Post-trip safety interventions: State-of-the-art, challenges, and practical implications.

INTRODUCTION Currently, risky driving behaviour is a major contributor to road crashes and as a result, wide array of tools have been developed in order to record and improve driving behaviour. Within that group of tools, interventions have been indicated to significantly enhance driving behaviour and road safety. This study critically reviews monitoring technologies that provide post-trip interventions, such as retrospective visual feedback, gamification, rewards or penalties, in order to inform an appropriate driver mentoring strategy delivered after each trip. METHOD The work presented here is part of the European Commission H2020 i-DREAMS project. The reviewed platform characteristics were obtained through commercially available solutions as well as a comprehensive literature search in popular scientific databases, such as Scopus and Google Scholar. Focus was given on state-of-the-art-technologies for post-trip interventions utilized in four different transport modes (i.e. car, truck, bus and rail) associated with risk prevention and mitigation. RESULTS The synthesized results revealed that smartphone applications and web-based platforms are the most accepted, frequently and easiest to use tools in cars, buses and trucks across all papers considered, while limited evidence of post-trip interventions in -rail was found. The majority of smartphone applications detected mobile phone use and harsh events and provided individual performance scores, while in-vehicle systems provided delayed visual reports through a web-based platform. CONCLUSIONS Gamification and appropriate rewards appeared to be effective solutions, as it was found that they keep drivers motivated in improving their driving skills, but it was clear that these cannot be performed in isolation and a combination with other strategies (i.e. driver coaching and support) might be beneficial. Nevertheless, as there is no holistic and cross-modal post-trip intervention solution developed in real-world environments, challenges associated with post-trip feedback provision and suggestions on practical implementation are also provided.

[1]  Marthinus J. Booysen,et al.  Combining speed and acceleration to detect reckless driving in the informal public transport industry , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[2]  Mark Stevenson,et al.  The effect of 'smart' financial incentives on driving behaviour of novice drivers. , 2018, Accident; analysis and prevention.

[3]  Fanxing Meng,et al.  Dynamic Vibrotactile Signals for Forward Collision Avoidance Warning Systems , 2015, Hum. Factors.

[4]  Cristy Ho,et al.  Assessing the effectiveness of "intuitive" vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator. , 2006, Accident; analysis and prevention.

[5]  Arne Höltl,et al.  Perceived usefulness of eco-driving assistance systems in Europe , 2012 .

[6]  Witold Bartnik,et al.  Continuous Feedback as a Key Component of Employee Motivation Improvement - A Railway Case Study based on the Placebo Effect , 2017, HICSS.

[7]  Juho Hamari,et al.  Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification , 2014, 2014 47th Hawaii International Conference on System Sciences.

[8]  Rune Elvik Rewarding Safe and Environmentally Sustainable Driving , 2014 .

[9]  Silvio Nocera,et al.  Reducing fuel consumption and carbon emissions through eco-drive training , 2017 .

[10]  Linda Ng Boyle,et al.  Augmenting the Technology Acceptance Model with Trust: Commercial Drivers’ Attitudes towards Monitoring and Feedback , 2012 .

[11]  Hiroaki Ishikawa,et al.  Self-Coaching System Based on Recorded Driving Data: Learning From One's Experiences , 2012, IEEE Transactions on Intelligent Transportation Systems.

[12]  Lennart E. Nacke,et al.  From game design elements to gamefulness: defining "gamification" , 2011, MindTrek.

[13]  Dick de Waard,et al.  The impact of immediate or delayed feedback on driving behaviour in a simulated Pay-As-You-Drive system. , 2015, Accident; analysis and prevention.

[14]  Lisa Dorn,et al.  Eco-driving training of professional bus drivers - Does it work? , 2015 .

[15]  Marius Marcu,et al.  Software Solution for Monitoring and Analyzing Driver Behavior , 2018, 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI).

[16]  Marc Brogle,et al.  Eco-efficient feedback technologies: Which eco-feedback types prefer drivers most? , 2011, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[17]  Sharon Newnam,et al.  Work-related driving safety in light vehicle fleets : a review of past research and the development of an intervention framework , 2011 .

[18]  Orit Taubman – Ben-Ari,et al.  The effects of positive emotion priming on self-reported reckless driving. , 2012 .

[19]  P. Delhomme,et al.  Attitudes des jeunes automobilistes à l’égard des principales actions contre l’insécurité routière en France , 2011 .

[20]  Guillaume Saint Pierre,et al.  An android based ecodriving assistance system to improve safety and efficiency of internal combustion engine passenger cars , 2015 .

[21]  Michelle Scott,et al.  The road code: encouraging more efficient driving practices in New Zealand , 2018 .

[22]  Elgar Fleisch,et al.  Supporting eco-driving with eco-feedback technologies : Recommendations targeted at improving corporate car drivers' intrinsic motivation to drive more sustainable , 2012 .

[23]  Bonaventura H.W. Hadikusumo,et al.  Structural equation model of integrated safety intervention practices affecting the safety behaviour of workers in the construction industry , 2017 .

[24]  L. Molnar,et al.  Older truck drivers: How can we keep them in the workforce for as long as safely possible? , 2020 .

[25]  Ayako Taniguchi,et al.  Reducing family car-use by providing travel advice or requesting behavioral plans: An experimental analysis of travel feedback programs , 2005 .

[26]  Linda Ng Boyle,et al.  Mitigating driver distraction with retrospective and concurrent feedback. , 2008, Accident; analysis and prevention.

[27]  Ryosuke Ando,et al.  A Study on Factors Affecting the Effective Eco-driving , 2012 .

[28]  Tim Horberry,et al.  Driver Acceptance of New Technology: Theory, Measurement and Optimisation , 2017 .

[29]  Advaith Siddharthan,et al.  Creating Textual Driver Feedback from Telemetric Data , 2015, ENLG.

[30]  Pete Underwood Driver acceptance of new technology: theory, measurement and optimisation , 2015, Ergonomics.

[31]  John D Lee,et al.  Using trip diaries to mitigate route risk and risky driving behavior among older drivers. , 2017, Accident; analysis and prevention.

[32]  E. Geller,et al.  Intervening to Improve the Safety of Delivery Drivers , 2000 .

[33]  Chalermpol Saiprasert,et al.  A method for driving event detection using SAX with resource usage exploration on smartphone platform , 2014, EURASIP J. Wirel. Commun. Netw..

[34]  E T Verhoef,et al.  Effects of Pay-As-You-Drive vehicle insurance on young drivers' speed choice: results of a Dutch field experiment. , 2011, Accident; analysis and prevention.

[35]  Alan Tapp,et al.  The effects of feedback and incentive-based insurance on driving behaviours: study approach and protocols , 2017, Injury Prevention.

[36]  In-Vehicle Data Recorder for Evaluation of Driving Behavior and Safety , 2006 .

[37]  Sharon Newnam,et al.  A Participative Education Program to Reduce Speeding In a Group of Work-Related Drivers. , 2009 .

[38]  Boniface Kabaso,et al.  Telematics and Road Safety , 2018, 2018 2nd International Conference on Telematics and Future Generation Networks (TAFGEN).

[39]  J. Schade,et al.  Acceptability of urban transport pricing strategies , 2003 .

[40]  L. Hartling,et al.  Graduated driver licensing for reducing motor vehicle crashes among young drivers. , 2004, The Cochrane database of systematic reviews.

[41]  Eleni I. Vlahogianni,et al.  Powered Two-Wheeler Detection Using Crowdsourced Smartphone Data , 2020, IEEE Transactions on Intelligent Vehicles.

[42]  Simo Salminen,et al.  Two interventions for the prevention of work-related road accidents , 2008 .

[43]  George Yannis,et al.  Innovative motor insurance schemes: A review of current practices and emerging challenges. , 2017, Accident; analysis and prevention.

[44]  Sandro Castronovo,et al.  On timing and modality choice with local danger warnings for drivers , 2009, AutomotiveUI.

[45]  Neville A Stanton,et al.  Safe driving in a green world: a review of driver performance benchmarks and technologies to support 'smart' driving. , 2011, Applied ergonomics.

[46]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[47]  Tomer Toledo,et al.  In-vehicle data recorders for monitoring and feedback on drivers' behavior , 2008 .

[48]  Kanwaldeep Kaur,et al.  Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars , 2018 .

[49]  John D. Lee,et al.  Warn me now or inform me later: Drivers' acceptance of real-time and post-drive distraction mitigation systems , 2012, Int. J. Hum. Comput. Stud..

[50]  J. Schade,et al.  Reactance or acceptance? Reactions towards the introduction of road pricing , 2007 .

[51]  Ingrid van Schagen,et al.  Driving speed and the risk of road crashes: a review. , 2006, Accident; analysis and prevention.

[52]  Anne T McCartt,et al.  Effects of in-vehicle monitoring on the driving behavior of teenagers. , 2010, Journal of safety research.

[53]  Thomas R Krause,et al.  Potential Application of Behavior-Based Safety in the Trucking Industry , 1999 .

[54]  Cheng-Min Lin,et al.  An Implementation of Android-Based Mobile Virtual Instrument for Telematics Applications , 2011, 2011 Second International Conference on Innovations in Bio-inspired Computing and Applications.

[55]  Manfred Tscheligi,et al.  Predicting information technology usage in the car: towards a car technology acceptance model , 2012, AutomotiveUI.

[56]  John D. Lee,et al.  ASSOCIATIONS BETWEEN TRUST AND PERCEIVED USEFULNESS AS DRIVERS ADAPT TO SAFETY SYSTEMS , 2008 .

[57]  Michael Maccoby,et al.  Narcissistic Leaders The Incredible Pros, the Inevitable Cons , 2004 .

[58]  Sharon Newnam,et al.  Modifying behaviour to reduce over-speeding in work-related drivers: an objective approach. , 2014, Accident; analysis and prevention.

[59]  Witold Bartnik,et al.  Supporting Energy Efficient Train Operation by Using Gamification to Motivate Train Drivers , 2016, SimTecT/ISAGA.