scholarly journals Evaluation of the influence of terrain and traffic road conditions on the driver’s driving performances by applying machine learning

2021 ◽  
pp. 355-355
Author(s):  
Davor Vujanovic ◽  
Sladjana Jankovic ◽  
Marko Stokic ◽  
Stefan Zdravkovic
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Co2 Emission ◽  
Aggressive Driving ◽  
Driving Style ◽  
The Road ◽  
Traffic Conditions ◽  
Road Section ◽  
Vehicle Fleet ◽  
Cargo Transportation

In this paper, research is done in the influence of different terrain and traffic conditions on road sections on the driver?s driving performances, i.e. on the car energy efficiency and CO2 emission. A methodology aimed at determining to which extent unfavorable traffic and/or terrain conditions on a road section contribute to the driver?s worse driving performances, and also to determine when the driver?s aggressive driving style is responsible for greater fuel consumption and greater CO2 emission is proposed. In order to apply the proposed methodology, a research study was carried out in a cargo transportation company and 12 drives who drove the same vehicle on five different road sections were selected. As many as 284 014 of the instances of the data about the defined parameters of the road section and the driver?s driving style were collected, based on which and with the help of machine learning a prediction of the scores for the road section and the scores for the driver?s driving style was performed. The obtained results have shown that the proposed methodology is a useful tool for managers enabling them to simply and quickly determine potential room for increasing the energy efficiency of the vehicle fleet and decreasing CO2 emission.

Study on Satisfaction Evaluation of Urban Road Traffic Conditions

2013 ◽  
Vol 361-363 ◽  
pp. 2344-2348
Author(s):  
Jing Fei Yu ◽  
Li Dong Wang ◽  
Nan Nan Wang ◽  
Xin Jie Zhang
Keyword(s):  
Road Traffic ◽  
Common Factors ◽  
Weighting Factor ◽  
Actual Situation ◽  
Urban Road ◽  
The Road ◽  
Traffic Conditions ◽  
Road Section ◽  
Four Levels ◽  
Satisfaction Evaluation

From the basic feelings of urban road use, the paper selects 15 indicators to factor analysis for satisfaction of urban road use; those indicators are attributed to the four common factors, final the scores of integrated weighting factor is taken variable to cluster analysis. The results showed that the satisfaction of 13 road sections that was selected can be divides into four levels. By comparison, the results of analysis are more in line with the actual situation of the road section, therefore the results can provide a basis to enhance and improve road conditions for the relevant personnel.

scholarly journals Risk-regulatory increase of trajectory safety, productivity and energy efficiency of motor transport operations in conditions of uncertainty

2019 ◽  
Vol 294 ◽  
pp. 01012
Author(s):  
Arseniy Khabutdinov
Keyword(s):  
Energy Efficiency ◽  
Information Complexity ◽  
Information Uncertainty ◽  
Discrete Energy ◽  
Motor Transport ◽  
The Road ◽  
Road Section ◽  
Operator Interface ◽  
And Control ◽  
The Mathematical Model

The proposed method of complex increase of trajectory safety, productivity and energy efficiency of motor transport operations taking into account the laws of vehicle algorithmic control in difficult traffic conditions. Proceeding from the provisions of the theory of energy efficiency of a generalized type car and the laws of its adaptively-discrete energy of motion, the processes of functioning of the transport-ergatic systems “Driver-Vehicle” (TESDV) and control of the car using adaptive driving procedures (productive, safe, energy-efficient) in the conditions of information uncertainty of locally trajectory situations (LTS). In this work presents logic-procedural and mathematical models for the analysis of risk-regulatory algorithms for control of vehicle, taking into account the functioning of TESDV and quantitative assessment of operator-interface complexity of road sections. The basis of the method of estimating the information complexity of the operator-road interface in relation to any element of the road is the mathematical model of the entropy of the object (as a measure of disorder), which is used in technical cybernetics. For each section of the road, the total indicator of information complexity is determined, which takes into account three levels of uncertainty: low, medium and high. At medium and high levels of complexity of the road section, TESDV implements a risk-regulating automobile control algorithm. Proceeding from the essence of the regulatory labor procedures of driving, as well as their role in the formation of the modes of the TESDV, proposed results of operational simulation of algorithms regulation of locally-trajectory driving risks (LTR): counterproductive, sensory-tempo, incidental and anergic.

scholarly journals Exploring the Energy Efficiency of Electric Vehicles with Driving Behavioral Data from a Field Test and Questionnaire

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Kezhen Hu ◽  
Jianping Wu ◽  
Mingyu Liu
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Field Test ◽  
Electric Vehicles ◽  
Field Tests ◽  
Driving Behavior ◽  
Future Research ◽  
Driving Style ◽  
Traffic Conditions ◽  
Energy Consumption Prediction

With increasing concerns about urban air quality and carbon emissions, electric vehicles (EVs) have gained popularity in megacities, especially in Europe and Asia. The energy consumption of EVs has subsequently caught researchers’ attention. However, the exploration of energy consumption of EVs has largely focused on people’s revealed driving behavior and rarely touched on their self-perception of driving styles. In this paper, we developed a more human-centric approach, aiming to investigate how the energy efficiency of EVs is shaped by the driving behavior and driving style in the urban scenario from field test data and driving style questionnaires (DSQs). Field tests were carried out on a designated route for a total of 13 drivers in the city of Beijing, where vehicle operation parameters were recorded under both congested and smooth traffic conditions. DSQs were collected from a larger pool of drivers including the field test drivers to be applied to driving style factor analysis. The results of a correlation analysis demonstrate the dynamic interaction between drivers’ revealed behavior and stated driving style under different traffic conditions. We also proposed an energy consumption prediction model with the fusion of collected driving parameters and DSQ data and the result is promising. We hope that this study would draw inspiration for future research on people’s transitioning driving behavior in an electric-mobility era.

scholarly journals The road network traffic capacity taking into account public transport stops layout method

2020 ◽  
Vol 2020 (2) ◽  
pp. 13-22
Author(s):  
Ievgen Medvediev ◽  
◽  
Sergiy Soroka ◽  
Keyword(s):  
Public Transport ◽  
Road Network ◽  
Road Traffic ◽  
Negative Impact ◽  
Negative Effects ◽  
The Public ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle Components ◽  
Vehicle Fleet

The vigorous motorization process is taking place in a growing number of countries year by year, and the number of people involved in road traffic is constantly increasing. The growth of the vehicle fleet and the volume of transportation lead to an increase in traffic that in the context of cities with a historical build-up leads to a traffic problem. It is particularly acute at the junctions of the road network. There is an increase in transport delays, queues, and congestion, causing reduce in speed, excessive fuel consumption, and increased wear-out of vehicle components and assemblies. These questions are constantly analyzed both in theoretical and practical terms. Today, the negative effects of motorization cannot be eliminated, and effective measures need to be developed to reduce their negative impact on the urban environment. An irrational location of public transport stops leads to a significant increase in transport delays. Respectively, the objective is to determine the optimal layout of the public transport stops on the street network, taking into account the existing and designed traffic conditions.

Reducing Energy Costs With Electric, Diesel and Dual-Powered Locomotives

2009 ◽  
Author(s):  
Janis Vitins
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Savings ◽  
New Technologies ◽  
Cost Savings ◽  
Energy Costs ◽  
Aggressive Driving ◽  
Driving Style ◽  
Mixed Network ◽  
Reduce Energy Consumption

Typically, the costs for traction energy add up to 20% or more of the total train operating costs with electric locomotives in Europe. Therefore, there is a high incentive for the railroads to reduce energy consumption and thus to improve operating margins. Additionally, rising costs for energy as well as environmental aspects will increase the need to reduce energy consumption in the future. Firstly, on electric locomotives the largest energy savings are obtained from power regeneration at braking. In this mode the locomotive acts as a moving power generator feeding energy back into the catenary network. Savings are typically in the range of 10 to 30%. Secondly, the driving style has a high impact on energy costs. Energy consumption can be lowered by more than 20% through an energy conscious driving style compared to aggressive driving. Thirdly, the energy efficiency of the whole traction chain is important. Electric locomotives designed for AC catenaries have an overall energy efficiency of up to 86%. Locomotives designed for 1.5 or 3 kV DC catenaries can have an overall energy efficiency of up to 90%. New technologies can potentially help to increase the power efficiencies even further. Apart from using efficient diesel engines, the fuel costs of diesel-electric locomotives can be reduced much in the same way as with electric locomotives. Regeneration of braking power on diesel-electric locomotives is, however, limited to feeding the auxiliaries and head end power (HEP) to passenger coaches. In Europe the energy costs per hauled ton-km are typically much lower with electric than with diesel traction. This gives dual-powered locomotives the advantage of overall lower energy costs for operation on both electrified and non-electrified networks. First estimates show that the total energy costs (diesel and electric operation) can be reduced by more than 35% in a mixed network with 80% electrification with a dual-powered locomotive compared to a diesel locomotive running the same train on the same route. In addition, the dual-powered locomotive provides major cost savings and increased quality of service with a one seat ride.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

Increasing the energy efficiency of a data center based on machine learning

2021 ◽  
Author(s):  
Zhen Yang ◽  
Jinhong Du ◽  
Yiting Lin ◽  
Zhen Du ◽  
Li Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Data Center

Intelligent control of complex traffic conditions based on machine learning

2021 ◽  
pp. 1-12
Author(s):  
Zhe Li
Keyword(s):  
Machine Learning ◽  
Simulation Model ◽  
Traffic Simulation ◽  
Learning Algorithms ◽  
Control Model ◽  
Machine Learning Algorithms ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Traffic Conditions ◽  
Simulation Control

 In order to improve the simulation effect of complex traffic conditions, based on machine learning algorithms, this paper builds a simulation model. Starting from the macroscopic traffic flow LWR theory, this paper introduces the process of establishing the original CTM mathematical model, and combines it with machine learning algorithms to improve it, and establishes the variable cell transmission model VCTM ordinary transmission, split transmission, and combined transmission mathematical expressions. Moreover, this paper establishes a road network simulation model to calibrate related simulation parameters. In addition, this paper combines the actual needs of complex traffic conditions analysis to construct a complex traffic simulation control model based on machine learning, and designs a hybrid microscopic traffic simulation system architecture to simulate all relevant factors of complex road conditions. Finally, this paper designs experiments to verify the performance of the simulation model. The research results show that the simulation control model of complex traffic conditions constructed in this paper has certain practical effects.

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