IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v94y2016icp194-204.html
   My bibliography  Save this article

Can feedback from in-vehicle data recorders improve driver behavior and reduce fuel consumption?

Author

Listed:
  • Toledo, Galit
  • Shiftan, Yoram

Abstract

This paper evaluates the effectiveness of feedback, based on In-Vehicle Data Recorders (IVDR), to improve driving behavior, increase driving safety, and reduce fuel consumption. We developed a framework for driving-behavior measurement, incorporating second-by-second data collected by IVDRs. IVDR units were installed in over 150 vehicles driven by more than 350 drivers for over a year. The experiment was divided into three stages. The first stage was a “blind”, control stage, with no feedback. The second stage incorporated verbal feedback given only to riskiest drivers. In the third stage all drivers received a bi-weekly written report about their driving performance. Safety events, such as braking, lateral acceleration or speeding, were recorded. Supplementary data regarding safety related events and fuel consumption were also collected. Safety incidents and fuel consumption were modeled as a function of IVDR measurement-based events, in order to identify which events best reflect safety incidents and excessive fuel consumption. Our results show that braking events best explain safety incidents, and all events together best explain fuel consumption. In addition, we found that for the riskiest drivers, feedback significantly reduced the IVDR events. Our models show that feedback can lead to a reduction of 8% in safety incidents, and 3–10% in fuel consumption, with a larger reduction obtained for large vehicles.

Suggested Citation

  • Toledo, Galit & Shiftan, Yoram, 2016. "Can feedback from in-vehicle data recorders improve driver behavior and reduce fuel consumption?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 194-204.
  • Handle: RePEc:eee:transa:v:94:y:2016:i:c:p:194-204
    DOI: 10.1016/j.tra.2016.09.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096585641630773X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2016.09.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Boriboonsomsin, Kanok & Vu, Alexander & Barth, Matthew, 2010. "Eco-Driving: Pilot Evaluation of Driving Behavior Changes Among U.S. Drivers," University of California Transportation Center, Working Papers qt9z18z7xq, University of California Transportation Center.
    2. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    3. Carney, C. & McGehee, D.V. & Lee, J.D. & Reyes, M.L. & Raby, M., 2010. "Using an event-triggered video intervention system to expand the supervised learning of newly licensed adolescent drivers," American Journal of Public Health, American Public Health Association, vol. 100(6), pages 1101-1106.
    4. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    5. Paefgen, Johannes & Staake, Thorsten & Fleisch, Elgar, 2014. "Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 27-40.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Federico Orsini & Mariaelena Tagliabue & Giulia De Cet & Massimiliano Gastaldi & Riccardo Rossi, 2021. "Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    2. Hoffmann, Christin & Thommes, Kirsten, 2024. "Can leaders motivate employees’ energy-efficient behavior with thoughtful communication?," Journal of Environmental Economics and Management, Elsevier, vol. 125(C).
    3. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    4. Ran Tu & Junshi Xu & Tiezhu Li & Haibo Chen, 2022. "Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review," IJERPH, MDPI, vol. 19(12), pages 1-14, June.
    5. Javier Goikoetxea Gonzalez & Diego Casado-Mansilla & Diego López-de-Ipiña, 2020. "Analysis of Driver’s Reaction Behavior Using a Persuasion-Based IT Artefact," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    6. Jian Gong & Junzhu Shang & Lei Li & Changjian Zhang & Jie He & Jinhang Ma, 2021. "A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors," Energies, MDPI, vol. 14(23), pages 1-18, December.
    7. Yunshun Zhang & Qishuai Xie & Minglei Gao & Yuchen Guo, 2023. "The Impact of In-Vehicle Traffic Lights on Driving Characteristics in the Presence of Obstructed Line-of-Sight," Sustainability, MDPI, vol. 15(10), pages 1-26, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carlos-Alberto Domínguez-Báez & Ricardo Mendoza-González & Huizilopoztli Luna-García & Mario Alberto Rodríguez-Díaz & Francisco Javier Luna-Rosas & Julio César Martínez-Romo & José M. Celaya-Padilla &, 2021. "A Methodological Process for the Design of Frameworks Oriented to Infotainment User Interfaces," Sustainability, MDPI, vol. 13(11), pages 1-14, May.
    2. Sanguinetti, Angela, 2018. "Onboard Feedback to Promote Eco-Driving: Average Impact and Important Features," Institute of Transportation Studies, Working Paper Series qt99m5j3q7, Institute of Transportation Studies, UC Davis.
    3. Stillwater, Tai & Kurani, Kenneth S., 2012. "Goal Setting, Framing, and Anchoring Responses to Ecodriving Feedback," Institute of Transportation Studies, Working Paper Series qt9k86f889, Institute of Transportation Studies, UC Davis.
    4. Stillwater, Tai & Kurani, Kenneth S., 2012. "Preliminary Results from a Field Experiment of Three Fuel Economy Feedback Designs," Institute of Transportation Studies, Working Paper Series qt11r5b3cs, Institute of Transportation Studies, UC Davis.
    5. Barla, Philippe & Gilbert-Gonthier, Mathieu & Lopez Castro, Marco Antonio & Miranda-Moreno, Luis, 2017. "Eco-driving training and fuel consumption: Impact, heterogeneity and sustainability," Energy Economics, Elsevier, vol. 62(C), pages 187-194.
    6. Hsu, Chia-Yu & Yang, Chin-Sheng & Yu, Liang-Chih & Lin, Chi-Fang & Yao, Hsiu-Hsen & Chen, Duan-Yu & Robert Lai, K. & Chang, Pei-Chann, 2015. "Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system," International Journal of Production Economics, Elsevier, vol. 164(C), pages 454-461.
    7. Sanguinetti, Angela & Queen, Ella & Yee, Christopher & Akanesuvan, Kantapon, 2020. "Average impact and important features of onboard eco-driving feedback: A meta-analysis," Institute of Transportation Studies, Working Paper Series qt9hm406d5, Institute of Transportation Studies, UC Davis.
    8. Najaf, Pooya & Thill, Jean-Claude & Zhang, Wenjia & Fields, Milton Greg, 2018. "City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 257-270.
    9. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    10. Khondoker Billah & Qasim Adegbite & Hatim O. Sharif & Samer Dessouky & Lauren Simcic, 2021. "Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017," Sustainability, MDPI, vol. 13(9), pages 1-18, May.
    11. Ahmed, Sumayyah & Sanguinetti, Angela, 2015. "OBDEnergy: Making Metrics Meaningful in Eco-driving Feedback," Institute of Transportation Studies, Working Paper Series qt0x73t2jw, Institute of Transportation Studies, UC Davis.
    12. Pietro Stabile & Federico Ballo & Giorgio Previati & Giampiero Mastinu & Massimiliano Gobbi, 2023. "Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios," Energies, MDPI, vol. 16(3), pages 1-19, January.
    13. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    14. Yuan, Weichang & Frey, H. Christopher, 2020. "Potential for metro rail energy savings and emissions reduction via eco-driving," Applied Energy, Elsevier, vol. 268(C).
    15. Bo Yang & Yao Wu & Weihua Zhang & Jie Bao, 2020. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    16. Bae, Bumjoon & Seo, Changbeom, 2022. "Do public-private partnerships help improve road safety? Finding empirical evidence using panel data models," Transport Policy, Elsevier, vol. 126(C), pages 336-342.
    17. Svetlana BAČKALIĆ & Dragan JOVANOVIĆ & Todor BAČKALIĆ & Boško MATOVIĆ & Miloš PLJAKIĆ, 2019. "The Application Of Reliability Reallocation Model In Traffic Safety Analysis On Rural Roads," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 14(1), pages 115-125, April.
    18. Izdebski, Mariusz & Jacyna-Gołda, Ilona & Gołda, Paweł, 2022. "Minimisation of the probability of serious road accidents in the transport of dangerous goods," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    19. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    20. Renfei Wu & Xunjia Zheng & Yongneng Xu & Wei Wu & Guopeng Li & Qing Xu & Zhuming Nie, 2019. "Modified Driving Safety Field Based on Trajectory Prediction Model for Pedestrian–Vehicle Collision," Sustainability, MDPI, vol. 11(22), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:94:y:2016:i:c:p:194-204. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.