scholarly journals Analysis of trend and development of the polish market used vehicles

2018 ◽  
Vol 19 (6) ◽  
pp. 640-643
Author(s):  
Paweł Olszowiec ◽  
Mirosław Luft ◽  
Zbigniew Łukasik
Keyword(s):  
Passenger Car ◽  
Passenger Vehicles ◽  
Changing Trends ◽  
Car Sales ◽  
Used Cars

The article presents the trend of changes in the Polish market of used cars over the years 2008 - 2018. Observations and the data presented in the material was obtained from associations and the office of experts verifying the condition of used vehicles before the purchase. The article covers the age, segment and price structures of passenger vehicles influencing the current market of used cars. The material also presents the main problems of passenger car sales in Poland and the changing trends of consumer needs.

Der Durchbruch des PKW zum Massenkonsumgut 1950-1964

2010 ◽  
Vol 51 (1) ◽  
Author(s):  
Christopher Kopper
Keyword(s):  
Additional Benefit ◽  
Price Regulation ◽  
Blue Collar ◽  
Car Ownership ◽  
Blue Collar Workers ◽  
Car Market ◽  
Car Sales ◽  
Used Car ◽  
Used Cars ◽  
Market Strategies

AbstractUntil now, research on the breakthrough of mass motorization has neglected the importance of the used car market. Empirical evidence proves that the used car market had a significant impact on the growth of car ownership and the purchase of cars among white and blue-collar workers. The transparency and flexibility of the used car market, the lack of price regulation and the degressive curve of used car prices facilitated car ownership among medium income Germans as early as the late 1950s. German car manufacturers recognized the potential of the used car market for the promotion of new car sales, but adopted different market strategies. US companies like Opel and Ford changed their models frequently to promote the sale of new cars and to accelerate the obsolescence of older models, whereas Volkswagen followed the strategy of incremental changes in order to create a higher value for used cars and to generate an additional benefit for new car customers.

Passenger Car Technology—Thirty Years of Advance

1986 ◽  
Vol 200 (3) ◽  
pp. 203-210
Author(s):  
R W Mellor
Keyword(s):  
Future Trends ◽  
Electronic Control ◽  
Data Systems ◽  
Personal View ◽  
Passenger Car ◽  
Passenger Vehicles ◽  
The Uk

Describes a personal view of the advances in passenger car technology in the UK through the last thirty years, and considers some present and future trends in the application of electronic control and data systems to passenger vehicles.

scholarly journals Decelerations of Passenger Vehicles on Gravel Arrester Beds

2020 ◽  
Vol 12 (5) ◽  
pp. 1761
Author(s):  
Miha Ambrož ◽  
Jovan Trajkovski ◽  
Robert Kunc
Keyword(s):  
Road Safety ◽  
Recent Past ◽  
Road Vehicles ◽  
Passenger Cars ◽  
Standard Size ◽  
Passenger Car ◽  
Safety Feature ◽  
The Road ◽  
Passenger Vehicles

Gravel-filled arrester beds, also called safety-escape ramps or vehicle run-out areas, have long been a road-safety feature for safely stopping heavy road vehicles from running off the road. In the recent past, there has been consideration given to installing these features on highway access ramps to provide safe areas to stop passenger cars driving at highway speeds. The work presented in this article was performed to investigate the behaviour of standard-size passenger cars on gravel-filled arrester beds, with a particular focus on the achievable vehicle decelerations and the ability of the arrester beds to safely stop a passenger car coasting in an uncontrolled manner. The findings show that the achievable average decelerations are in the range of 0.3 g for coasting vehicles and up to 0.9 g when the vehicles are braking. The results prove that more research is required to quantify the influence of the gravel parameters on the achievable decelerations.

USED CAR PRICE PREDICTION USING MACHINE LEARNING

2021 ◽  
Vol 1 (10) ◽  
pp. 246-251
Author(s):  
Himanshu Dahiya ◽  
Chetan Aggarwal ◽  
Shubh Goyal ◽  
Mini Agarwal
Keyword(s):  
Machine Learning ◽  
Price Prediction ◽  
Whole Process ◽  
Financial Conditions ◽  
Car Market ◽  
Car Sales ◽  
Used Car ◽  
Used Cars ◽  
Global Increase ◽  
The Many

Cars are an important asset and their importance has increased exponentially in our life. With the increase in the demand and growing needs, the production of cars has also increased. But due to inflation in the prices of new cars, there are people who still can only afford a used car due to their financial conditions. This whole process has given rise to the used car market, which is outperforming many other industries and is rising every day. The rising market for the used car has also resulted in a great increment in sales of Used Cars. Used Car Sales are on a global increase. But, determining the appropriate listing price of a used car is a challenging task, due to the many factors that drive prices of a used vehicle in the market. And that is why there is an urgent need for a system which can accurately predict the price of a used car. considering all the factors that affect the price of a used car. Keywords: Used Car Price Prediction, Linear Regression, XGBoost, Decision Tree

scholarly journals Analysis Characteristics of Car Sales In E-Commerce Data Using Clustering Model

2019 ◽  
Vol 2 (1) ◽  
pp. 68-77
Author(s):  
Puspita Kencana Sari ◽  
Adelia Purwadinata
Keyword(s):  
Model Performance ◽  
Sale Price ◽  
Community Based ◽  
Car Traffic ◽  
Clustering Model ◽  
Car Sales ◽  
Study Characteristics ◽  
Used Cars ◽  
Use Of The Internet ◽  
Buying In

The number of car sales in e-commerce is currently increase along with the increasing use of the Internet in Indonesia. Purchases of Car in Indonesia are currently get higher, especially in used cars, which are a necessity for the community based on the odd-even system of car traffic policies currently applied in Jakarta. This research aims to study characteristics of clusters formed in e-commerce site to predict how are the car sales segmentation. Data is collected from big-two e-commerce site about car selling and buying in Indonesia. Clustering model is build using K-Means method and Davies Bouldin Index as evaluation of the clusters formed. The results show for both clusters, the first cluster has characteristic lowers sale price and older production year. The second cluster has higher price with latest production. From the model performance, evaluation from Davies Bouldin Index  is quite good for both models. Keywords : Big Data, Clustering, K-Means, E-Commerce

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