In an article published in the open access journal Sustainability, researchers studied the economy and safety of electric buses based on a driver’s driving behavior. The driving operation behavior was analyzed using remotely collected travel data of buses. Four economic and safety characteristic indicators were also retrieved using correlation examination, safety analysis, and an R2 test.
Study: Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior. Image Credit: Anastasia Sotchenko/Shutterstock.com
The economic assessment model was built using the Elman neural network, while the safety evaluation model was established using the extreme learning machine (ELM). A comprehensive and comparative analysis was conducted of the driving behavior of 10 drivers. The findings demonstrated that based on the Elman neural network algorithm and the ELM, the suggested evaluation model could effectively distinguish safe and economical driving behavior.
Additionally, a driving operation behavior graph was constructed. The findings demonstrated that changing the driving operation behavior of buses to one that was safer and more economical produced relatively stable features. The driver could execute a moderate driving operation in real-time when the vehicle's speed fluctuation was smooth. The findings further revealed a relation between economy and driving safety and that buses exercising higher security tend to be more economical.
The extensive use of electric public buses has a promising effect on emission reduction and energy conservation, which is crucial for sustainable development. This study provided a theoretical basis for planning the operation and maneuvering of electric buses, including acceleration functions, braking, and driving speed.
About the Study
Drivers play a crucial part in ensuring the safety of road traffic, and energy consumption and safety in transportation are currently topics of much discussion. Thus, the evaluation of driver behavior mechanisms has attracted global attention. According to previous research, driver behavior is responsible for approximately 30% of the improvement in vehicle fuel economy.
Currently, a sequence of driving behavior subtraction units, including sharp turning subtraction units, sharp acceleration subtraction units, sharp deceleration subtraction units, etc., are used to evaluate driving speed behavior in China. The driving behavior scores are calculated based on data such as the number of sharp turns and the abrupt acceleration and deceleration rate. However, the assessment procedure is hugely subjective, and data mining studies of the potential link between driving behavior and traffic safety are scarce.
Researchers are now concerned with assessing the economy and safety of driver driving behavior. Different techniques and criteria have been developed to evaluate driving behavior and classify and rate drivers based on their driving performance. The idea of a driver behavior profile (DBP) has been introduced to be used in research and practice.
Based on past driver operating data, a DBP is a broad and standardized measure of driver behavior that considers the magnitude and frequency of speeding, abrupt acceleration, and rapid braking. The efficiency of driving behavior interventions can be effectively assessed by integrating DBP and temporal and spatial data. Additionally, driver behavior analysis is frequently used as a benchmark for insurance investments, where premiums are decided per the safety and fuel efficiency standards assessed from previous driving data.
The practice of “eco-driving,” which efficiently minimizes fuel consumption and CO2 emissions, is a practical approach to saving energy when driving. Eco-driving involves optimizing the speed profile of vehicles and is a novel approach toward sustainability. Therefore, driving behavior analysis based on eco-driving optimization merits further study.
This study aimed to (i) clarify the selection procedure and the process of assessment indexes for the supplied dataset, (ii) evaluate the economy and safety of electric buses, (iii) and suggest a method with an enhanced evaluation effect.
The economy evaluation model based on Elman neural network and the safety evaluation model based on ELM were constructed to evaluate the safety and economy of passenger vehicles. Comparing the safety and economy evaluation findings followed the analysis of the evaluation results, and the correlation between the economy and safety of electric buses was further examined.
For data preparation, 10 electric buses operating in Beijing, with a collection period of one year and a sampling interval of 15 s, were considered. Vehicle velocity, acceleration, standard velocity deviation, acceleration rate of change, brake pedal travel, and gas pedal travel were first chosen as the distinguishing factors to be assessed to assess driver behavior.
The speed, standard deviation, acceleration, jerk, brake pedal stroke, and accelerator pedal stroke were used as the basis for the R2 test. Correlation test results showed a positive correlation between acceleration and pedal stroke, a strong correlation between speed and acceleration, a negative correlation between pedal stroke and brake pedal stroke, and a negative correlation between pedal stroke and brake stroke.
A correlation between safety and the economy of driving behavior could be found after evaluating the bus driving behavior. Bus drivers with higher safety scores also tended to have relatively higher economies.
The driving operation behavior graph of the bus with the highest and lowest economy and the bus with the highest and lowest safety was plotted based on the electric buses’ economy and safety assessment results. High safety and high economy buses were compared for variations in driving operation behavior between high and low safety and high and low economy buses. The findings showed that the bus with higher economy and safety had relatively consistent speed and fewer fluctuations, and the driving operation behavior was relatively stable.
This study assessed the economic and safety performance of 10 electric buses. Assessment indices of the dataset were extracted, and two different types of economic and safety evaluation models were constructed.
The findings revealed a correlation between the economy and safety factors in driving behavior, and buses with high safety ratings tended to be more economically inclined. The results also showed that bus drivers with better levels of safety and economy could perform proper driving maneuvers at the appropriate times, and the operating condition of their buses was relatively stable.
The researchers believe the paper can provide theoretical support for future investigations on the economy and safety evaluation of bus drivers’ driving behavior. Additionally, it may give relevant organizations the base for assessing and retraining driver security and energy saving.
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Zhou, Y., et al. (2022). Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior. Sustainability, 14(17), 10772. https://www.mdpi.com/2071-1050/14/17/10772