Abstract
Environmental protection is a fundamental policy in many countries, where the vehicle emission pollution turns to be outstanding as a main component of pollutions in environmental monitoring. Remote sensing technology has been widely used on vehicle emission detection recently and this is mainly due to the fast speed, reality, and large scale of the detection data retrieved from remote sensing methods. In the remote sensing process, the information about the fuel type and registration time of new cars and nonlocal registered vehicles usually cannot be accessed, leading to the failure in assessing vehicle pollution situations directly by analyzing emission pollutants. To handle this problem, this paper adopts data mining methods to analyze the remote sensing data to predict fuel type and registration time. This paper takes full use of decision tree, random forest, AdaBoost, XgBoost, and their fusion models to successfully make precise prediction for these two essential information and further employ them to an essential application: vehicle emission evaluation.
Highlights
In the USA, EPA (Environmental Protection Administration) proposed MOVES algorithm [2] to calculate the vehicle emission ratio in some fixed locations and periods of time. e Japanese government enforces the vehicle exhaust emission monitoring system in their country, and the emission behaviour of each vehicle in Japan can be checked on the official website of Japanese national transportation [3]
Wu [6] collected the values of CO2, HC, CO, and NO exhausted by 1092 vehicles in the Xian Yang city using simplified loaded mode. ey established regression equations between the emission value and vehicle information and found that the average emission value was highly related with the vehicle acceptability and the age of the vehicle
Important information is buried in the vehicle emission remote sensing data. is paper exploits data mining methods to deal with the data and obtain valuable knowledge from them. ere are three main directions in data mining: the improvements of classical data mining algorithms, ensemble learning algorithms, and data mining with deep learning. e improvements on classical algorithms are usually performed and employed in multiple application scenarios taking additional information into consideration
Summary
In the USA, EPA (Environmental Protection Administration) proposed MOVES algorithm [2] to calculate the vehicle emission ratio in some fixed locations and periods of time. e Japanese government enforces the vehicle exhaust emission monitoring system in their country, and the emission behaviour of each vehicle in Japan can be checked on the official website of Japanese national transportation [3]. Is paper introduces data mining technology to these valuable data to explore efficient information in vehicle exhaust emission detection. Important information is buried in the vehicle emission remote sensing data. Many traffic engineering-related researches mainly focus on analyzing relevant data such as traffic diversion [19], traffic safety monitoring [20], engine diagnosis [21], road safety [22] and traffic accident [23], and remote sensing image processing [24,25,26,27,28,29,30,31,32,33,34,35], extracting useful information and digging out valuable knowledge. Chen et al [38] proposed a driving-events-based ecodriving behaviour evaluation model and the model was proved to be highly accurate (96.72%)
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