Smartphone-based hard-braking event detection at scale for road safety services

Accelerometer, 2022 Android Auto Projection Mode, 2022 Arun, 2021, A systematic review of traffic conflict-based safety measures with a focus on application context, Anal. Methods Accid. Res., 32 Bagdadi, 2011, Jerky driving—an indicator of accident proneness?, Accid. Anal. Prev., 43, 1359, 10.1016/j.aap.2011.02.009 Bagdadi, 2013, Development of a method for detecting jerks in safety critical events, Accid. Anal. Prev., 50, 83, 10.1016/j.aap.2012.03.032 Campbell, 2012 Car Speed, 2022 Chen, Dongyao, Cho, Kyong-Tak, Han, Sihui, Jin, Zhizhuo, Shin, Kang G, 2015. Invisible sensing of vehicle steering with smartphones. In: Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services. pp. 1–13. Chicago Data Portal, 2022 City of Seattle, 2022 Dingus, 2016, Driver crash risk factors and prevalence evaluation using naturalistic driving data, Proc. Natl. Acad. Sci., 113, 2636, 10.1073/pnas.1513271113 Dingus, Thomas A, Hankey, Jonathan M, Antin, Jonathan F, Lee, Suzanne E, Eichelberger, Lisa, Stulce, Kelly E, McGraw, Doug, Perez, Miguel, Stowe, Loren, 2015. Naturalistic Driving Study: Technical Coordination and Quality Control. Number SHRP 2 Report S2-S06-RW-1. Gettman, 2008 Artificial Intelligence at Google, 2022 Google Maps, 2022 GPS, 2022 Guo, 2019, Statistical methods for naturalistic driving studies, Annu. Rev. Stat. Appl., 6, 309, 10.1146/annurev-statistics-030718-105153 Guo, 2013, Individual driver risk assessment using naturalistic driving data, Accid. Anal. Prev., 61, 3, 10.1016/j.aap.2012.06.014 Guo, 2015, Older driver fitness-to-drive evaluation using naturalistic driving data, J. Saf. Res., 54, 49, 10.1016/j.jsr.2015.06.013 Guo, 2017, The effects of age on crash risk associated with driver distraction, Int. J. Epidemiol., 46, 258 Guo, 2010, Near crashes as crash surrogate for naturalistic driving studies, Transp. Res. Rec., 2147, 66, 10.3141/2147-09 Gyroscope, 2022 Hankey, 2016 Hardt, 2016, Equality of opportunity in supervised learning, Adv. Neural Inf. Process. Syst., 29, 3315 Highway Safety Manual, 2014 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 HSIS, 2022 Hull, Bret, Bychkovsky, Vladimir, Zhang, Yang, Chen, Kevin, Goraczko, Michel, Miu, Allen, Shih, Eugene, Balakrishnan, Hari, Madden, Samuel, 2006. Cartel: a distributed mobile sensor computing system. In: Proceedings of the 4th International Conference on Embedded Networked Sensor Systems. pp. 125–138. Kim, 2016, Exploring the association of rear-end crash propensity and micro-scale driver behavior, Saf. Sci., 89, 45, 10.1016/j.ssci.2016.05.016 Klauer, 2017 Klauer, 2014, Distracted driving and risk of road crashes among novice and experienced drivers, N. Engl. J. Med., 370, 54, 10.1056/NEJMsa1204142 Kuang, 2014, A review of crash surrogate events, 2254 Kuang, 2015, A tree-structured crash surrogate measure for freeways, Accid. Anal. Prev., 77, 137, 10.1016/j.aap.2015.02.007 Lampert, 2014, The bane of skew, Mach. Learn., 97, 5, 10.1007/s10994-013-5432-x Laureshyn, 2017, In search of the severity dimension of traffic events: Extended Delta-V as a traffic conflict indicator, Accid. Anal. Prev., 98, 46, 10.1016/j.aap.2016.09.026 Linear Acceleration, 2022 Liu, Luyang, Li, Hongyu, Liu, Jian, Karatas, Cagdas, Wang, Yan, Gruteser, Marco, Chen, Yingying, Martin, Richard P, 2017. Bigroad: Scaling road data acquisition for dependable self-driving. In: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. pp. 371–384. Mao, 2021, Decision-adjusted driver risk predictive models using kinematics information, Accident Anal. Prevention, 156, 106088, 10.1016/j.aap.2021.106088 Mohan, Prashanth, Padmanabhan, Venkata N., Ramjee, Ramachandran, 2008. Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems. pp. 323–336. Nadimi, 2020, An evaluation of time-to-collision as a surrogate safety measure and a proposal of a new method for its application in safety analysis, Transp. Lett., 12, 491, 10.1080/19427867.2019.1650430 NHTSA, 2018 NHTSA, 2022 NHTSA, 2022 N.Y.C. Open Data, 2022 Open Data D.C., 2022 Ozbay, 2008, Derivation and validation of new simulation-based surrogate safety measure, Transp. Res. Rec., 2083, 105, 10.3141/2083-12 Palat, 2019, Evaluating individual risk proneness with vehicle dynamics and self-report data - toward the efficient detection of At-risk drivers, Accid. Anal. Prev., 123, 140, 10.1016/j.aap.2018.11.016 Parker, 1989 Peesapati, 2018, Can post encroachment time substitute intersection characteristics in crash prediction models?, J. Saf. Res., 66, 205, 10.1016/j.jsr.2018.05.002 Perkins, 1968, Traffic conflict characteristics-accident potential at intersections, Highw. Res. Rec. Russell Dicker, 2021 Samara, Lana, St-Aubin, Paul, Loewenherz, Franz, Budnick, Noah, Miranda-Moreno, Luis, 2020. Video-based network-wide surrogate safety analysis to support a proactive network screening using connected cameras: Case study in the City of Bellevue (WA) United States. In: Proceedings of the Transportation Research Board 100th Annual Meeting, Washington, DC, USA. pp. 9–13. Shi, 2022, Real-time driving risk assessment using deep learning with XGBoost, Accid. Anal. Prev., 178, 10.1016/j.aap.2022.106836 Simons-Morton, 2013, Trajectories of kinematic risky driving among novice teenagers, Accid. Anal. Prev., 51, 27, 10.1016/j.aap.2012.10.011 Simons-Morton, 2009, Hard braking events among novice teenage drivers by passenger characteristics, 236 Simons-Morton, 2012, Do elevated gravitational-force events while driving predict crashes and near crashes?, Am. J. Epidemiol., 175, 1075, 10.1093/aje/kwr440 Stipancic, 2017, Impact of congestion and traffic flow on crash frequency and severity: Application of smartphone-collected GPS travel data, Transp. Res. Rec., 2659, 43, 10.3141/2659-05 Stipancic, 2017, Impact of congestion and traffic flow on crash frequency and severity: Application of smartphone-collected GPS travel data, Transp. Res. Rec., 2659, 43, 10.3141/2659-05 The U.S. Census Bureau, 2022 Thiagarajan, Arvind, Ravindranath, Lenin, LaCurts, Katrina, Madden, Samuel, Balakrishnan, Hari, Toledo, Sivan, Eriksson, Jakob, 2009. Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems. pp. 85–98. Transportation Injury Mapping System (TIMS), 2022 Vaswani, 2017, Attention is all you need, 5998 Vision Zero Network, 2021 Vision Zero Network, 2022 Wang, 2015, Determining driver phone use by exploiting smartphone integrated sensors, IEEE Trans. Mob. Comput., 15, 1965, 10.1109/TMC.2015.2483501 WHO, 2018 WHO, 2019 WHO, 2020 Wikipedia, 2022 Xie, 2019, Use of real-world connected vehicle data in identifying high-risk locations based on a new surrogate safety measure, Accid. Anal. Prev., 125, 311, 10.1016/j.aap.2018.07.002 Yang, 2019, Modeling of time-dependent safety performance using anonymized and aggregated smartphone-based dangerous driving event data, Accid. Anal. Prev., 132, 10.1016/j.aap.2019.105286 Zheng, 2021, Modeling traffic conflicts for use in road safety analysis: A review of analytic methods and future directions, Anal. Methods Accid. Res., 29