Car Prices Research Articles

The Framework of Car Price Prediction and Damage Detection Technique

In this paper, the research area has always been car price forecasting. We demonstrate that using the proposed object detection method, the type of damage can be categorized into two classes with good accuracy damaged and undamaged. So, when we discovered these issues, we decided to develop a mobile application called Car Price Prediction, which allows users to anticipate the price of a used car. So, we trained the damage identification model using our data using a state-of-the-art image detection method convolutional neural network and evaluated the accuracy on a GPU server and a smartphone.

Огляд європейських тенденцій розвитку електротранспорту та його взаємодії з енергетичним сектором

An overview of the trends in the development of electric transport in European countries is presented, including the state and tax policy of supporting electric transport owners, the dynamics of the growth of the European fleet of electric vehicles, the development of charging infrastructure and the main principles of the interaction between electric vehicles and power system. The experience of countries that have successfully developed electric transport was reviewed, and it was found that the demand for electric cars mostly depends on the price of electric cars for the end user, infrastructure development and government incentive programs. The review also showed that, in practice, the wide spread of electric vehicles should occur simultaneously with the development of the corresponding infrastructure, as well as the development of systems for their interaction with the energy system and compliance with global environmental standards. The growing number of electric vehicles certainly requires special attention from network and power system operators, because the appearance of charging stations of various manufactures and capacities, installed in residential areas, private and apartment buildings, can lead to the number of problems, the emergence of which is associated with electromagnetic compatibility, overloading of electric cables and distribution transformers, safety of operation, that as a result can negatively affect the reliability and quality of power supply. There is also the potential risk that with a certain number of EVs, there will not be enough existing generation capacity and capacity of the electricity grid to charge EV batteries. The analysis of the principles of interaction between electric vehicles and power system in Europe showed that they are based on the concept of Smart Grid - smart or intelligent power supply networks, by means of which the electric vehicle charging system can be managed by the energy supplying company in order to solve a number of energy system tasks: increasing the efficiency of network infrastructure using, peak load shift and the development of smart grid infrastructure. Keywords: electric vehicle, electric charging station, charging infrastructure, power system, environmental requirements, the Smart Grid concept

Energy Losses Related to Ring Pack Wear in Gasoline Car Engine

Decreasing production and rising prices of cars, especially those with electric drive, lead to longer use of cars with internal combustion engines. It can be assumed that in the future, more and more cars powered by such engines with high mileage and therefore high wear will be used. Engine wear leads to reduced efficiency and increased emissions. This paper analyzes the impact of wear of the piston–rings–cylinder system components on energy losses associated with gas leakage from the combustion chamber and friction of the rings against the cylinder liner in a car spark-ignition engine. A ring pack model was used for the analyses. The input data for the simulation were gained in measurements made on the engine test stand and measurements of the wear of the engine components used in the car. The energy losses associated with blow-by in an unworn engine ranged from 1.5% of the indicated work at high load to almost 5% at low load. In the engine after 300,000 km, these losses increased to 2.5% and 7.5%, respectively. Ring friction losses in an unworn engine ranged from 1.5% at high load to 9% at low load. The effect of wear on these losses was smaller. They increased by only 0.1% at high load and 1% at low load.

Towards Systematic Design Considerations for Visualizing Cross-View Data Relationships.

Due to the scale of data and the complexity of analysis tasks, insight discovery often requires coordinating multiple visualizations (views), with each view displaying different parts of data or the same data from different perspectives. For example, to analyze car sales records, a marketing analyst uses a line chart to visualize the trend of car sales, a scatterplot to inspect the price and horsepower of different cars, and a matrix to compare the transaction amounts in types of deals. To explore related information across multiple views, current visual analysis tools heavily rely on brushing and linking techniques, which may require a significant amount of user effort (e.g., many trial-and-error attempts). There may be other efficient and effective ways of displaying cross-view data relationships to support data analysis with multiple views, but currently there are no guidelines to address this design challenge. In this article, we present systematic design considerations for visualizing cross-view data relationships, which leverages descriptive aspects of relationships and usable visual context of multi-view visualizations. We discuss pros and cons of different designs for showing cross-view data relationships, and provide a set of recommendations for helping practitioners make design decisions.

Pengembangan Model Machine Learning Regresi sebagai Web Service untuk Prediksi Harga Pembelian Mobil dengan Metode CRISP-DM

Along with the increasing public demand for car transportation modes, car sales businesses are also increasing. Efforts to exist and be competitive are carried out such as by applying machine learning models to determine the car’s selling price based on its specification. Businesses can also stimulate sales by actively offering customers. The effectiveness of the active and massive offerings can be increased by personalizing the offers provided. This research uses a machine learning-based approach to learn customer profile data to predict the car’s price they would buy. The research was conducted by adopting the CRISP-DM framework and developed using the Google Colaboratory and Azure Machine Learning platforms. The modeling stage developed six regression models, those are linear regression, Lasso, Ridge, Random Forest Regressor, Elastic-net, and Support Vector Regressor (SVR). After the evaluation stage, the Lasso regression model with the performance of R-squared (R2) of 0,99958 and Mean Absolute Error (MAE) of 2.284.865,29 deployed as a web service endpoint so it could be accessed in real-time. The web service required the customer’s “Gender, Age, Annual Salary, Credit Card Debt, and Net Worth” to return a response of the recommended car price range prediction for the customer to buy. In further development, predictions obtained through web services can be implemented in public applications to display personalized car sales offers or pages based on customer profiles

Used Car Price Prediction Based on the Iterative Framework of XGBoost+LightGBM

To better address the problem of the low prediction accuracy of used car prices under a large number of features and big data and improve the accuracy of existing deep learning models, an iterative framework combining XGBoost and LightGBM is proposed in this paper. First, the relevant data processing is carried out for the initial recognition features. Then, by training the deep residual network, the predicted results are fused with the original features as new features. Finally, the new feature group is input into the iteration framework for training, the iteration is stopped, and the results are output when the performance reaches the highest value. These experimental results show that the combination of the deep residual network and iterative framework has a better prediction accuracy than the random forest and deep residual network. At the same time, by combining the existing mainstream methods with the iterative framework, it is verified that the iterative framework proposed in this paper can be applied to other models and greatly improve the prediction performance of other models.

Predicting the Price of Pre-Owned Cars Using Machine Learning and Data Science

Abstract: Storm Motors Is An E-Commerce Company Who Act As Mediators Between Parties Interested In Selling And Buying Pre-Owned Cars. They Have Recorded Data About The Seller And Car Details, Registration Details, Web Advertisement Details, Make And Model Information And Price. The Company Wishes To Develop An Algorithm To Predict The Price Of Pre-Owned Cars Based On Various Attributes Associated With The Car To Make A Sale Quickly, If The Price Is Reasonable And Satisfies Both The Seller And Buyer, By Comparing The Price Of Various Car Models Based On Car Features To Improve Their Business. In This Paper, We Have Conducted A Comparative Study Using Machine Learning Algorithms Like Linear Regression And Random Forest Algorithms Which Is Implemented With Jupyter Note Book. The Study Shows That Linear Regression Algorithm Performance Is More Than Random Forest Algorithm. We Have Also Experimented With Auto Ai Experimentation In Ibm Cloud Watson Studio, Which Automatically Builds The Best Predictive Model By Comparing With Other Algorithm, With Accurate Measures. In This Auto Ai Experiment We Have Found That Linear Regression Is Performing Better Than Ridge Algorithm And Random Forest. The Main Objective Of This Paper Is To Find The Best Predictive Model For Predicting Pre-Owned Car Price.

Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network

As the mobile Internet improves by leaps and bounds, the model of traditional offline used car trading has gradually lost the ability to live up to the needs of consumers, and online used car trading platforms have emerged as the times require. Second-hand car price assessment is the premise of second-hand car trading, and a reasonable price can reflect the objective, fair, and true nature of the second-hand car market. In order to standardize the evaluation standards of used car prices and improve the accuracy of used car price forecasts, the linear correlation between vehicle parameters, vehicle conditions, and transaction factors and used car price was comprehensively investigated, grey relational analysis was applied to filter the feature variables of factors affecting used car price, the traditional BP neural network was also optimized by combining the particle swarm optimization algorithm, and a used car price prediction method based on PSO-GRA-BPNN was proposed. The results show that only the correlation coefficient of new car price, engine power, and used car price is greater than 0.6, which has a certain linear correlation. The correlation between new car price, displacement, mileage, gearbox type, fuel consumption, and registration time on used car prices is greater than 0.7, and the impact of other indicators on used car prices is negligible. Compared with the traditional BPNN model and the multiple linear regression, random forest, and support vector machine regression models proposed by other researchers, the MAPE of the PSO-GRA-BPNN model proposed in this paper is 3.936%, which is 30.041% smaller than the error of the other three models. The MAE of the PSO-GRA-BPNN model is 0.475, which is a maximum reduction of 0.622 compared to the other three models. R can reach up to 0.998, and R2 can reach 0.984. Although the longest training time is 94.153 s, the overall prediction effect is significantly better than other used car price prediction models, providing a new idea and method for used car evaluation.

Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets

China and India, the world's two most populous developing economies, are also among the world's largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers affecting fuel prices, car prices and fuel economy. This study estimates the responsiveness of new car buyers in China and India to such policy levers and drivers including income. Furthermore, we estimate the potential for rebound effect and the effectiveness of a feebate policy. To accomplish this, we developed a joint discrete-continuous model of car choice and usage based on revealed preference survey data from approximately 8000 new car buyers from India and China who purchased cars in 2016–17. Conditional on buying a new car, the fuel consumption in both markets is found to be relatively unresponsive to fuel price and income, with magnitudes of elasticity estimates ranging from 0.12 to 0.15. For both markets, the mean segment-level direct elasticities of fuel consumption relative to car price and fuel economy range from 0.57 to 0.65. The rebound effect on fuel savings due to cost-free fuel economy improvement is found to be 17.1% for India and 18.8% for China. A revenue-neutral feebate policy, with average rebates and fees of up to around 15% of the retail price, resulted in fuel savings of around 0.7% for both markets. While the feebate policy's rebound effect is low – 7.3% for India and 1.6% for China– it does not appear to be an effective fuel conservation policy.

Housing Price Estimation with Deep Learning: A Case Study of Sakarya Turkey

Shelter is one of the most basic human needs. Besides housing needs, the housing market is also very important for investment. It is also a market where many people, such as engineers, architects, real estate agents make economic gain. When a house is bought for living in it, it is not desired to be changed for many years, and when it is bought for investment, it is a tool that requires good income. Therefore, the best decision should be made when buying a house, and it should be scrutinized. Correct estimation of house prices is very important for both buyers to make the right decision and for sellers to sell without a loss. There are many parameters for estimating house prices. In addition to variables such as the number of floors, location, and several bathrooms used in previous studies, economic factors (such as the price of bread, foreign currency price, new car price) and the housing loan interest rate of the banks were taken as inputs in this study. Sakarya province, where all parameters can be tested to make a more accurate determination, was chosen as the research area. A comparison of polynomial regression, random forest, and deep learning methods was made and it was concluded that the most accurate method was deep learning. At the same time, it was determined which parameters are more effective in house price estimation.

Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios

Traffic density is growing day by day due to the increasing population and affordable prices of cars. It created a void for traffic management systems to cope with traffic congestion and prioritize ambulances. The consequences can be a terrible situation. Emergency vehicles are the most affected in these situations, and inadequate traffic control can put many lives at stake. Ambulances on the road are detected using an acoustic-based Artificial Intelligence system in this article. Emergency vehicle siren and road noise datasets have been developed for ambulance acoustic monitoring. The dataset is developed along with a deep learning (MLP-based) model and trained to use audio monitoring to predict the ambulance presence on the roads. This model achieved 90% accuracy when trained and validated against a developed dataset of only 300 files. With this validated algorithm, researchers can develop a real-time hardware-based model to detect emergency vehicles and make them arrive at the hospital as soon as possible.

Policy measures and their impact on transport performance, modal split and greenhouse gas emissions in German long-distance passenger transport

In our study, we used a system dynamics model to analyze how frequently discussed transport policy measures affect the total transport performance of long-distance passenger transport in Germany and the transport performance of relevant modes (modal split shares). We then combined the results with established data on mode-specific greenhouse gas emissions to ultimately determine the climate protection effects of the measures.Our analysis shows that an actual modal shift from road to rail will only occur if the price competitiveness and supply conditions of long-distance rail passenger transport are significantly improved. Scenarios for increasing the price of passenger car traffic via a passenger car toll on trunk roads or an additional CO2-based tax on fuels increase the market share of rail, but limit overall transport performance growth compared to our reference scenario with no measures. A doubling of long-distance passenger rail transport cannot be achieved with any of the measures or bundles of measures examined, neither in terms of transport performance nor modal split share. According to our calculations, only the “car repression” scenario makes a noticeable contribution to reducing CO2 emissions. German government's ambitious climate targets for 2030 can only be reached if the climate footprint of vehicles – especially passenger cars – is significantly improved. It is also noteworthy that, according to our analysis, a politically frequently demanded ban on domestic flights would ultimately even lead to an increase in CO2 emissions, since, among other things, general travel costs would fall.

Price Prediction of Used Cars Using Machine Learning

Abstract: The goal of this study is to develop a model that can anticipate fair used car pricing based on a variety of factors such as vehicle model, year of manufacture, fuel type, Price, Kms Driven . In the used car market, this strategy can benefit vendors, purchasers, and car manufacturers. It can then produce a reasonably accurate price estimate based on the data that users provide. Machine learning and data science are used in the model-building process. The data was taken from classified ads for second hand autos. To attain the maximum accuracy, the researchers used a variety of regression approaches, including linear regression, polynomial regression, support vector regression, decision tree regression, and random forest regression. This project visualized the data to better comprehend the dataset before starting the model-building process. To assure the regression's performance, the dataset was partitioned and changed to fit the regression. R-square was used to evaluate the performance of each regression .The final model contains more elements of used autos than earlier research while also having a higher forecast accuracy. Keywords: Analysis, Machine Learning, Ridge Regression, Lasso Regression, Linear Regression.

Used Car Price Prediction using Different Machine Learning Algorithms

Abstract: A car price prediction has been a high-interest research area, as it needed recognizable effort and knowledge of the field expert. This paper mainly focuses on working of three different kind regression algorithms which are used to predict price of a used car. In this project, We have Considered number of distinct attributes which are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars we have used three different kinds of machine learning techniques which comes under supervised machine learning type of algorithm which are linear regression, lasso regression and ridge regression respectively. we have used Python libraries to design GUI for our project and some other machine learning related libraries like Numpy, Pandas, Sklearn etc. we have calculated and compared the accuracies of three machine learning algorithms. The accuracies for linear regression, lasso and Ridge regression were 83.65%, 87.09% and 84.00% respectively. The final main price is predicted according to lasso regression as it gives highest accuracy amongst three different algorithms. Keywords: car price prediction, machine learning, Regression techniques, linear regression, lasso regression, ridge regression

Behavior of consumer on origin of brand in automobile market

Country of origin and consumers are of increasing importance to marketing and consumer behaviorsearches to make the company more established in the mind of the consumer from that of the factthat whether selling of cars granted to the consumer as lower cost of labor, action of transportation,parent of the company have been the main reasons for such decision new car market of origin on consumer perception and its quality the Price related consequences of country of origin have beenin this Based on their brand origin the Pattern may suggest maybe different brand provide differentthings there for the questions of consumer and wide range of brand to buy from which company provides the best service at the same time in auto mobile market consist of several types like sedans, hatchbacks, needs as a result there is wide variety of us from widened of ranges in the market structure provides a new interesting insight into the role of brand region segments of served in different products in the auto mobile market in car market consumerchoice is verified in light in Europe 50 brands from 12 different countries difference in systematic fashion among the car models based on the various ranges from high to low. The natural question is whether region and prices for cars are different in specifications as a resultand additional but also into question is how the structure of respective prices where is across different car have the purpose of this paper is to address the fundamental question not only for thecar industry but also for the aim of the paper is twofold search of the automobile market in India first one is to find a country of origin of the new carsin market controlling the infrastructure of themarket. Second battle of the face value and the beauty you see of the cars.

Linear Regression Algorithm Based Price Prediction of Car and Accuracy Comparison with Support Vector Machine Algorithm

The aim of the study is to use Linear Regression (LR) algorithm based price prediction of car price and accuracy comparison with support vector machine (SVM) classification algorithm. Materials and methods: LR (N=205) and SVM algorithm (N=205) are applied for car price prediction as a mechanism. The accuracy and prediction of the classifiers was evaluated and recorded with G power 80% and alpha value 0.05. Results: The SVM produces 89% accuracy in predicting the car price on the sample dataset and the LR predicts the accuracy at the rate 91.7%. LR algorithm based accuracy appears (significant 0.563) to be better than SVM algorithm for car price prediction. Conclusion: The accuracy performance parameter of the LR algorithm appears to be better than the SVM algorithm.

حال المنازعات الناشئة عن التقصير في عقد الإيجار: دراسة في شركة BOWRIZ لإجارة السيارات بماجيلانج في منظور الشريعة الإسلامية

Along with the development of the times, several people or companies that are engaged in car rental have emerged because of the importance of transportation, especially cars and very expensive car prices. From this rental, a lease agreement or ijarah emerg es. For rental owners and tenants, they want to get mutually beneficial reciprocity, but in reality, on the ground, there is a situation where achievement is not achieved or is said to be "default". The default also occurred in Bowriz's car rental, which w as the object of the research. From this explanation, a problem arose, namely how the practice of resolving default disputes at Bowriz car rental and how to review Islamic law on the settlement. This study aims to determine the practice of settlement of de fault disputes in Bowriz car rental and a review of Islamic law on its settlement. Data collection methods are interviews, direct observation, and asking for some car rental documents. The problems that exist in the study are then analyzed using qualitativ e analysis methods with an inductive approach. The Islamic law used is the principle of Mu'amalah which has been recommended by the Quran and Sunnah. The results of the research conducted indicate that the settlement of the default dispute is resolved by u sing deliberation and mediation. Both methods are in accordance with Islamic law because they prioritize the principle of Mu'amalah. and asked for some car rental documents. The problems that exist in the study are then analyzed using qualitative analysis methods with an inductive approach. The Islamic law used is the principle of Mu'amalah which has been recommended by the Quran and Sunnah. The results of the research conducted indicate that the settlement of the default dispute is resolved by using delibe ration and mediation. Both methods are in accordance with Islamic law because they prioritize the principle of Mu'amalah. and asked for some car rental documents. The problems that exist in the study are then analyzed using qualitative analysis methods wit h an inductive approach. The Islamic law used is the principle of Mu'amalah which has been recommended by the Quran and Sunnah. The results of the research conducted indicate that the settlement of the default dispute is resolved by using deliberation and mediation. Both methods are in accordance with Islamic law because they prioritize the principle of Mu'amalah. The results of the research conducted indicate that the settlement of the default dispute is resolved by using deliberation and mediation. Both m ethods are in accordance with Islamic law because they prioritize the principle of Mu'amalah. The results of the research conducted indicate that the settlement of the default dispute is resolved by using deliberation and mediation. Both methods are in acc ordance with Islamic law because they prioritize the principle of Mu'amalah.

Assessing consumer valuations of future costs versus purchase prices in Japan's auto market

To tax vehicles such that market distortions are minimized and environmental goals are promoted, policymakers must determine whether consumers correctly assess automobile prices versus future costs of ownership. Drawing from data spanning 2006 to 2015 for Japan's new car market, we apply the aggregate random coefficients logit model to estimate consumer demand with rich substitution patterns. We find that Japanese consumers primarily focused on fuel costs and future registration taxes followed by vehicle purchase prices. This finding implies that promoting eco-friendly vehicles requires Japanese policymakers' attention to fuel and vehicle registration taxes. This finding also suggests that salient features of preferential tax treatment in the auto market distort consumer preferences toward future costs. Counterfactual experiments under a fixed amount of tax revenue reveal that raising fuel taxes improves average fuel economy more than registration taxes do at the expense of broader social welfare.

ПОСЛЕДСТВИЯ ПОВЫШЕНИЯ УТИЛИЗАЦИОННОГО СБОРА ДЛЯ ТРАНСПОРТНЫХ СРЕДСТВ В РОССИЙСКОЙ ФЕДЕРАЦИИ

Introduction: the issue of increasing car recycling fee accompanied by a concurrent introduction of a compensating subsidy to domestic car producers has been recently widely discussed in Russia. Such a measure might be viewed as a protectionist measure disguised as the need to comply with environmental standards. This raises the question of this policy expedience. Objectives: to analyze the consequences of increasing the car recycling fee in the Russian Federation. Methods: comparative analysis, generalization and extrapolation of results, econometric estimations. Results: data analysis from various sources (Association of European Businesses, Federal Customs Service, Federal State Statistics Service, Federal Tax Service etc.) shows that effective car recycling fee rate have been growing much faster than inflation or real income index. Tax burden of the recycling fee in car prices is sufficiently high (15–30 %). A 25 % increase in recycling fee rate leads to a 4 % car prices increase in the most likely scenario, which, in turn, will foster a 6 % decrease in car demand. Cumulatively, this may lead to a 17 % increase in recycling fee tax revenue, the largest part of which will have to be returned to domestic car producers. Conclusions: despite the presence of the compensating subsidy to domestic car producers, domestic cars prices will also rise by 4 % to maintain price parity with imported cars. The price increase will mostly affect the well-being of low-income households. In addition, taken the current rates structure, an increase in recycling fee will stimulate the use of illegal schemes of importing foreign cars to Russia by individuals. There are significant negative consequences of increasing the car recycling fee in the Russian Federation, and therefore it should be recognized as inappropriate.

Forecasting resale value of the car: Evaluating the proficiency under the impact of machine learning model

Car resale value prediction is the technique for estimating the prices of an old car. Second hand cars are in high demand but the process for evaluating their value is flawed which may lead them to be priced at unrealistic rates. Here, we offer our contribution for this issue. In this paper, an ensemble machine learning approach, XG Boost is proposed, which takes the important features of a car into account and assigns the cost based on it. Our proposed system uses multiple gradient boosted trees which minimizes the overfitting and eliminates irregularities from the predictions. Further, the accuracies for different machine learning algorithms such as Linear regression, Lasso Regression, Random Forest, KNN, and CART are calculated and compared with our model. From experimental results, major factors which contributed towards the car prices are found to be year of manufacture, distance driven, fuel type and transmission. The proposed approach contributes promising results in forecasting the resale value of the car, and achieves an accuracy of 93.73%.