from EBs were larger than total exhaust and non-exhaust PM EFs from DBs, indicating that the electrification of buses would cause an increase in the level of PM emissions. The total annual mass of PM emissions from EBs was apparently higher than that from DBs. Moreover, a sensitivity analysis showed that tire wear, brake wear, and road wear PM emissions were more reliant on vehicle mileage, whereas resuspension of road dust was more dependent on vehicle weight. This finding can serve as a guideline for policymakers to design mitigation strategies for reducing extra PM emissions due to the electrification of buses by reasonably reducing vehicle weight and annual mileage.

Numerical Study on Aerodynamic Characteristics of Heavy-Duty Vehicles Platooning for Energy Savings and CO2 Reduction

This study aims to analyze the aerodynamic interaction between moving vehicles platooning with the change in the platooning conditions on a freeway. The effect of the vortex generated by the forward vehicle reduces the value of the stagnation pressure generated at the front of the rear vehicle, which effectively reduces drag on the driving vehicle. To elucidate this, a total of four vehicles were applied to platooning at a speed of 100 km/h by altering the gap distance of heavy-duty vehicles (HDVs) such as 0.5 Length (L), 1 L, 1.5 L, 2.0 L, and 2.5 L under the conditions of 1 L equal to 13.16 m. The stagnation pressure at the front of the following vehicle (FV) was reduced, and quantitative analysis of drag force generated at each leading vehicle (LV) and following vehicle that is platooning exhibited a reduction of about 51%, 56%, and 52%, respectively, when compared to the single moving HDV. This is considered as a reduction in engine power for the driving vehicle. Taken together, these results are effective in improving fuel efficiency and reducing CO2, a representative greenhouse gas, and predicting fuel and CO2 reduction based on HDV annual mileage according to the highway conditions and logistics.