Driving Behavior Evaluation Model Base on Big Data From Internet of Vehicles

2019 ◽  
Vol 10 (4) ◽  
pp. 78-95 ◽  
Author(s):  
Ruru Hao ◽  
Hangzheng Yang ◽  
Zhou Zhou
Keyword(s):  
Fuel Consumption ◽  
Evaluation Model ◽  
Driving Behavior ◽  
Characteristic Parameters ◽  
Accuracy Rate ◽  
Operating Data ◽  
Model Base ◽  
Proposed Model ◽  
Consumption Data ◽  
Vehicle Networking

This article attempts to evaluate whether a driving behavior is fuel-efficient. To solve this problem, a driving behavior evaluation model was proposed in this article. First, the operating data and fuel consumption data of five trucks were obtained from the vehicle networking system. Four characteristic parameters, which are closely related to fuel consumption, were extracted from 19 sets of vehicle operating data. Then, K-means clustering combined with DBSCAN was adopted to cluster the four characteristic parameters into different driving behaviors. Three types of driving behavior were labeled respectively as low, medium and high fuel consumption driving behavior after clustering analysis. The clustering accuracy rate reached 79.7%. Finally, a fuel consumption-oriented driving behavior evaluation model was established. The model was trained with the labeled samples. The trained model can evaluate the driving behavior online and gives an evaluation of whether the driving behavior is fuel-efficient. The test results show that the prediction accuracy rate of the proposed model can reach to 77.13%.

A NOVEL FIVE-CATEGORY LOAN-RISK EVALUATION MODEL USING MULTICLASS LS-SVM BY PSO

2012 ◽  
Vol 11 (04) ◽  
pp. 857-874 ◽  
Author(s):  
JIE CAO ◽  
HONGKE LU ◽  
WEIWEI WANG ◽  
JIAN WANG
Keyword(s):  
Financial Regulation ◽  
Evaluation Model ◽  
Least Square ◽  
Support Vector ◽  
Accuracy Rate ◽  
Rural Credit ◽  
Proposed Model ◽  
Loan Risk ◽  
Regulation Approach ◽  
Risk Evaluation Model

Five-category loan classification (FCLC) is an international financial regulation approach. Recently, the application and implementation of FCLC in the Chinese microfinance bank has mostly relied on subjective judgment, and it is difficult to control and lower loan risk. In view of this, this paper is dedicated to researching and solving this problem by constructing the FCLC model based on improved particle-swarm optimization (PSO) and the multiclass, least-square, support-vector machine (LS-SVM). First, LS-SVM is the extension of SVM, which is proposed to achieve multiclass classification. Then, improved PSO is employed to determine the parameters of multiclass LS-SVM for improving classification accuracy. Finally, some experiments are carried out based on rural credit cooperative data to demonstrate the performance of our proposed model. The results show that the proposed model makes a distinct improvement in the accuracy rate compared with one-vs.-one (1-v-1) LS-SVM, one-vs.-rest (1-v-r) LS-SVM, 1-v-1 SVM, and 1-v-r SVM. In addition, it is an effective tool in solving the problem of loan-risk rating.

scholarly journals Driver’s Eco-Driving Behavior Evaluation Modeling Based on Driving Events

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Chen Chen ◽  
Xiaohua Zhao ◽  
Ying Yao ◽  
Yunlong Zhang ◽  
Jian Rong ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Evaluation Model ◽  
Effective Means ◽  
Principal Component ◽  
Driving Behavior ◽  
Driving Mode ◽  
Driving Skills ◽  
Long Time ◽  
Independent Variable ◽  
The Relationship

Eco-driving is an effective means to reduce vehicle fuel consumption. Although many researches and devices have been developed to introduce eco-driving, quantitative effects of driver behaviors on fuel consumption are still unclear, as well as quantitative eco-driving advices. To solve this problem and promote the application of eco-driving in China, a driving-events-based eco-driving behavior evaluation model was proposed in this paper. First, based on taxicab operating data, the relationship between three vehicle operating parameters (speed, acceleration, and driving mode duration) and fuel consumption was analyzed. Then, nine fuel-consumption-involved driving events (including Accelerating Sharply, Decelerating Sharply, and Long-Time Accelerating) were proposed and defined. Using the frequency of each driving event in a certain distance as independent variable and vehicle fuel consumption as dependent variable, principal component analysis (PCA) and multiple linear regression were applied to establish driver’s eco-driving behavior evaluation model. The model was proved to be highly accurate (96.72%). At last, based on the evaluation model, corresponding quantitative eco-driving advices were provided to help driver to improve their driving skills.

scholarly journals Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Ying Yao ◽  
Xiaohua Zhao ◽  
Chang Liu ◽  
Jian Rong ◽  
Yunlong Zhang ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Fuel Consumption ◽  
Prediction Models ◽  
Driving Behavior ◽  
Mobile Phone Data ◽  
Support Vector ◽  
Transportation Fuel ◽  
Average Speed ◽  
Consumption Data ◽  
Consumption Prediction

Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption.

scholarly journals Risky Driving Behavior Recognition Based on Vehicle Trajectory

2021 ◽  
Vol 18 (23) ◽  
pp. 12373
Author(s):  
Shengdi Chen ◽  
Qingwen Xue ◽  
Xiaochen Zhao ◽  
Yingying Xing ◽  
Jian John Lu
Keyword(s):  
Risk Evaluation ◽  
Evaluation Model ◽  
Driving Behavior ◽  
Trajectory Data ◽  
Behavior Recognition ◽  
Risky Driving ◽  
Car Following ◽  
Proposed Model ◽  
Behavior Based ◽  
Risk Evaluation Model

This paper proposes a measurement of risk (MOR) method to recognize risky driving behavior based on the trajectory data extracted from surveillance videos. Three types of risky driving behavior are studied in this paper, i.e., speed-unstable driving, serpentine driving, and risky car-following driving. The risky driving behavior recognition model contains an MOR-based risk evaluation model and an MOR threshold selection method. An MOR-based risk evaluation model is established for three types of risky driving behavior based on driving features to quantify collision risk. Then, we propose two methods, i.e., the distribution-based method and the boxplot-based method, to determine the threshold value of the MOR to recognize risky driving behavior. Finally, the trajectory data extracted from UAV videos are used to validate the proposed model. The impact of vehicle types is also taken into consideration in the model. The results show that there are significant differences between threshold values for cars and heavy trucks when performing speed-unstable driving and risky car-following driving. In addition, the difference between the proportion of recognized risky driving behavior in the testing dataset compared with that in the training dataset is limited to less than 3.5%. The recognition accuracy of risky driving behavior with the boxplot- and distribution-based methods are, respectively, 91% and 86%, indicating the validation of the proposed model. The proposed model can be widely applied to risky driving behavior recognition in video-based surveillance systems.

scholarly journals Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

2021 ◽  
Vol 18 (23) ◽  
pp. 12452
Author(s):  
Qiong Bao ◽  
Hanrun Tang ◽  
Yongjun Shen
Keyword(s):  
Relative Risk ◽  
Risk Evaluation ◽  
Absolute Risk ◽  
Evaluation Model ◽  
Optimization Method ◽  
Driving Behavior ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Proposed Model ◽  
Risky Driving Behaviors

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

Real-world assessment of urban bus transport in a medium-sized city of the Middle East: Driving behavior, emission performance, and fuel consumption

2021 ◽  
Vol 12 (3) ◽  
pp. 113-124 ◽  
Author(s):  
Omid Ghaffarpasand ◽  
Mohammad Reza Talaie ◽  
Hossein Ahmadikia ◽  
Amirreza Talaie Khozani ◽  
Maryam Davari Shalamzari ◽  
...  
Keyword(s):  
Middle East ◽  
Fuel Consumption ◽  
Real World ◽  
Driving Behavior ◽  
Emission Performance ◽  
Urban Bus ◽  
Bus Transport

scholarly journals Research on the Evaluation Model of Urban Tourism Management Efficiency with Uncertain Linguistic Information

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Song-Mao Wang ◽  
Liang-Yan Fang ◽  
Feng Deng
Keyword(s):  
Decision Making ◽  
Evaluation Model ◽  
Multiple Attribute Decision Making ◽  
Urban Tourism ◽  
Weighted Averaging ◽  
Linguistic Information ◽  
Management Efficiency ◽  
Tourism Management ◽  
Proposed Model ◽  
Multiple Attribute

We investigate the multiple attribute decision making problems for evaluating the urban tourism management efficiency with uncertain linguistic information. We utilize the uncertain linguistic weighted averaging (ULWA) operator to aggregate the uncertain linguistic information corresponding to each alternative and get the overall value of the alternatives and, then rank the alternatives and select the most desirable one(s). Finally, a numerical example for evaluating the urban tourism management efficiency with uncertain linguistic information is used to illustrate the proposed model.

scholarly journals A Proposed Model for Detecting Learning Styles Based on Agent Learning

2016 ◽  
Vol 11 (10) ◽  
pp. 65 ◽  
Author(s):  
M S Hasibuan ◽  
L E Nugroho ◽  
P I Santosa ◽  
S S Kusumawardani
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Evaluation Model ◽  
Learning System ◽  
Learning Activities ◽  
Instructional Materials ◽  
Data Driven ◽  
Automatic Learning ◽  
Proposed Model ◽  
Agent Learning

A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.

scholarly journals Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

2021 ◽  
Vol 336 ◽  
pp. 05008
Author(s):  
Cheng Wang ◽  
Sirui Huang ◽  
Ya Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Massive Open Online ◽  
Data Set ◽  
Proposed Model ◽  
Contextual Feature

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

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