• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.1
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• pp.153-161
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• 2004

A vehicle fuel economy is one of the most important issues in view of environmental regulation and customer's needs. In order to improve the vehicle fuel economy, great efforts has been carried out on the components bases. However, systematic analysis of vehicle fuel consumption is necessary for the further improvement of vehicle fuel economy. In this paper, a methodology for the breakdown of vehicle fuel consumption was studied and proposed for systematic analysis of the vehicle fuel economy. The energy equation for the vehicle power train was set up for the analysis of the vehicle fuel economy and simplified to be calculated or estimated using the measured data in a vehicle. The amount of fuel that was used in vehicle components under arbitrary driving conditions was quantified.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.247-256
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• 2012

Recently, estimation of greenhouse gas (GHG) emission has continuously emerged as an important global issue. This study compares various statistical methods for estimation of fuel consumption, which is necessary for calculation of GHG emission in road transportation sector. Existing methods have focused on using merely transportation fuel supply or distance traveled for calculation of fuel consumption. Estimates of GHG emission based on fuel supply, however, cannot reflect various vehicle types or model year. This study suggests and compares, from statistical point of view, several methods, which can be applied to estimate fuel consumption of each vehicle, by combining distance traveled and fuel efficiency (mileage), and total fuel consumption of all vehicles. It also suggests practical measures that can reflect vehicle types and model year to suggested methods for future research.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.65-71
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• 2011

It is practically hard to measure vehicle fuel consumption required to evaluate the energy-related governmental policies and traffic management strategies; the existing methods are too simplified due to the limited field data available. Existing methods are even unable to reflect the amount of fuel consumed when vehicles accelerate and decelerate, and such technical limitations have reduced the quality of the policy evaluation. This study proposes a new fuel consumption model that simultaneously considers the effects of both cruising speed and acceleration/deceleration of vehicles. A new fuel consumption model was developed based on the simulation data generated by AVL Cruise, a vehicle simulation program. The estimated by the proposed model was compared against the one from the existing method. Comparison results showed that the proposed model provided much reliable estimate (fuel consumption) than the other did.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.167-175
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• 2011

Evaluation of fuel consumption for the various road condition and vehicle type is necessary to perform the economic analysis of road construction which is important for the efficient design and management of road. Economic analysis of road should consider the social cost which can be divided into agency cost including initial construction expense, maintenance cost, and so on, and user cost consisting of vehicle operating cost, congestion cost, etc. Since vehicle operating cost depends on the traffic volume, fuel consumption that is a major part of vehicle operating cost will change by traffic volume as well. Fuel consumption is significantly affected by vehicle speed and road condition, especially the roughness. Thus, fuel consumption should be evaluated in terms of road condition, which is not currently considered. In this study, the estimation model of fuel consumption for the passenger cars in Korea has been developed by considering the road condition. First, the relationship between vehicle speed and fuel consumption that is used to calculate the vehicle operating cost for investment evaluation of transportation facility and the initial feasibility study of road construction was investigated. Second, with the consideration of road roughness, fuel consumption of the passenger car was measured. From the measurement, it was found that fuel consumption increased by $80m{\ell}$ per 100km driving as the roughness increased by 1m/km. Therefore, it is recommended that for the economic analysis of road design and management, the fuel consumption should be a function of road roughness.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.749-754
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• 2011

It is common that basic unit and model of fuel consumption have been used to evaluate effectiveness analysis of transportation infrastructure investment programs. However they could not reflect vehicle characteristics such as loading capacity and types of heavy vehicles. For these reasons, this study reviews convention fuel consumption model which is widely used and conducts a field experiment for 5 classes of heavy vehicles. To develop the fuel consumption quadratic model the field data are used and we develop each model by classes, and then compare with convention fuel consumption model. As a result, between convention and suggested model, there are considerable differences, which have a similar pattern between an 11-ton cargo of convention model and a 25-ton cargo type dump truck of the suggested model. Likewise we identify that there is an approximately 26% gap between convention model result and the result which is calculated a weighted average by registered number of heavy vehicles based on 5 types of fuel consumption model suggested in this study. This result implies that convention fuel assumption model has a realistic limitation.

• International Journal of Highway Engineering
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• v.19 no.4
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• pp.1-7
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• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.1-20
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• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.55-64
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• 2013

Conventionally, greenhouse gas (GHG) emissions in the transport sector have been estimated using the fuel consumption (i.e. Tier 1 method). However, the GHG emissions on road networks may not be practically estimated using the Tier 1 method because it is not practical to monitor fuel consumption on a road segment. Further, air pollutant emissions on a road may not be estimated efficiently by the Tier 1 method either due to the diverse characteristics of vehicles, such as travel speed, vehicle type, model year, fuel type, etc. Given these conditions, the goal of this study is to propose a Tier 3 level methodology to calculate $CO_2$ and $NO_X$ emissions on inter-regional roads using the information from ITS infrastructure. The methodology may avoid the under-estimation issue caused by the concavity of emission factor curves because the ITS speed or volume information is aggregated by a short time interval. The proposed methodology was applied to 4 road segments as a case study. The results show that the management of heavy vehicles' speed is important to control the $CO_2$ and $NO_X$ emissions on road networks.