Abstract
To overcome limitations of crash-based analyses of road safety, researchers are looking for surrogate measures of safety. Floating car data (FCD, or probe vehicle data) provides opportunities to extract such measures. The goal of this review is to identify challenges and opportunities regarding using FCD to develop surrogate measures of safety. Specific focus was placed on most frequent speed-related indicators (speed, acceleration, jerk) and their sampling rate, study size, reliability and validity. The review indicated several remaining knowledge gaps; nevertheless, with the current rate of technology development and research, many of these gaps are likely to be resolved quickly. The main conclusion is that nature, benefits and limitations of different FCD sources need to be carefully understood and considered before adopting FCD to derive surrogate measures of safety. Further research and development opportunities exist in the subject area.
Keywords
- Road safety
- Floating car data
- Probe vehicle data
- Surrogate measures of safety
- Speed
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Chang A, Saunier N, Laureshyn A (2017) Proactive methods for road safety analysis. SAE International, Warrendale
Bessler S, Paulin T (2013) Literature study on the state of the art of probe data systems in Europe. FTW Telecommunications Research Center, Vienna
Nilsson G (2004) Traffic safety dimensions and the Power Model to describe the effect of speed on safety. Lund University, Lund
Elvik R (2009) The Power Model of the relationship between speed and road safety: Update and new analyses. Institute of Transport Economics, Oslo
Elvik R (2013) A reparameterisation of the Power Model of the relationship between the speed of traffic and the number of accidents and accident victims. Accid Anal Prev 50:854–860
Leduc G (2008) Road traffic data: collection methods and applications. European Communities, Luxembourg City
Rose G (2006) Mobile phones as traffic probes: Practices, prospects and issues. Transp Rev 26:275–291
Berntsen M, Molnár P, Zděnek M (2016) Driving behavior and risk of accident: evidence from Norway. Norwegian University of Science and Technology, Trondheim
Wang X, Fan T, Chen M, Deng B, Wu B, Tremont P (2015) Safety modeling of urban arterials in Shanghai, China. Accid Anal Prevent 83:57–66
Wang X, Fan T, Li W, Yu R, Bullock D, Wu B, Tremont P (2016) Speed variation during peak and off-peak hours on urban arterials in Shanghai. Transp Res Part C 67:84–94
Wang X, Zhou Q, Quddus M, Fan T, Fang S (2018) Speed, speed variation and crash relationships for urban arterials. Accid Anal Prev 113:236–243
Jurewicz C, Espada I, Makwasha T, Han C, Alawi H, Ambros J (2017) Validation and applicability of floating car speed data for road safety. In: Australasian road safety conference, Perth
Bekhor S, Lotan T, Gitelman V, Morik S (2013) Free-flow travel speed analysis and monitoring at the national level using global positioning system measurements. J Transp Eng ASCE 139:1235–1243
Hrubeš P, Blümelová J (2015) Comparative analysis for floating car and loop detectors data. In: 22nd ITS World Congress, Bordeaux
Pascale A, Deflorio F, Nicoli M, Dalla Chiara B, Pedroli M (2015) Motorway speed pattern identification from floating vehicle data for freight applications. Transp Res Part C 51:104–119
Aarts LT, Bijleveld FD, Stipdonk HL (2015) Usefulness of ʼfloating car speed dataʼ for proactive road safety analyses: analysis of TomTom speed data and comparison with loop detector speed data of the provincial road network in the Netherlands. SWOV Institute for Road Safety Research, Leidschendam
Laporte S (ed) (2010) Design of a naturalistic riding study-implementation plan. 2BESAFE project deliverable 5
Backer-Grøndahl A, Phillips R, Sagberg F, Touliou K, Gatscha M (2009) Topics and applications of previous and current naturalistic driving studies. PROLOGUE project deliverable 1.1
Welsh R, Reed S, Talbot R, Morris A (2010) Data collection, analysis methods and equipment for naturalistic studies and requirements for the different application areas. PROLOGUE project deliverable 2.1
Bärgman J (2015) On the analysis of naturalistic driving data: development and evaluation of methods for analysis of naturalistic driving data from a variety of data sources. Chalmers University of Technology, Gothenburg
Reinau KH, Andersen CS, Agerholm N (2016) A new method for identifying hazardous road locations using GPS and accelerometer. In: 23rd ITS World Congress, Melbourne
Toledo T, Musicant O, Lotan T (2008) In-vehicle data recorders for monitoring and feedback on drivers’ behavior. Transp Res Part C 16:320–331
Ambros J, Valentová V, Gogolín O, Andrášik R, Kubeček J, Bíl M (2017) Improving the self-explaining performance of Czech national roads. Transp Res Rec 2635:62–70
Bagdadi O, Várhelyi A (2011) Jerky driving—an indicator of accident proneness? Accid Anal Prev 43:1359–1363
Punzo V, Borzacchiello MT, Ciuffo B (2011) On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data. Transp Res Part C 19:1243–1262
Joubert JW, de Beer D, de Koker N (2016) Combining accelerometer data and contextual variables to evaluate the risk of driver behaviour. Transp Res Part F 41:80–96
Carsten O, Kircher K, Jamson S (2013) Vehicle-based studies of driving in the real world: The hard truth? Accid Anal Prev 58:162–174
Valero-Mora PM, Tontsch A, Welsh R, Morris A, Reed S, Touliou K, Margaritis D (2013) Is naturalistic driving research possible with highly instrumented cars? Lessons learnt in three research centres. Accid Anal Prev 58:187–194
Kraft WH, Homburger WS, Pline JL (eds) (2019) Traffic engineering handbook, 6th edn. Institute of Transportation Engineers, Washington
Narasimha Murthy AS, Mohle HR (2001) Transportation engineering basics, 2nd edn. American Society of Civil Engineers, Reston
PIARC (2003) Road Safety Manual: recommendations from the World Road Association (PIARC). Route2market, Harrogate
Transportation Research Board (2011) Modeling operating speed: synthesis report. Transportation Research Board, Washington
Smith BL, Zhang H, Fontaine M, Green M (2003) Cell-phone probes as an ATMS tool. Smart Travel Laboratory, Charlottesville
Vandenberghe W, Vanhauwaert E, Verbrugge S, Moerman I, Demeester P (2012) Feasibility of expanding traffic monitoring systems. IET Intel Transport Syst 6:347–354
Srinivasan KK, Jovanis PP (1996) Determination of number of probe vehicles required for reliable travel time measurement in urban network. Transp Res Rec 1537:15–22
Hakkert AS, Gitelman V (eds) (2007) Road safety performance indicators: manual. SafetyNet project deliverable 3.8
Ambros J, Kyselý M (2016) Free-flow vs car-following speeds: Does the difference matter? Adv Transp Studies 40:17–26
Pline JL (ed) (1992) Traffic engineering handbook, 4th edn. Institute of Transportation Engineers, Washington
Wang J, Dixon K, Li H, Hunter M (2006) Operating-speed model for low-speed urban tangent streets based on in-vehicle global positioning system data. Transp Res Rec 1961:24–33
TomTom traffic index: measuring congestion worldwide. https://www.tomtom.com/en_gb/trafficindex/about
Diependaele K, Riguelle F, Temmerman P (2016) Speed behavior indicators based on floating car data: results of a pilot study in Belgium. Transportation Research Procedia 14:2074–2082
Spasovic LN, Dimitrijevic B, Kim K (2013) Probe vehicle data comparative validation study—New Jersey and New York—final report. New Jersey Institute of Technology, Newark
Rapolu S, Kumar A (2015) Comparing arterial speeds from “big-data” sources in Southeast Florida (Bluetooth, HERE and INRIX). In: 15th TRB national transportation planning applications conference, Atlantic City
Clergue L, Buttignol V (2014) Using GPS data in favour of traffic knowledge. In: Transport Research Arena, Paris
De Boer G, Krootjes P (2012) The quality of floating car data benchmarked: an alternative to roadside equipment? In: 19th ITS World Congress, Vienna
Brockfeld E, Lorkowski S, Mieth P, Wagner P (2007) Benefits and limits of recent floating car data technology—an evaluation study. In: 11th WCTR Conference, Berkeley
INRIX I-95 VPP data summary validation. http://inrix.com/case-studies/inrix-i-95-vpp-data-summary-validation-case-study/
Espada I, Bennett P (2015) Probe data and its application in traffic studies. In: IPWEA/IFME conference, Rotorua
Lattimer CR, Glotzbach G (2012) Evaluation of third-party travel time data in Tallahassee. FL. In ITS America, National Harbor
Bar-Gera H (2007) Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: a case study from Israel. Transp Res Part C 15:380–391
Yim Y (2003) The state of cellular probes. University of California, Berkeley
Sagberg F, Selpi S, Piccinini GFB, Engström J (2015) A review of research on driving styles and road safety. Hum Factors 57:1248–1275
Dingus TA, McGehee DV, Manakkal N, Jahns SK, Carney C, Hankey JM (1997) Human factors field evaluation of automotive headway maintenance/collision warning devices. Hum Factors 39:216–229
Feng F, Bao S, Sayer JR, Flannagan C, Manser M, Wunderlich R (2017) Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data. Accid Anal Prev 104:125–136
Kiefer RJ, Flannagan CA, Jerome CJ (2006) Time-to-collision judgments under realistic driving conditions. Hum Factors 48:334–345
Markkula G, Engström J, Lodin J, Bärgman J, Victor T (2016) A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies. Accid Anal Prevent 95:209–226
Chevalier A, Chevalier AJ, Clarke E, Coxon K, Brown J, Rogers K, Boufous S, Ivers R, Keay L (2016) Naturalistic rapid deceleration data: drivers aged 75 years and older. Data Brief 9:909–916
Chevalier A, Coxon K, Chevalier AJ, Clarke E, Rogers K, Brown J, Boufous S, Ivers R, Keay L (2017) Predictors of older drivers’ involvement in rapid deceleration events. Accid Anal Prev 98:312–319
Keay L, Munoz B, Duncan DD, Hahn D, Baldwin K, Turano KA, Munro CA, Bandeen-Roche K, West SK (2013) Older drivers and rapid deceleration events: Salisbury eye evaluation driving study. Accid Anal Prev 58:279–285
Tselentis DI, Yannis G, Vlahogianni EI (2017) Innovative motor insurance schemes: a review of current practices and emerging challenges. Accid Anal Prev 98:139–148
Stavrakaki A-M, Tselentis DI, Barmpounakis EN, Vlahogianni EI, Yannis G (2019) How much driving data do we need to assess driver behavior? In: 98th transportation research board annual meeting, Washington
Ellison AB, Greaves SP, Bliemer MCJ (2015) Driver behaviour profiles for road safety analysis. Accid Anal Prev 76:118–132
Aichinger C, Nitsche P, Stütz R, Harnisch M (2016) Using low-cost smartphone sensor data for locating crash risk spots in a road network. Transp Res Procedia 14:2015–2024
Baldwin KC, Duncan DD, West SK (2004) The driver monitor system: a means of assessing driver performance. Johns Hopkins APL Technical Digest 25:269–277
Hickman JS, Hanowski RJ (2011) Use of a video monitoring approach to reduce at-risk driving behaviors in commercial vehicle operations. Transp Res Part F 14:189–198
Johnson DA, Trivedi MM (2011) Driving style recognition using a smartphone as a sensor platform. In: 14th international IEEE conference on intelligent transportation systems, Washington (2011)
Kamla J, Parry T, Dawson A (2019) Analysing truck harsh braking incidents to study round-about accident risk. Accid Anal Prev 122:365–377
Musicant O, Botzer A, Laufer I, Collet C (2018) Relationship between kinematic and physiological indices during braking events of different intensities. Hum Factors 60:415–427
Paefgen J, Kehr F, Zhai Y, Michahelles F (2012) Driving behavior analysis with smart-phones: insights from a controlled field study. In: 11th international conference on mobile and ubiquitous multimedia, Ulm
Ryder B, Gahr B, Egolf P, Dahlinger A, Wortmann F (2017) Preventing traffic accidents with in-vehicle decision support systems—the impact of accident hotspot warnings on driver behaviour. Decis Support Syst 99:64–74
Naude C, Serre T, Dubois-Lounis M, Fournier J-Y, Lechner D, Guilbot M, Le-doux V (2019) Acquisition and analysis of road incidents based on vehicle dynamics. Accid Anal Prev 130:117–124
Pande A, Chand S, Saxena N, Dixit V, Loy J, Wolshon B, Kentda JD (2017) A preliminary investigation of the relationships between historical crash and naturalistic driving. Accid Anal Prev 101:107–116
Dingus TA, Klauer SG, Neale VL, Petersen A, Lee SE, Sudweeks JD, Perez MA, Hankey J, Ramsey DJ, Gupta S, Bucher C, Doerzaph ZR, Jermeland J, Knipling RR (2006) The 100-car naturalistic driving study, phase II—results of the 100-car field experiment. National Highway Traffic Safety Administration, Washington
Händel P, Skog I, Wahlström J, Bonawiede F, Welch R, Ohlsson J, Ohlsson M (2014) Insurance telematics: opportunities and challenges with the smartphone solution. IEEE intelligent transportation systems magazine, pp 57–70
Ziakopoulos A, Tselentis D, Kontaxi A, Yannis G (2020) A critical overview of driver recording tools. J Safety Res 72:203–212
Stipancic J, Miranda-Moreno L, Saunier N (2018) Vehicle manoeuvers as surrogate safety measures: Extracting data from the GPS-enabled smartphones of regular drivers. Accid Anal Prev 115:160–169
Mousavi S-M, Parr SA, Pande A, Wolshon B (2015) Identifying high-risk roadways through jerk-cluster analysis. In: 2015 road safety and simulation international conference, Orlando
Andersen CS, Reinau KH, Agerholm N (2016) The relationship between road characteristics and speed collected from floating car data. J Traffic Transp Eng 4:291–298
Gitelman V, Pesahov F, Carmel R, Bekhor S (2016) The identification of infrastructure characteristics influencing travel speeds on single-carriageway roads to promote self-explaining roads. Transp Res Procedia 14:4160–4169
Gaca S, Kieć M (2016) Speed management for local and regional rural roads. Transp Res Procedia 14:4170–4179
Bassani M, Cirillo C, Molinari S, Tremblay J (2016) Random effect models to predict operating speed distribution on rural two-lane highways. J Transp Eng ASCE 142:04016019
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The chapter was produced with the financial support of Czech Ministry of Education, Youth and Sports under the National Sustainability Program I project of Transport R&D Centre (LO1610), using the research infrastructure from the Operational Program Research and Development for Innovation (CZ.1.05/2.1.00/03.0064).
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Ambros, J., Jurewicz, C., Chevalier, A., Valentová, V. (2021). Speed-Related Surrogate Measures of Road Safety Based on Floating Car Data. In: Macioszek, E., Sierpiński, G. (eds) Research Methods in Modern Urban Transportation Systems and Networks. Lecture Notes in Networks and Systems, vol 207. Springer, Cham. https://doi.org/10.1007/978-3-030-71708-7_9
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