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Neural Computing and Applications

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10.03.2021 | Original Article

Evaluation of professional driver’s eco-driving skills based on type-2 fuzzy logic model

verfasst von: Stefan Zdravković, Davor Vujanović, Marko Stokić, Dragan Pamučar

Erschienen in: Neural Computing and Applications | Ausgabe 18/2021

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Abstract

Due to the great market competition, transport companies face the need to reduce their vehicle fleet costs. The vehicle fleet managers’ actions on the driver’s driving style to achieve fuel consumption savings are measures to increase the fleet’s energy efficiency. The authors developed a model for evaluating driving style using a type-2 fuzzy logic system. The model comprehensively considers three parameters: engine speed, accelerator pedal position, and acceleration/deceleration. These parameters can precisely describe the driving style and additionally have a strong influence on fuel consumption. The model output is the driver’s score, representing the influence of driving style to fuel consumption. The model is tested in the company whose drivers have attended the eco-driving training course. Each driver’s driving style was monitored for 15 days to obtain trustworthy assessments regarding driving style. The result was twofold: firstly, we point out the importance of simultaneous observation of all three defined parameters to get reliable driver’s score in terms of driving style, and secondly, it is established that drivers have significantly different driving styles regardless of whether they have attended the same eco-driving training. The established differences in driving styles have a direct impact on the obtained differences in fuel consumption among drivers. The proposed model can significantly reduce fuel consumption depending on the driving style and increase the vehicle fleet’s energy efficiency.

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Eurostat (2019) Freight transport statistics—modal split: tables and figures. Eurostat, Luxembourg

Kot S (2015) Cost structure in relation to the size of road transport enterprises. PROMET Traffic Transp 27:387–394. https://doi.org/10.7307/ptt.v27i5.1687CrossRef

Kovács G (2017) Optimisation method and software for fuel cost reduction in case of road transport activity. Acta Polytech 57:201–208. https://doi.org/10.14311/AP.2017.57.0201CrossRef

Gohari A, Matori AN, Yusof KW et al (2018) The effect of fuel price increase on transport cost of container transport vehicles. Int J GEOMATE 15:174–181. https://doi.org/10.21660/2018.50.30814CrossRef

Schall DL, Wolf M, Mohnen A (2016) Do effects of theoretical training and rewards for energy-efficient behavior persist over time and interact? A natural field experiment on eco-driving in a company fleet. Energy Policy 97:291–300. https://doi.org/10.1016/j.enpol.2016.07.008CrossRef

Beloufa S, Cauchard F, Vedrenne J et al (2019) Learning eco-driving behaviour in a driving simulator: contribution of instructional videos and interactive guidance system. Transp Res Part F Traffic Psychol Behav 61:201–216. https://doi.org/10.1016/j.trf.2017.11.010CrossRef

Huang Y, Ng ECY, Zhou JL et al (2018) Eco-driving technology for sustainable road transport: a review. Renew Sustain Energy Rev 93:596–609. https://doi.org/10.1016/j.rser.2018.05.030CrossRef

Díaz-Ramirez J, Giraldo-Peralta N, Flórez-Ceron D et al (2017) Eco-driving key factors that influence fuel consumption in heavy-truck fleets: a Colombian case. Transp Res Part D Transp Environ 56:258–270. https://doi.org/10.1016/j.trd.2017.08.012CrossRef

Ho SH, Wong YD, Chang VWC (2015) What can eco-driving do for sustainable road transport? Perspectives from a city (Singapore) eco-driving programme. Sustain Cities Soc 14:82–88. https://doi.org/10.1016/j.scs.2014.08.002CrossRef

10.

Fors C, Kircher K, Ahlström C (2015) Interface design of eco-driving support systems—truck drivers’ preferences and behavioural compliance. Transp Res Part C Emerg Technol 58:706–720. https://doi.org/10.1016/j.trc.2015.03.035CrossRef

11.

Pozueco L, Pañeda XG, Tuero AG et al (2017) A methodology to evaluate driving efficiency for professional drivers based on a maturity model. Transp Res Part C Emerg Technol 85:148–167. https://doi.org/10.1016/j.trc.2017.09.017CrossRef

12.

Goes G, Bandeira R, Gonçalves D et al (2019) The effect of eco-driving initiatives toward sustainable urban waste collection. Int J Sustain Transp 14:1–10. https://doi.org/10.1080/15568318.2019.1584933CrossRef

13.

Xu Y, Li H, Liu H et al (2017) Eco-driving for transit: an effective strategy to conserve fuel and emissions. Appl Energy 194:784–797. https://doi.org/10.1016/j.apenergy.2016.09.101CrossRef

14.

Stillwater T, Kurani KS, Mokhtarian PL (2017) The combined effects of driver attitudes and in-vehicle feedback on fuel economy. Transp Res Part D Transp Environ 52:277–288. https://doi.org/10.1016/j.trd.2017.02.013CrossRef

15.

Yao Y, Zhao X, Ma J et al (2019) Driving simulator study: eco-driving training system based on individual characteristics. Transp Res Rec 2673:463–476. https://doi.org/10.1177/0361198119843260CrossRef

16.

Brand C, Anable J, Morton C (2019) Lifestyle, efficiency and limits: modelling transport energy and emissions using a socio-technical approach. Energy Effic 12:187–207. https://doi.org/10.1007/s12053-018-9678-9CrossRef

17.

Pampel SM, Jamson SL, Hibberd DL, Barnard Y (2018) Old habits die hard? The fragility of eco-driving mental models and why green driving behaviour is difficult to sustain. Transp Res Part F Traffic Psychol Behav 57:139–150. https://doi.org/10.1016/j.trf.2018.01.005CrossRef

18.

Silva Cruz I, Katz-Gerro T (2016) Urban public transport companies and strategies to promote sustainable consumption practices. J Clean Prod 123:28–33. https://doi.org/10.1016/j.jclepro.2015.12.007CrossRef

19.

Schall DL, Mohnen A (2017) Incentivising energy-efficient behavior at work: an empirical investigation using a natural field experiment on eco-driving. Appl Energy 185:1757–1768. https://doi.org/10.1016/j.apenergy.2015.10.163CrossRef

20.

Zang J, Song G, Wu Y, Yu L (2019) Method for evaluating eco-driving behaviors based on vehicle specific power distributions. Transp Res Rec 2673:1–11. https://doi.org/10.1177/0361198119853561CrossRef

21.

Delgado OF, Clark NN, Thompson GJ (2011) Modeling transit bus fuel consumption on the basis of cycle properties. J Air Waste Manag Assoc 61:443–452. https://doi.org/10.3155/1047-3289.61.4.443CrossRef

22.

Strömberg HK, Karlsson ICMA (2013) Comparative effects of eco-driving initiatives aimed at urban bus drivers—results from a field trial. Transp Res Part D Transp Environ 22:28–33. https://doi.org/10.1016/j.trd.2013.02.011CrossRef

23.

Stokic M, Momcilovic V, Vujanovic D (2019) Evaluation of driver’s eco-driving skills based on fuzzy logic model—a realistic example of vehicle operation in real-world conditions. J Appl Eng Sci 17:217–223. https://doi.org/10.5937/jaes17-22106CrossRef

24.

Eboli L, Mazzulla G, Pungillo G (2017) How drivers’ characteristics can affect driving style. Transp Res Procedia 27:945–952. https://doi.org/10.1016/j.trpro.2017.12.024CrossRef

25.

de Abreu e Silva J, Moura F, Garcia B, Vargas R (2015) Influential vectors in fuel consumption by an urban bus operator: bus route, driver behavior or vehicle type? Transp Res Part D Transp Environ 38:94–104. https://doi.org/10.1016/j.trd.2015.04.003CrossRef

26.

Magana VC, Munoz-Organero M (2016) Artemisa: a personal driving assistant for fuel saving. IEEE Trans Mob Comput 15:2437–2451. https://doi.org/10.1109/TMC.2015.2504976CrossRef

27.

Gilman E, Keskinarkaus A, Tamminen S et al (2015) Personalised assistance for fuel-efficient driving. Transp Res Part C Emerg Technol 58:681–705. https://doi.org/10.1016/j.trc.2015.02.007CrossRef

28.

Eftekhari HR, Ghatee M (2018) Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition. Transp Res Part F Traffic Psychol Behav 58:782–796. https://doi.org/10.1016/j.trf.2018.06.044CrossRef

29.

Neumann T (2018) The importance of telematics in the transport system. TransNav Int J Mar Navig Saf Sea Transp 12:617–623. https://doi.org/10.12716/1001.12.03.22CrossRef

30.

Said H, Nicoletti T, Perez-Hernandez P (2016) Utilising telematics data to support effective equipment fleet-management decisions: utilisation rate and hazard functions. J Comput Civ Eng 30:04014122. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000444CrossRef

31.

Andrieu C, Saint PG (2012) Comparing effects of eco-driving training and simple advices on driving behavior. Procedia Soc Behav Sci 54:211–220. https://doi.org/10.1016/j.sbspro.2012.09.740CrossRef

32.

Coloma JF, García M, Wang Y (2018) Eco-driving effects depending on the travelled road. correlation between fuel consumption parameters. Transp Res Procedia 33:259–266. https://doi.org/10.1016/j.trpro.2018.10.101CrossRef

33.

Orfila O, Saint Pierre G, Messias M (2015) An android based ecodriving assistance system to improve safety and efficiency of internal combustion engine passenger cars. Transp Res Part C Emerg Technol 58:772–782. https://doi.org/10.1016/j.trc.2015.04.026CrossRef

34.

Rolim C, Baptista P, Duarte G et al (2017) Real-time feedback impacts on eco-driving behavior and influential variables in fuel consumption in a Lisbon urban bus operator. IEEE Trans Intell Transp Syst 18:3061–3071. https://doi.org/10.1109/TITS.2017.2657333CrossRef

35.

Basarić VB, Jambrović M, Miličić MB et al (2017) Positive effects of eco-driving in public transport: a case study of the city Novi Sad. Therm Sci 21:683–692. https://doi.org/10.2298/TSCI150219160BCrossRef

36.

Beusen B, Broekx S, Denys T et al (2009) Using on-board logging devices to study the longer-term impact of an eco-driving course. Transp Res Part D Transp Environ 14:514–520. https://doi.org/10.1016/j.trd.2009.05.009CrossRef

37.

Ma H, Xie H, Huang D, Xiong S (2015) Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses. Transp Res Part D Transp Environ 41:205–216. https://doi.org/10.1016/j.trd.2015.10.003CrossRef

38.

Jeffreys I, Graves G, Roth M (2018) Evaluation of eco-driving training for vehicle fuel use and emission reduction: a case study in Australia. Transp Res Part D Transp Environ 60:85–91. https://doi.org/10.1016/j.trd.2015.12.017CrossRef

39.

Nègre J, Delhomme P (2017) Drivers’ self-perceptions about being an eco-driver according to their concern for the environment, beliefs on eco-driving, and driving behavior. Transp Res Part A Policy Pract 105:95–105. https://doi.org/10.1016/j.tra.2017.08.014CrossRef

40.

Berry IM (2010) The effects of driving style and vehicle performance on the real-world fuel consumption of U.S. light-duty vehicles. Master's thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts

41.

Araújo R, Igreja Â, De Castro R, Araújo RE (2012) Driving coach: a smartphone application to evaluate driving efficient patterns. IEEE Intell Veh Symp Proc 1:1005–1010. https://doi.org/10.1109/IVS.2012.6232304CrossRef

42.

Zhou M, Jin H (2017) Development of a transient fuel consumption model. Transp Res Part D Transp Environ 51:82–93. https://doi.org/10.1016/j.trd.2016.12.001CrossRef

43.

Pañeda XG, Garcia R, Diaz G et al (2016) Formal characterisation of an efficient driving evaluation process for companies of the transport sector. Transp Res Part A Policy Pract 94:431–445. https://doi.org/10.1016/j.tra.2016.10.004CrossRef

44.

Hajek H, Popiv D, Just M, Bengler K (2011) Influence of a multimodal assistance supporting anticipatory driving on the driving behavior and driver’s acceptance. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics). LNCS, vol 6776, pp 217–226. https://doi.org/10.1007/978-3-642-21753-1_25

45.

Kim JS, Tak TO (2016) Design and performance validation of tactile force generating type eco-pedal to improve fuel economy. Trans Korean Soc Mech Eng A 40:963–970. https://doi.org/10.3795/KSME-A.2016.40.11.963CrossRef

46.

Baldean D-L (2019) Research of fuel injection duty on a spark ignited engine. Appl Math Mech Eng 62:577–582

47.

Sanguinetti A, Kurani K, Davies J (2017) The many reasons your mileage may vary: toward a unifying typology of eco-driving behaviors. Transp Res Part D Transp Environ 52:73–84. https://doi.org/10.1016/j.trd.2017.02.005CrossRef

48.

Birrell S, Taylor J, McGordon A et al (2014) Analysis of three independent real-world driving studies: a data driven and expert analysis approach to determining parameters affecting fuel economy. Transp Res Part D Transp Environ 33:74–86. https://doi.org/10.1016/j.trd.2014.08.021CrossRef

49.

Suzdaleva E, Nagy I (2018) An online estimation of driving style using data-dependent pointer model. Transp Res Part C Emerg Technol 86:23–36. https://doi.org/10.1016/j.trc.2017.11.001CrossRef

50.

Barla P, Gilbert-Gonthier M, Lopez Castro MA, Miranda-Moreno L (2017) Eco-driving training and fuel consumption: impact, heterogeneity and sustainability. Energy Econ 62:187–194. https://doi.org/10.1016/j.eneco.2016.12.018CrossRef

51.

Liang Q, Mendel JM (2000) Interval type-2 fuzzy logic systems: theory and design. IEEE Trans Fuzzy Syst 8:535–550. https://doi.org/10.1109/91.873577CrossRef

52.

Karnik NN, Mendel JM (2001) Operations on type-2 fuzzy sets. Fuzzy Sets Syst 122:327–348. https://doi.org/10.1016/S0165-0114(00)00079-8MathSciNetCrossRefMATH

53.

Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci (N Y) 8:199–249. https://doi.org/10.1016/0020-0255(75)90036-5MathSciNetCrossRefMATH

54.

Khosravi A, Nahavandi S (2014) Effects of type reduction algorithms on forecasting accuracy of IT2FLS models. Appl Soft Comput J 17:32–38. https://doi.org/10.1016/j.asoc.2013.12.007CrossRef

55.

Wu D, Mendel JM (2007) Uncertainty measures for interval type-2 fuzzy sets. Inf Sci (N Y) 177:5378–5393. https://doi.org/10.1016/j.ins.2007.07.012MathSciNetCrossRefMATH

56.

Yeh CY, Jeng WHR, Lee SJ (2011) Data-based system modeling using a type-2 fuzzy neural network with a hybrid learning algorithm. IEEE Trans Neural Netw 22:2296–2309. https://doi.org/10.1109/TNN.2011.2170095CrossRef

57.

Zhou SM, Garibaldi JM, John RI, Chiclana F (2009) On constructing parsimonious type-2 fuzzy logic systems via influential rule selection. IEEE Trans Fuzzy Syst 17:654–667. https://doi.org/10.1109/TFUZZ.2008.928597CrossRef

58.

Mendel JM (2001) Uncertain rule-based fuzzy logic systems: introduction and new directions, 2nd edn. Prentice-Hall, Upper-Saddle RiverMATH

59.

Precup R-E, Preitl S, Petriu E et al (2020) Model-based fuzzy control results for networked control systems. Rep Mech Eng 1:10–25. https://doi.org/10.31181/rme200101010pCrossRef

60.

Vilela M, Oluyemi G, Petrovski A (2020) A holistic approach to assessment of value of information (VOI) with fuzzy data and decision criteria. Decis Mak Appl Manag Eng 3:97–118. https://doi.org/10.31181/dmame2003097vCrossRef

61.

Vilela M, Oluyemi G, Petrovski A (2019) Fuzzy logic applied to value of information assessment in oil and gas projects. Pet Sci 16:1208–1220. https://doi.org/10.1007/s12182-019-0348-0CrossRef

62.

Cisel Aras A, Gocer I (2016) Driver rating based on interval type-2 fuzzy logic system. IFAC-PapersOnLine 49:95–100. https://doi.org/10.1016/j.ifacol.2016.08.015CrossRef

Titel: Evaluation of professional driver’s eco-driving skills based on type-2 fuzzy logic model
verfasst von: Stefan Zdravković
Davor Vujanović
Marko Stokić
Dragan Pamučar
Publikationsdatum: 10.03.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 18/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-05823-z

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