目录
摘要 II
关键词 II
Abstract III
引言
引言 1
1 绪论 1
1．1 研究背景与意义 1
1．2 研究现状 1
1．3 本文结构 2
2 不良驾驶行为的挖掘 3
2．1 数据预处理 3
2．1．1 处理思路 4
2．1．2 处理方法 5
2．1．3 处理结果 5
2．2 行驶量的计算 5
2．3 不良驾驶行为的特征提取 6
2．4 数据计算结果 9
3 基于不良驾驶行为的行车安全评价模型 11
3．1 建立安全评价指标体系 11
3．2 基于熵权法确定权重 11
3．3 基于TOPSIS与熵权法的行车安全评价模型 12
4 基于环境因素和不良驾驶行为的行车安全综合评价模型 14
4．1 建立综合评价指标体系 14
4．2 自然环境的分析 14
4．3 基于层次分析法（AHP）确定权重 16
4．4 基于AHP和模糊综合评价法的行车安全综合评价模型 19
4．5 小结 21
5 总结与展望 22
致谢 22
参考文献 23
附录 23
数据分析在行车安全评价模型中的应用
摘 要
随着车联网技术的兴起，我们对于车辆驾驶行为中的数据采集更加方便、快捷、有效。车联网系统可以满足对于车辆安全管理的需求，进行有效监督。为了提高安全管理水平和运输效率，现在很多运输行业都利用车联网来分析运输车辆的行驶数据。
本文对某企业的车联网系统数据进行数据分析，首先对运输车辆的数据进行处理与分析，对130辆运输车辆的行驶状态，如速度、路程、加速度等进行计算，并提取车辆不良驾驶行为的特征，进行统计分析。在此基础上，采用熵权法确定权重，并利用TOPSIS建立基于驾驶行为的行车安全评价模型。并且进一步综合考虑安全、效率和节能，以及自然气象条件和道路状况等因素，建立行车安全的综合评 *51今日免费论文网|www.51jrft.com +Q: &351916072& 
价模型，使运输车辆管理部门能够更有效地管理车辆。
本文的评价模型既充分利用了车联网原始数据中的信息，增加了模型的客观性，又将模糊性概念进行定量化分析，提高模型的实用性。最后，通过赋予运输车辆安全得分的形式，使评价结果更加直观。
APPLICATION OF DATA ANALYSIS IN DRIVING SAFETY EVALUATION MODEL
ABSTRACT
With the rise of Internet of Vehicles technology, it is more convenient, fast and effective for data collection in vehicle driving behavior. The Internet of Vehicles system can meet the requirements for vehicle safety management and carry out effective supervision. In order to improve the safety management level and transportation efficiency, many transportation industries now use the Internet of Vehicles to analyze the driving data of transportation vehicles.
In this paper, we analyze the data of an enterprises Internet of Vehicles system. First, we process and analyze the data of transportation vehicles, and calculate the driving status of 130 transportation vehicles, such as speed, distance, acceleration and so on. And we extract the characteristics of the vehicle’s bad driving behavior for analysis. On this basis, the entropy weight method is used to determine the weights, and TOPSIS is used to establish a driving safety evaluation model based on driving behavior. Furthermore, we consider factors such as meteorological conditions and road conditions comprehensively, and establish a comprehensive evaluation model of driving safety. This enables the transportation vehicle management department to manage vehicles more efficiently.
The evaluation model in this paper not only makes full use of the information in the original data of the Internet of Vehicles, which increases the objectivity of the model, but also analyzes the concept of ambiguity quantitatively, which increases the practicality of the model. Finally, by giving the transportation vehicle a safety score, the evaluation results are more intuitive.

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