IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9089-d614022.html
   My bibliography  Save this article

Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior

Author

Listed:
  • Federico Orsini

    (Department of Civil, Environmental and Architectural Engineering, University of Padua, 35131 Padua, Italy
    Mobility and Behavior Research Center—MoBe, University of Padua, 35131 Padua, Italy)

  • Mariaelena Tagliabue

    (Department of Civil, Environmental and Architectural Engineering, University of Padua, 35131 Padua, Italy
    Mobility and Behavior Research Center—MoBe, University of Padua, 35131 Padua, Italy
    Department of General Psychology, University of Padua, 35131 Padua, Italy)

  • Giulia De Cet

    (Department of Civil, Environmental and Architectural Engineering, University of Padua, 35131 Padua, Italy
    Mobility and Behavior Research Center—MoBe, University of Padua, 35131 Padua, Italy)

  • Massimiliano Gastaldi

    (Department of Civil, Environmental and Architectural Engineering, University of Padua, 35131 Padua, Italy
    Mobility and Behavior Research Center—MoBe, University of Padua, 35131 Padua, Italy
    Department of General Psychology, University of Padua, 35131 Padua, Italy)

  • Riccardo Rossi

    (Department of Civil, Environmental and Architectural Engineering, University of Padua, 35131 Padua, Italy
    Mobility and Behavior Research Center—MoBe, University of Padua, 35131 Padua, Italy)

Abstract

Real-time coaching programs are designed to give feedback on driving behavior to usage-based motor insurance users; they are often general purpose programs that aim to promote smooth driving. Here, we investigated the effect of different on-board real-time coaching programs on the driving behavior on highway deceleration lanes with a driving simulator experiment. The experiment was organized into two trials. The first was a baseline trial, in which participants drove without receiving any feedback; a cluster analysis was then performed to divide participants into two groups, based on their observed driving style. One month later, a second trial was carried out, with participants driving on the same path as the first trial, this time receiving contingent feedback related to their braking/acceleration behavior. Four feedback systems were tested; overall, there were eight experimental groups, depending on the clustered driving style (aggressive and defensive), feedback modality (visual and auditory), and feedback valence (positive and negative). Speed, deceleration, trajectory, and lateral control variables, collected before and onto the deceleration lane, were investigated with mixed ANOVAs, which showed that the real-time coaching programs significantly reduced speeds and maximum deceleration values, while improving lateral control. A change toward a safer exit strategy (i.e., entering the lane before starting to decelerate) was also observed in defensive drivers.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9089-:d:614022
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9089/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/16/9089/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ryder, Benjamin & Dahlinger, Andre & Gahr, Bernhard & Zundritsch, Peter & Wortmann, Felix & Fleisch, Elgar, 2019. "Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 611-626.
    3. Tscharaktschiew, Stefan, 2016. "The private (unnoticed) welfare cost of highway speeding behavior from time saving misperceptions," Economics of Transportation, Elsevier, vol. 7, pages 24-37.
    4. Miremad Soleymanian & Charles B. Weinberg & Ting Zhu, 2019. "Sensor Data and Behavioral Tracking: Does Usage-Based Auto Insurance Benefit Drivers?," Marketing Science, INFORMS, vol. 38(1), pages 21-43, January.
    5. Huang, Yuhan & Ng, Elvin C.Y. & Zhou, John L. & Surawski, Nic C. & Chan, Edward F.C. & Hong, Guang, 2018. "Eco-driving technology for sustainable road transport: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 596-609.
    6. Giuseppina Pappalardo & Salvatore Cafiso & Alessandro Di Graziano & Alessandro Severino, 2021. "Decision Tree Method to Analyze the Performance of Lane Support Systems," Sustainability, MDPI, vol. 13(2), pages 1-13, January.
    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. Xin Tian & Mengmeng Shi & Mengyu Shao & Binghong Pan, 2023. "Calculation Method of Deceleration Lane Length and Slope Based on Reliability Theory," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    2. Maria Rodionova & Angi Skhvediani & Tatiana Kudryavtseva, 2022. "Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    3. Yong Fang & Jiayi Zhou & Hua Hu & Yanxi Hao & Dianliang Xiao & Shaojie Li, 2022. "Combination Layout of Traffic Signs and Markings of Expressway Tunnel Entrance Sections: A Driving Simulator Study," Sustainability, MDPI, vol. 14(6), pages 1-13, March.

    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. 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.
    2. Saeed Vasebi & Yeganeh M. Hayeri, 2021. "Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    3. Santos, Alberto & Maia, Pedro & Jacob, Rodrigo & Wei, Huang & Callegari, Camila & Oliveira Fiorini, Ana Carolina & Schaeffer, Roberto & Szklo, Alexandre, 2024. "Road conditions and driving patterns on fuel usage: Lessons from an emerging economy," Energy, Elsevier, vol. 295(C).
    4. Tscharaktschiew, Stefan & Reimann, Felix, 2021. "On employer-paid parking and parking (cash-out) policy: A formal synthesis of different perspectives," Transport Policy, Elsevier, vol. 110(C), pages 499-516.
    5. Li, Menglin & Yin, Long & Yan, Mei & Wu, Jingda & He, Hongwe & Jia, Chunchun, 2024. "Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions," Energy, Elsevier, vol. 304(C).
    6. Anindya Ghose & Beibei Li & Meghanath Macha & Chenshuo Sun & Natasha Ying Zhang Foutz, 2020. "Trading Privacy for the Greater Social Good: How Did America React During COVID-19?," Papers 2006.05859, arXiv.org.
    7. Jaller, Miguel & Pahwa, Anmol & Zhang, Michael, 2021. "Cargo Routing and Disadvantaged Communities," Institute of Transportation Studies, Working Paper Series qt9qg2318x, Institute of Transportation Studies, UC Davis.
    8. 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).
    9. 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).
    10. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    11. Yang Wang & Alessandra Boggio-Marzet, 2018. "Evaluation of Eco-Driving Training for Fuel Efficiency and Emissions Reduction According to Road Type," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    12. Robaina, Margarita & Neves, Ana, 2021. "Complete decomposition analysis of CO2 emissions intensity in the transport sector in Europe," Research in Transportation Economics, Elsevier, vol. 90(C).
    13. 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.
    14. Wojciech Adamski & Krzysztof Brzozowski & Jacek Nowakowski & Tomasz Praszkiewicz & Tomasz Knefel, 2021. "Excess Fuel Consumption Due to Selection of a Lower Than Optimal Gear—Case Study Based on Data Obtained in Real Traffic Conditions," Energies, MDPI, vol. 14(23), pages 1-15, November.
    15. Panagiotis Fafoutellis & Eleni G. Mantouka & Eleni I. Vlahogianni, 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    16. Juan Francisco Coloma & Marta García & Gonzalo Fernández & Andrés Monzón, 2021. "Environmental Effects of Eco-Driving on Courier Delivery," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    17. Ola Svenson & Nichel Gonzalez & Gabriella Eriksson, 2018. "Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rules," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(5), pages 401-412, September.
    18. Huang, Yuhan & Surawski, Nic C. & Zhuang, Yuan & Zhou, John L. & Hong, Guang, 2021. "Dual injection: An effective and efficient technology to use renewable fuels in spark ignition engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    19. Evangelinos, Christos & Tscharaktschiew, Stefan & Marcucci, Edoardo & Gatta, Valerio, 2018. "Pricing workplace parking via cash-out: Effects on modal choice and implications for transport policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 369-380.
    20. Jing Wang & Chenhao Zhao & Zhixia Liu, 2024. "Can Historical Accident Data Improve Sustainable Urban Traffic Safety? A Predictive Modeling Study," Sustainability, MDPI, vol. 16(22), pages 1-24, 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:gam:jsusta:v:13:y:2021:i:16:p:9089-:d:614022. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.