Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2295 2024-03-18 10:20:21 |
2 Reference format revised. -202 word(s) 2093 2024-03-19 02:49:28 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Ma, Z.; Jørgensen, B.N.; Ma, Z. Energy-Efficient Driving. Encyclopedia. Available online: (accessed on 15 April 2024).
Ma Z, Jørgensen BN, Ma Z. Energy-Efficient Driving. Encyclopedia. Available at: Accessed April 15, 2024.
Ma, Zhipeng, Bo Nørregaard Jørgensen, Zheng Ma. "Energy-Efficient Driving" Encyclopedia, (accessed April 15, 2024).
Ma, Z., Jørgensen, B.N., & Ma, Z. (2024, March 18). Energy-Efficient Driving. In Encyclopedia.
Ma, Zhipeng, et al. "Energy-Efficient Driving." Encyclopedia. Web. 18 March, 2024.
Energy-Efficient Driving

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles’ fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available.

driving behavior driving pattern energy efficiency artificial intelligence

1. Introduction

The CO2 emissions in the transportation sector form a significant component of the manmade greenhouse gas (GHG) which results in global warming through the greenhouse effect. While this sector gradually decarbonizes, it still contributes to almost 30% of GHG emissions [1][2], 65% of which are caused by road transport [3]. Therefore, the strategies to improve fuel economy need to be studied to reduce CO2 emissions.
Energy-efficient driving technology is an important factor in reducing vehicle fuel consumption. It refers to the decisions that a driver can make to improve the efficiency of the engine [4]. These decisions could be multidimensional, involving vehicle selection [5], route planning [6], and driving behavior recommendation [7]. Some industrial stakeholders, like the public transportation and logistics companies, already have enough vehicles, and the route should be planned based on not only the fuel economy, but also the work requirements and efficiency. The vehicle and route conditions can only be controlled by their manufacturers or constructors, and the drivers can only optimize their driving behaviors and styles to enhance energy efficiency. Therefore, driving behavior analysis is an important research domain to recommend the least fuel-consumption driving style in such industry sectors.
The relationship between fuel consumption and driving behavior has been a popular research topic recently with the development of artificial intelligence (AI) and machine learning (ML). The main factors representing and influencing driving behavior include velocity, acceleration, gear, road parameters, weather, etc., and the data can be collected through sensor networks and CANbus (controller area network) [8]. With the ever-increasing volume of generated data, traditional models like linear regression cannot produce accurate estimation results in real-world applications [9]. In recent years, many state-of-the-art machine learning (ML) (e.g., random forest [10] and neural networks [11]) and reinforcement learning (RL) [12] models have been developed to perform research on energy consumption. Random forest is utilized in [10] to classify the road types and more precise environment perception helps to enhance vehicle energy efficiency. The neural networks model is applied in [11] to estimate the fuel consumption of three vehicles on distinct road conditions. The study in [12] utilizes an RL model to generate energy-saving driving behaviors and integrates vision-perceptive methods to achieve higher energy efficiency. Meanwhile, some studies apply classification [13] or clustering [14] algorithms to find out the best practices in driving styles for fuel saving.

2. Energy-Efficient Driving

2.1. Overview of Energy-Efficient Driving Research

The energy-efficient driving technology represents the strategies to merge vehicle speed management approaches and GHG emission reduction techniques, aiming at minimizing fuel consumption [15]. Of course, the benefits of energy-efficient driving go beyond energy saving. For one thing, the driving cost to the individuals and the companies can be reduced. For another, when developing energy-efficient driving strategies, safety conditions are also included, so as to reduce accidents and traffic fatalities [16]. Hence, the goal of energy-efficient driving technology is to help the driver choose a CO2-reduction driving strategy under some safety and law conditions [15].
Recent research reveals that fuel consumption can be reduced by approximately 15% under different optimization approaches and various road conditions. The GMM model is employed in [7] to learn different driving modes and the most energy-saving acceleration model is calculated to save up to 15.81% energy. The hybrid RL method proposed in [12] reduces energy consumption by 12.70%. The developed data-driven optimal energy consumption cost model and optimal battery current model [17] are, respectively, constructed via two neural networks and can improve fuel economy by up to 16.7%. There are different topics of energy-efficient driving deserving investigation, and distinct research aspects to optimize the energy-efficient driving strategies. Figure 1 summarizes the research on energy-efficient driving, and they are analyzed in two parts in the following discussion, including the popular research topics and the applied technical methodologies.
Figure 1. Summarization of energy-efficient driving research.
As Figure 1 demonstrates, the main topics in energy-efficient driving research include vehicle selection, route planning, and driving behaviors.
In the studies of vehicle selection, the relationship between vehicle parameters, including mass, the tire–road friction coefficient, power train parameters, etc., are analyzed, and the structure of the vehicle is optimized to save fuel consumption. For instance, neural networks are employed to map the efficiency of a planetary gearbox for an electric vehicle based on powertrain data generated from the efficiency experiments and design an energy-efficient powertrain in [18].
Vehicle route planning is another important study aspect. An energy-saving routing algorithm is capable of reducing the driving distance and frequency of acceleration/deceleration. A routing algorithm based on historical driving data to locate in energy-efficient routes is proposed by [19], where 51.5% of energy is saved in the case study. Lastly, ecological driving behavior plays an important role in fuel saving through the proper control of gas/brake/clutch pedals [20]. The driving-behavior prediction can guide the drivers to adjust the control style of the vehicle, avoiding inefficient driving.

2.2. Data Sources for Energy-Efficient Driving Research

Before modeling driving behavior and studying its impact on fuel consumption, it is significant to collect the vehicle and driver data in real-world conditions. There are various data sources of driving behaviors, including simulation tools, meters, CANbus, OBDs (on-board diagnostics), and smartphones, which are shown in Table 1.
Table 1. Data sources for energy-efficient driving.
Data Sources Counts Ref.
Simulation data 14 [21][22][23][24][25][26][27][28][29][30][31][32][33][34]
Embedded sensors Meters 5 [14][17][35][36][37]
CANbus 6 [13][35][38][39][40][41]
OBDs 5 [7][11][12][42][43]
Smartphones 2 [39][43]
Additional sensors 9 [10][11][12][13][14][40][41][44][45]
The easily implemented and efficient method is to collect data in the simulation environment so that multiple routes and climates can be set and multi-dimensional datasets are obtained easily. For instance, the driving performance data in a driving simulator is generated in [21], and the driving behavior data in a networked game are collected and analyzed in [46].
Although simulation data are easy to generate and abundant, they still differ from real-world data because some conditions might be simplified in the simulation systems.
The basic data collection method is to read the odometer and log the fuel use, mileage, and velocity manually. This method is relatively simple and cheap, but human errors may occur in the data recording [4]. In addition, only a few observations and variables can be recorded manually, so the volume may not be big enough for big data analysis.
A more efficient data collection way is to utilize data loggers, which are plugged into CANbus and OBDs. CANbus is the controller area network bus, allowing microcontrollers to communicate with each other’s applications without a host computer [47]. CANbus can provide detailed data concerning the running conditions of a vehicle, e.g., fuel consumption per second, real-time position, velocity, acceleration, and engine conditions. OBDs represent on-board diagnostic scanners and are usually connected to the engine control unit to provide real-time driving data [48]. The data loggers connected to the CANbus and OBDs are allowed to collect real-time data during normal driving.
Furthermore, smartphones have been used for data collection recently. Many sensors and software (e.g., the GPS and the accelerometer) are equipped in smartphones, so they are capable of collecting most of the required data [49]. The weather and road conditions are available online and collected via smartphones as well. The combination of smartphones and dataloggers in CANbus or OBDs fulfills almost all the requirements of the input datasets. 

2.3. Variables Reflecting Energy Efficiency

To assess energy efficiency, different variables are selected and computed in distinct research. Table 2 shows the four variables regarding energy efficiency.
Table 2. Variables reflecting energy efficiency.
Variables Units
Fuel consumption mL/s [13][41]
g [7]
L/km [8]
L/100 km [11][29][42][50]
gallon/mile [35]
mL [23][24][25]
kg [51]
gallon [32][33]
Electrical energy consumption Wh/km [10][38]
J [38]
kwh/100 km [14][52]
kwh [36][37]
Wh [26]
kJ/s [31]
Fuel economy km/L [44]
mile/gallon [28]
CO2 emissions g/km [21][52]
g/mile [35]
g [34]

The variables are mainly divided into energy consumption and energy economy. Energy consumption signifies the amount of fuel or electricity a vehicle utilizes to cover a specific distance. 

2.4. Factors Impacting Energy-Efficient Driving Behaviors

Various variables of driving behavior are collected in different studies. Eleven main factors can be concluded based on the literature, and each includes various sub-variables. These factors can be divided into two groups: factors reflecting driving behavior and factors affecting driving behavior. The factors reflecting driving behavior include the speed, acceleration, deceleration, pedal, steering, gear selection, and engine, which are controlled by the drivers. The factors affecting driving behavior represent the objective vehicle and environmental parameters, involving distance, weather-, traffic signal-, and road conditions. Their definitions are shown in Table 3.
Table 3. Definitions of influencing factors of energy-efficient driving.
Categories Influencing Factors Definition
Factors reflecting driving behaviors Speed real-time linear velocity of the vehicle
Acceleration real-time acceleration of the vehicle
Deceleration real-time deceleration of the vehicle
Pedal (gas/brake/clutch) pedal force, pedal frequency, and pedal depth
Steering angle of the rotating steering wheel
Gear selection of gear ratio of a manual vehicle
Engine engine load and engine speed
Factors impacting driving behaviors Distance distance between vehicles, distance from vehicle to the traffic light infrastructure and distance from vehicle to the station
Weather temperature, visibility, rainfall, and wind speed
Traffic signal traffic signal status generated from the infrastructures
Road road geometry, road slope and radius of the curve of the road

An optimized engine control strategy is another significant approach for fuel saving through the speed selection of the transmission box and the control of pedal force and depth. The gear shift and pedal control can affect the energy efficiency of the engine [40]

Distance is the most popular research point that impacts driving behavior, which contains multiple features, including the distances to the traffic lights or the station and those between the vehicles in different scenarios and rules [12][24][51]

The combination of the mentioned features is also discussed in the literature. For instance, four types of influential variables including vehicle characteristics, driver characteristics, driving behavior, and weather conditions are summarized in [8], and a fuel-consumption classification model based on the decision tree was established to train the generated datasets. The driving behavior data from the CANbus dataset and questionnaires is collected in [38] with the velocity, acceleration, and steering wheel angle being generated from CANbus, while the questionnaire assembles the subjective driving characteristics (e.g., self-confidence, impatience, and rude driving behavior). The data from a driven vehicle, including the velocity, acceleration, pedal variables, engine parameters, road conditions, etc., are measured in [50], and a real-time fuel consumption estimation method is proposed for recommending the optimal speed in time. Furthermore, a deep-learning framework is developed in [11] to analyze the data from the OBD-II module and CANbus, where the input features include the velocity and engine parameters.

2.5. AI Models Applied in Energy-Efficient Driving Research

2.5.1. Prediction Models for Energy-Efficient Driving

The prediction models include regression and classification tasks. In regression tasks, the values of the target features (e.g., fuel consumption) are predicted by training a combination function based on the input variables [21][23]. In classification tasks, the driving styles are classified into several groups (e.g., energy-efficient and inefficient styles) [43][45]. Specifically, the unsupervised classification methods (the clustering methods) group the data based on their similarities [39].
The linear regression family is the most popular model in the cases because it is a transparent and easy-implemented approach [2][7][13][21][22][29][38][44][50][53]. The contribution of each input variable can be recognized from the weights, allowing good mathematical interpretation [54]. For instance, bivariate regression methods are used in [13] to predict the relationships between each influencing factor and fuel consumption. The energy-efficient driving suggestions are made based on a series of regression lines. For example, the contributions of different energy-efficient driving rules to CO2 emission reduction are measured through linear regression analysis in various scenarios in [21]

2.5.2. Reinforcement Learning for Energy-Efficient Driving

Reinforcement learning (RL) is mainly used to optimize agents’ actions in an environment by repeated interactions to maximize the cumulative reward [55][56]. The establishment of an RL environment is typically based on the Markov decision process (MDP) which is a discrete-time stochastic control process [57]. An RL framework is agent-based, with the agent being the targeted vehicle whose fuel consumption is targeted to be minimized, and the surrounding vehicles, roads, signals, traffic rules, and other parameters constitute the environment [25]. The general scenario is visualized in Figure 2. The roadside units represent the facilities that affect the driving styles, including bus stations, schools, etc.
Figure 2. RL energy-efficient driving scenario.
Compared with other model-based simulation schemes, the RL-based energy-efficient driving method improves the accuracy and generalization [4]. On one hand, other models can deal with specific driving scenarios like intersection passing and car following with their different analysis strategies. 


  1. Xu, Z.G.; Wei, T.; Easa, S.; Zhao, X.M.; Qu, X.B. Modeling relationship between truck fuel consumption and driving behavior using data from internet of vehicles. Comput.-Aided Civ. Inf. 2018, 33, 209–219.
  2. Environmental Protection Agency. U.S. Greenhouse Gas Inventory Report: 1990–2014; Environmental Protection Agency: Washington, DC, USA, 2016. Available online: (accessed on 15 January 2024).
  3. Turkensteen, M. The accuracy of carbon emission and fuel consumption computations in green vehicle routing. Eur. J. Oper. Res. 2017, 262, 647–659.
  4. Huang, Y.H.; Ng, E.C.Y.; Zhou, J.L.; Surawski, N.C.; Chan, E.F.C.; Hong, G. Eco-driving technology for sustainable road transport: A review. Renew. Sust. Energy Rev. 2018, 93, 596–609.
  5. Sivak, M.; Schoettle, B. Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy. Transp. Policy 2012, 22, 96–99.
  6. Wang, J.H.; Elbery, A.; Rakha, H.A. A real-time vehicle-specific eco-routing model for on-board navigation applications capturing transient vehicle behavior. Transp. Res. C Emerg. Technol. 2019, 104, 1–21.
  7. Lu, H.L.; Chen, T.; Xie, H.; Song, K. Eco-driving at signalized intersections based on driving behavior self-learning. IFAC PapersOnline 2018, 51, 337–342.
  8. Chen, M.C.; Yeh, C.T.; Wang, Y.S. Eco-driving for urban bus with big data analytics. J. Ambient. Intell. Humaniz. Comput. 2020.
  9. Fafoutellis, P.; Mantouka, E.G.; Vlahogianni, E.I. Eco-Driving and its impacts on fuel efficiency: An overview of technologies and data-driven methods. Sustainability 2021, 13, 226.
  10. Julio-Rodriguez, J.D.; Rojas-Ruiz, C.A.; Santana-Diaz, A.; Bustamante-Bello, M.R.; Ramirez-Mendoza, R.A. Environment classification using machine learning methods for eco-driving strategies in intelligent vehicles. Appl. Sci. 2022, 12, 5578.
  11. Yen, M.H.; Tian, S.L.; Lin, Y.T.; Yang, C.W.; Chen, C.C. Combining a universal OBD-II module with deep learning to develop an eco-driving analysis system. Appl. Sci. 2021, 11, 4481.
  12. Bai, Z.W.; Hao, P.; Shangguan, W.; Cai, B.G.; Barth, M.J. Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections. IEEE Trans. Intell. Transp. Syst. 2022, 23, 15850–15863.
  13. Jakobsen, K.; Mouritsen, S.C.H.; Torp, K. Evaluating eco-driving advice using GPS/CANBus data. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, USA, 5–8 November 2013; pp. 44–53.
  14. Huang, Y.Q.; Zhu, L.N.; Sun, R.; Yi, J.J.; Liu, L.; Luan, T.H. Save or waste: Real data based energy-efficient driving. IEEE Access 2020, 8, 133936–133950.
  15. Andrieu, C.; Pierre, G.S. Using statistical models to characterize eco-driving style with an aggregated indicator. In Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, Alcalá de Henares, Spain, 3–7 June 2012; pp. 63–68.
  16. Barkenbus, J.N. Eco-driving: An overlooked climate change initiative. Energy Policy 2010, 38, 762–769.
  17. Li, J.; Liu, Y.G.; Zhang, Y.J.; Lei, Z.Z.; Chen, Z.; Li, G. Data-driven based eco-driving control for plug-in hybrid electric vehicles. J. Power Sources 2021, 498, 229916.
  18. Lukas, B.; Patrick, B.; Leon, S.; Markus, K. Enhanced efficiency prediction of an electrified off-highway vehicle transmission utilizing machine learning methods. Procedia Comput. Sci. 2021, 192, 417–426.
  19. Bozorgi, A.M.; Farasat, M.; Mahmoud, A. A time and energy efficient routing algorithm for electric vehicles based on historical driving data. IEEE Trans. Intell. Veh. 2017, 2, 308–320.
  20. Magana, V.C.; Munoz-Organero, M. Artemisa: A personal driving assistant for fuel saving. IEEE T Mobile Comput. 2016, 15, 2437–2451.
  21. Beloufa, S.; Cauchard, F.; Vedrenne, J.; Vailleau, B.; Kemeny, A.; Merienne, F.; Boucheix, J.M. Learning eco-driving behaviour in a driving simulator: Contribution of instructional videos and interactive guidance system. Transp. Res. F Traffic Psychol. Behav. 2019, 61, 201–216.
  22. Xing, Y.; Lv, C.; Mo, X.; Hu, Z.; Huang, C.; Hang, P. Toward safe and smart mobility: Energy-aware deep learning for driving behavior analysis and prediction of connected vehicles. IEEE Trans. Intell. Transport Sys 2021, 22, 4267–4280.
  23. Bakibillah, A.S.M.; Kamal, M.A.S.; Tan, C.P. Sustainable eco-driving strategy at signalized intersections from driving data. In Proceedings of the 2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020, Chiang Mai, Thailand, 23–26 September 2020; pp. 165–170.
  24. Bakibillah, A.S.M.; Kamal, M.A.S.; Tan, C.P.; Hayakawa, T.; Imura, J.I. Event-Driven Stochastic Eco-Driving Strategy at Signalized Intersections From Self-Driving Data. IEEE Trans. Veh. Technol. 2019, 68, 8557–8569.
  25. Guo, Q.Q.; Angah, O.; Liu, Z.J.; Ban, X.G. Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors. Transp. Res. C Emerg. Technol. 2021, 124, 102980.
  26. Jiang, X.; Zhang, J.; Wang, B. Energy-efficient driving for adaptive traffic signal control environment via explainable reinforcement learning. Appl. Sci. 2022, 12, 5380.
  27. Lee, H.; Kim, N.; Cha, S.W. Model-based reinforcement learning for eco-driving control of electric vehicles. IEEE Access 2020, 8, 202886–202896.
  28. Li, H.Y.; Li, N.; Kolmanovsky, I.; Girard, A. Energy-efficient autonomous vehicle control using reinforcement learning and interactive traffic simulations. In Proceedings of the 2020 American Control Conference (ACC), Denver, CO, USA, 1–3 July 2020; pp. 3029–3034.
  29. Li, J.; Wu, X.D.; Xu, M.; Liu, Y.G. Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections. Energy 2022, 251, 123924.
  30. Liu, B.; Sun, C.; Ren, Q.; Wei, X.; Min, Q.; Liang, B. Adaptive eco-driving of fuel cell vehicles based on multi-light trained deep reinforcement learning. In Proceedings of the 2021 IEEE Vehicle Power and Propulsion Conference (VPPC), Gijon, Spain, 25–28 October 2021; pp. 1–6.
  31. Meng, X.L.; Wang, H.; Lin, M.; Zhou, Y.H. Deep reinforcement learning for energy-efficient train operation of automatic driving. In Proceedings of the 2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China, 20–22 November 2020; pp. 123–126.
  32. Qi, X.; Wu, G.; Boriboonsomsin, K.; Barth, M.J. Data-driven energy efficient driving control in connected vehicle environment. In Data-Driven Solutions to Transportation Problems; Elsevier: Amsterdam, The Netherlands, 2018; pp. 11–49.
  33. Qi, X.W.; Luo, Y.D.; Wu, G.Y.; Boriboonsomsin, K.; Barth, M. Deep reinforcement learning enabled self-learning control for energy efficient driving. Transp. Res. C Emerg. Technol. 2019, 99, 67–81.
  34. Shi, J.Q.; Qiao, F.X.; Li, Q.; Yu, L.; Hu, Y.J. Application and evaluation of the reinforcement learning approach to eco-driving at intersections under infrastructure-to-vehicle communications. Transp. Res. Rec. 2018, 2672, 89–98.
  35. Chang, Y.; Yang, W.Q.; Zhao, D. Energy efficiency and emission testing for connected and automated vehicles using real-world driving data. In Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7 November 2018; pp. 2058–2063.
  36. De Martinis, V.; Gallo, M.; D’Acierno, L. Estimating the benefits of energy-efficient train driving strategies: A model calibration with real data. Urban Transp. XIX 2013, 130, 201–211.
  37. Zhu, Q.; Su, S.; Tang, T.; Liu, W.; Zhang, Z.; Tian, Q. An eco-driving algorithm for trains through distributing energy: A Q-learning approach. ISA Trans. 2022, 122, 24–37.
  38. Hu, K.Z.; Wu, J.P.; Liu, M.Y. Exploring the energy efficiency of electric vehicles with driving behavioral data from a field test and questionnaire. J. Adv. Transp. 2018, 2018, 1074817.
  39. Rettore, P.H.L.; Campolina, A.B.; Villas, L.A.; Loureiro, A.A.F. A method of eco-driving based on intra-vehicular sensor data. In Proceedings of the 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece, 3–6 July 2017; pp. 1122–1127.
  40. Opila, D.F.; Wang, X.; McGee, R.; Cook, J.A.; Grizzle, J.W. Performance comparison of hybrid vehicle energy management controllers on real-world drive cycle data. In Proceedings of the 2009 Conference on American Control Conference, St. Louis, MO, USA, 10–12 June 2009; pp. 4618–4625.
  41. Guo, C.J.; Yang, B.; Andersen, O.; Jensen, C.S.; Torp, K. EcoMark 2.0: Empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data. Geoinformatica 2015, 19, 567–599.
  42. Ma, H.J.; Xie, H.; Brown, D. Eco-driving assistance system for a manual transmission bus based on machine learning. IEEE Trans. Intell. Transp. Syst. 2018, 19, 572–581.
  43. Hsu, C.Y.; Lim, S.S.; Yang, C.S. Data mining for enhanced driving effectiveness: An eco-driving behaviour analysis model for better driving decisions. Int. J. Prod. Res. 2017, 55, 7096–7109.
  44. Kim, M.J.; Lim, C.H.; Lee, C.H.; Kim, K.J.; Park, Y.; Choi, S. Approach to service design based on customer behavior data: A case study on eco-driving service design using bus drivers’ behavior data. Serv. Bus. 2018, 12, 203–227.
  45. Amini, M.R.; Feng, Y.H.; Yang, Z.; Kolmanovsky, I.; Sun, J. Long-term vehicle speed prediction via historical traffic data analysis for improved energy efficiency of connected electric vehicles. Transp. Res. Rec. 2020, 2674, 17–29.
  46. Prendinger, H.; Oliveira, J.; Catarino, J.; Madruga, M.; Prada, R. iCO(2): A networked game for collecting large-scale eco-driving behavior data. IEEE Internet Comput. 2014, 18, 28–35.
  47. Evin, E.; Aydin, M.B.; Kardas, G. Design and implementation of a CANBus-based eco-driving system for public transport bus services. IEEE Access 2020, 8, 8114–8128.
  48. Hermawan, G.; Husni, E. Acquisition, modeling, and evaluating method of driving behavior based on OBD-II: A literature survey. In IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2020; Volume 879, p. 012030.
  49. Vlahogianni, E.I.; Barmpounakis, E.N. Driving analytics using smartphones: Algorithms, comparisons and challenges. Transp. Res. C Emerg. Technol. 2017, 79, 196–206.
  50. Suzdaleva, E.; Nagy, I. Data-based speed-limit-respecting eco-driving system. Transp. Res. C Emerg. Technol. 2014, 44, 253–264.
  51. Xi, G.H.; Zhao, X.B.; Liu, Y.; Huang, J.; Deng, Y.D. A hierarchical ensemble learning framework for energy-efficient automatic train driving. Tsinghua Sci. Technol. 2019, 24, 226–237.
  52. Esser, A.; Eichenlaub, T.; Schleiffer, J.E.; Jardin, P.; Rinderknecht, S. Comparative evaluation of powertrain concepts through an eco-impact optimization framework with real driving data. Optim. Eng. 2021, 22, 1001–1029.
  53. Vanting, N.B.; Ma, Z.; Jørgensen, B.N. A scoping review of deep neural networks for electric load forecasting. Energy Inform. 2021, 4, 49.
  54. Zhou, M.; Jin, H.; Wang, W.S. A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing. Transp. Res. D Transp. Environ. 2016, 49, 203–218.
  55. Kaelbling, L.P.; Littman, M.L.; Moore, A.W. Reinforcement learning: A survey. J. Artif. Intell. Res. 1996, 4, 237–285.
  56. Arulkumaran, K.; Deisenroth, M.P.; Brundage, M.; Bharath, A.A. Deep reinforcement learning: A brief survey. IEEE Signal Process Mag. 2017, 34, 26–38.
  57. Cao, X.R. A sensitivity view of Markov decision processes and reinforcement learning. In Modeling, Control and Optimization of Complex Systems: In Honor of Professor Yu-Chi Ho; Springer: Berlin/Heidelberg, Germany, 2003; Volume 14, pp. 261–283.
Subjects: Transportation
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , ,
View Times: 43
Revisions: 2 times (View History)
Update Date: 19 Mar 2024