Driving safety assessment for ride-hailing drivers

Mao, Huiying; Deng, Xinwei; Jiang, Hai; Shi, Liang; Li, Hao; Tuo, Liheng; Shi, Donghai; Guo, Feng

doi:10.1016/j.aap.2020.105574

Cited by 40 publications

(18 citation statements)

References 23 publications

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“…Importantly, for capacity comparisons, our study tries to provide a solution for road safety. As Mao et al (2021) mentioned through big data, long drives and peak-hour driving are the main reasons for accidents. Therefore, we proved that the E-bike was a good solution compared with motorbikes because E-bike users need bikes for a maximum of 100 km in 2 days' usage and speed capacity from 20 to 30 km/h.…”

Section: Resultsmentioning

confidence: 99%

Factors Affecting Electric Bike Adoption: Seeking an Energy-Efficient Solution for the Post-COVID Era

Yasir

Ahmad

et al. 2022

Front. Energy Res.

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Sustainability think tanks such as the United Nations Organization have a strong focus on achieving economic and environmental sustainability goals globally. On the road to sustainable development, electric bike (E-bike) adoption is crucial. Nevertheless, research on the factors associated with E-Bike use, especially the psychological, financial, and capacity factors, has remained unexplored. This paper extends the theory of planned behavior with six novel factors related to individual choices to analyze E-bike adoption behavior. A sample of 507 Chinese bike riders is collected through the snowball sampling technique. The sample is estimated through structural equation modeling. The key findings are as follows: first, speed capacity, mileage capacity, and real-time camera positively drove E-bike adoption intention. Second, price differentiation negatively affected E-bike adoption intention. Third, the theory of planned behavior factors, including perceived relative advantage, cost savings, subjective norms, perceived behavioral control, and attitudes toward E-bike adoption, proved to be drivers of E-bike adoption intention. Finally, cost savings are the most critical factor of E-bike adoption intention, whereas perceived behavior control is the least critical factor. These results will help green transportation companies and emerging economies promote E-bike adoption to reach the environmental sustainability goals of the United Nations.

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Section: Resultsmentioning

confidence: 99%

Factors Affecting Electric Bike Adoption: Seeking an Energy-Efficient Solution for the Post-COVID Era

Yasir

Ahmad

et al. 2022

Front. Energy Res.

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“…SHAP stands for "SHapley Additive exPlanations", a machine learning (ML) approach to explain models predictions and provide interpretability of an ML model. First presented by Lloyd Shapley, it uses Shapley values to interpret the model's output 16 , 23 , 40 , 41 . The Shapley value of a feature is equal to the difference between the average prediction value of samples with and without this feature 42 .…”

Section: Methodsmentioning

confidence: 99%

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

Fatahi

Nasiri

Dadfar

et al. 2022

Sci Rep

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Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.

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“…In case of any hazardous driving situation, the driver's mind triggers an acute stress response due to increased activity in the sympathetic nervous system, which may stop the body from taking appropriate action timely and may lead to severe losses [4]- [6]. Professional drivers are more prone to stress than workers in other sectors [7], [8]. Stress negatively impacts driving behavior, which may often lead to traffic accidents, thus causing significant damage to humans and vehicles every year [9], [10].…”

Section: Introductionmentioning

confidence: 99%

ECG-Based Driver’s Stress Detection Using Deep Transfer Learning and Fuzzy Logic Approaches

et al. 2022

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Driver's stress detection is a critical research area to help reduce the likelihood of traffic accidents and driver's health complexities due to prolonged stress. Previous work in this area is heavily based on traditional machine learning models to classify the driver's stress levels using handcrafted features extraction techniques. Extracting the best features using these approaches is always a challenging task. Recently, deep learning techniques have emerged for constructing reliable features automatically and classifying the classes with high accuracy. However, large deep learning models face gradient exploding or vanishing problems. Moreover, acquiring a large dataset for training an entire network from scratch is also a challenging task. This paper is based on the deep transfer learning technique to avoid these problems and to reduce computational cost and time. Seven models are proposed for real-world driver's stress levels detection using Electrocardiogram (ECG) signals. Different Convolutional Neural Network (CNN)-based pre-trained networks are used to classify the driver's three stress levels. The time-frequency ECG components for the three stress levels are obtained as scalogram images using a normalized Continuous Wavelet Transform (CWT) filter bank and Morse wavelet. Results show that Model 5 based on Xception outperforms the GoogLeNet, InceptionResNetV2, and InceptionV3 based models by 11.32%, 11.32%, 9.45%, 7.54%, 5.66%, and 1.88% respectively and achieves 98.11% overall validation accuracy. Ranking estimation using fuzzy logic approach shows that Xception based Model 5 achieves the highest rank for driver's high and medium stress levels, while DenseNet-201 based Model 4 achieves the highest rank for low-stress level detection among the other models.

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Driving safety assessment for ride-hailing drivers

Cited by 40 publications

References 23 publications

Factors Affecting Electric Bike Adoption: Seeking an Energy-Efficient Solution for the Post-COVID Era

Factors Affecting Electric Bike Adoption: Seeking an Energy-Efficient Solution for the Post-COVID Era

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

ECG-Based Driver’s Stress Detection Using Deep Transfer Learning and Fuzzy Logic Approaches

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