A Quantitative and Qualitative Review of the Role of Intelligent Transportation Systems in Road Safety Studies through Three Decades

Document Type : Regular Article

Authors

1 Associate Professor, Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy

2 Ph.D. Candidate, Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy

3 Assistant Professor, Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy

4 Professor, College of IT Convergence, Gachon University, Seongnam 13120, Korea

Abstract

Road safety is an important subject to study in both the technical and academic fields of road transportation. In recent years, there has been a significant rise in the number of studies that look at how intelligent transportation systems (ITS) can be used and what role it plays in making roads safer in different countries. Nevertheless, there are still relatively few in-depth quantitative and qualitative analyses published on the topic of ITS's role in ensuring road safety. For this purpose, the main goal of this study is to look at a thorough quantitative and qualitative analysis of how ITS is used in road safety as a part of transportation engineering. In this study, we reviewed the scientific studies done on the use of ITS in studies of road safety from 1990s to 2022. These studies were published in journals or presented at conferences that were part of the Web of Science (WoS) Index. The analysis in this study gives a thorough map of the field, showing how it has changed over time and pointing the way to new areas of research.

Keywords

Main Subjects

References

[1]     Guido, Vitale, Astarita, Giofrè. Comparison Analysis between Real Accident Locations and Simulated Risk Areas in An Urban Road Network. Safety 2019;5:60. https://doi.org/10.3390/safety5030060.
[2]     Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Astarita V, Park Y, et al. Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy. Saf 2022, Vol 8, Page 28 2022;8:28. https://doi.org/10.3390/SAFETY8020028.
[3]     Guido G, Shaffiee Haghshenas S, Shaffiee Haghshenas S, Vitale A, Astarita V. Application of Feature Selection Approaches for Prioritizing and Evaluating the Potential Factors for Safety Management in Transportation Systems. Computers 2022;11. https://doi.org/10.3390/computers11100145.
[4]     Zinno R, Haghshenas SS, Guido G, Vitale A. Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art. IEEE Access 2022;10. https://doi.org/10.1109/ACCESS.2022.3199443.
[5]     Zinno R, Haghshenas SS, Guido G, Rashvand K, Vitale A, Sarhadi A. The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges. Appl Sci 2023;13. https://doi.org/10.3390/app13010097.
[6]     Astarita V, Haghshenas SS, Guido G, Vitale A. Developing new hybrid grey wolf optimization-based artificial neural network for predicting road crash severity. Transp Eng 2023;12. https://doi.org/10.1016/j.treng.2023.100164.
[7]     Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Astarita V, Haghshenas AS. Feasibility of stochastic models for evaluation of potential factors for safety: A case study in southern Italy. Sustain 2020. https://doi.org/10.3390/su12187541.
[8]     Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Gallelli V, Astarita V. Development of a binary classification model to assess safety in transportation systems using GMDH-type neural network algorithm. Sustain 2020. https://doi.org/10.3390/SU12176735.
[9]     Soltaninejad M, Fardhosseini MS, Kim YW. Safety climate and productivity improvement of construction workplaces through the 6S system: mixed-method analysis of 5S and safety integration. Int J Occup Saf Ergon 2022;28:1811–21. https://doi.org/10.1080/10803548.2021.1935624.
[10]   Guido G, Haghshenas SS, Vitale A, Astarita V. Challenges and Opportunities of Using Data Fusion Methods for Travel Time Estimation. 2022 8th Int Conf Control Decis Inf Technol CoDIT 2022 2022:587–92. https://doi.org/10.1109/CODIT55151.2022.9804014.
[11]   Guido G, Astarita V, Vitale A, Gallelli V, Saccomanno F. A new safety performance index for speed-related crashes. 1st Editio, CRC Press; 2017, p. 593–600.
[12]   Mahpour A, Shafaati M, Amir MA. The effective factors on the safety culture of HAZMAT drivers. Amirkabir J Civ Eng 2021;5:69–78. https://doi.org/10.22060/AJCE.2020.17422.5631.
[13]   Nadimi N, Ragland DR, Mohammadian Amiri A. 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 2020;12:491–500. https://doi.org/10.1080/19427867.2019.1650430.
[14]   Jafarzadeh Ghoushchi S, Shaffiee Haghshenas S, Memarpour Ghiaci A, Guido G, Vitale A. Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment. Neural Comput Appl 2023;35:4549–67. https://doi.org/10.1007/s00521-022-07929-4.
[15]   Elvik R, Høye A, Vaa T, Sørensen M. The handbook of road safety measures. Bingley. UK: Emerald Group Publishing Limited; 2009.
[16]   Hughes BP, Newstead S, Anund A, Shu CC, Falkmer T. A review of models relevant to road safety. Accid Anal Prev 2015;74:250–70. https://doi.org/10.1016/j.aap.2014.06.003.
[17]   Elvik R, Vadeby A, Hels T, van Schagen I. Updated estimates of the relationship between speed and road safety at the aggregate and individual levels. Accid Anal Prev 2019;123:114–22. https://doi.org/10.1016/j.aap.2018.11.014.
[18]   Ziakopoulos A, Yannis G. A review of spatial approaches in road safety. Accid Anal Prev 2020;135:105323. https://doi.org/10.1016/j.aap.2019.105323.
[19]   Hu Y, Li Y, Huang H, Lee J, Yuan C, Zou G. A high-resolution trajectory data driven method for real-time evaluation of traffic safety. Accid Anal Prev 2022;165:106503. https://doi.org/10.1016/j.aap.2021.106503.
[20]   Haghani M, Behnood A, Dixit V, Oviedo-Trespalacios O. Road safety research in the context of low- and middle-income countries: Macro-scale literature analyses, trends, knowledge gaps and challenges. Saf Sci 2022;146:105513. https://doi.org/10.1016/j.ssci.2021.105513.
[21]   Bassani M, Rossetti L, Catani L. Spatial analysis of road crashes involving vulnerable road users in support of road safety management strategies. Transp Res Procedia 2020;45:394–401. https://doi.org/10.1016/j.trpro.2020.03.031.
[22]   Karimi A, Bassani M, Boroujerdian A, Catani L. Investigation into passing behavior at passing zones to validate and extend the use of driving simulators in two-lane roads safety analysis. Accid Anal Prev 2020;139:105487. https://doi.org/10.1016/j.aap.2020.105487.
[23]   Bauernschuster S, Rekers R. Speed Limit Enforcement and Road Safety. SSRN Electron J 2019. https://doi.org/10.2139/ssrn.3523528.
[24]   Cooper DF, Ferguson N. Traffic studies at T-Junctions. 2. A conflict simulation Record. Traffic Eng Control 1976;17.
[25]   Darzentas J, Cooper DF, Storr PA, McDowell MRC. Simulation of road traffic conflicts at T-junctions. Simulation 1980;34:155–64. https://doi.org/10.1177/003754978003400505.
[26]   Pape DB, Narendran VK, Koenig MJ, Hadden JA, Everson JH, Pomerleau DA. Dynamic Vehicle Simulation to Evaluate Countermeasure Systems for Run-Off-Road Crashes, 1996. https://doi.org/10.4271/960517.
[27]   Barcelo J, Ferrer JL, Martin R. Simulation assisted design and assessment of vehicle guidance systems. Int Trans Oper Res 1999;6:123–43. https://doi.org/10.1111/j.1475-3995.1999.tb00147.x.
[28]   Abdel-Aty MA, Radwan AE. Modeling traffic accident occurrence and involvement. Accid Anal Prev 2000;32:633–42. https://doi.org/10.1016/S0001-4575(99)00094-9.
[29]   Astarita V, Giofré V, Guido G, Vitale A. Investigating road safety issues through a microsimulation model. Procedia - Soc Behav Sci 2011;20:226–35. https://doi.org/10.1016/j.sbspro.2011.08.028.
[30]   Alonso B, Astarita V, Dell’Olio L, Giofrè VP, Guido G, Marino M, et al. Validation of Simulated Safety Indicators with Traffic Crash Data. Sustainability 2020;12:925. https://doi.org/10.3390/su12030925.
[31]   Laureshyn A, Svensson Å, Hydén C. Evaluation of traffic safety, based on micro-level behavioural data: Theoretical framework and first implementation. Accid Anal Prev 2010;42:1637–46. https://doi.org/10.1016/j.aap.2010.03.021.
[32]   Guido G, Astarita V, Giofré V, Vitale A. Safety performance measures: a comparison between microsimulation and observational data. Procedia - Soc Behav Sci 2011;20:217–25. https://doi.org/10.1016/j.sbspro.2011.08.027.
[33]   Essa M, Sayed T. Traffic conflict models to evaluate the safety of signalized intersections at the cycle level. Transp Res Part C Emerg Technol 2018;89. https://doi.org/10.1016/j.trc.2018.02.014.
[34]   Astarita V, Festa DC, Giofrè VP, Guido G. Surrogate Safety Measures from Traffic Simulation Models a Comparison of different Models for Intersection Safety Evaluation. Transp. Res. Procedia, vol. 37, 2019. https://doi.org/10.1016/j.trpro.2018.12.186.
[35]   Gallelli V, Guido G, Vitale A, Vaiana R. Effects of calibration process on the simulation of rear-end conflicts at roundabouts. J Traffic Transp Eng (English Ed 2019;6. https://doi.org/10.1016/j.jtte.2018.03.006.
[36]   Mishra A, Chen K, Poddar S, Posadas E, Rangarajan A, Ranka S. Using Video Analytics to Improve Traffic Intersection Safety and Performance. Vehicles 2022;4:1288–313. https://doi.org/10.3390/vehicles4040068.
[37]   Karbasi A, O’Hern S. Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic. Futur Transp 2022;2. https://doi.org/10.3390/futuretransp2010002.
[38]   Lu Q, Tettamanti T, Hörcher D, Varga I. The impact of autonomous vehicles on urban traffic network capacity: an experimental analysis by microscopic traffic simulation. Transp Lett 2020;12. https://doi.org/10.1080/19427867.2019.1662561.
[39]   Virdi N, Grzybowska H, Waller ST, Dixit V. A safety assessment of mixed fleets with Connected and Autonomous Vehicles using the Surrogate Safety Assessment Module. Accid Anal Prev 2019;131. https://doi.org/10.1016/j.aap.2019.06.001.
[40]   Guido G, Haghshenas SS, Haghshenas SS, Vitale A, Gallelli V, Astarita V. Prioritizing the Potential Smartification Measures by Using an Integrated Decision Support System with Sustainable Development Goals (a Case Study in Southern Italy). Safety 2022;8:35. https://doi.org/10.3390/safety8020035.
[41]   Jabbar R, Dhib E, Said A Ben, Krichen M, Fetais N, Zaidan E, et al. Blockchain Technology for Intelligent Transportation Systems: A Systematic Literature Review. IEEE Access 2022;10. https://doi.org/10.1109/ACCESS.2022.3149958.
[42]   Naderpour H, Khatami SM, Barros RC. Prediction of Critical Distance Between Two MDOF Systems Subjected to Seismic Excitation in Terms of Artificial Neural Networks. Period Polytech Civ Eng 2017. https://doi.org/10.3311/PPci.9618.
[43]   Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. https://doi.org/10.1016/j.jobe.2018.01.007.
[44]   Naderpour H, Mirrashid M. Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Complex Intell Syst 2019. https://doi.org/10.1007/s40747-019-00118-2.
[45]   Rezazadeh Eidgahee D, Haddad A, Naderpour H. Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling. Sci Iran 2018;0:0–0. https://doi.org/10.24200/sci.2018.5663.1408.
[46]   Moosavi SMH, Ma Z, Armaghani DJ, Aghaabbasi M, Ganggayah MD, Wah YC, et al. Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses. Appl Sci 2022;12:9392.
[47]   Ikram RMA, Ewees AA, Parmar KS, Yaseen ZM, Shahid S, Kisi O. The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction. Appl Soft Comput 2022;131:109739. https://doi.org/10.1016/j.asoc.2022.109739.
[48]   Ikram RMA, Dai H-L, Ewees AA, Shiri J, Kisi O, Zounemat-Kermani M. Application of improved version of multi verse optimizer algorithm for modeling solar radiation. Energy Reports 2022;8:12063–80. https://doi.org/10.1016/j.egyr.2022.09.015.
[49]   Skentou AD, Bardhan A, Mamou A, Lemonis ME, Kumar G, Samui P, et al. Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models. Rock Mech Rock Eng 2022:https://doi.org/10.1007/s00603-022-03046-9.
[50]   Moosavi SMH, Aghaabbasi M, Yuen CW, Jahed Armaghani D. Evaluation of Applicability and Accuracy of Bus Travel Time Prediction in High and Low Frequency Bus Routes Using Tree-Based ML Techniques. J Soft Comput Civ Eng 2023;7:74–97.
[51]   Fakharian P, Rezazadeh Eidgahee D, Akbari M, Jahangir H, Ali Taeb A. Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms. Structures 2023;47:1790–802. https://doi.org/10.1016/j.istruc.2022.12.007.
[52]   Hina MD, Soukane A, Ramdane-Cherif A. Computational Intelligence in Intelligent Transportation Systems: An Overview. EAI/Springer Innov. Commun. Comput., 2022. https://doi.org/10.1007/978-3-030-78284-9_2.
[53]   Yuan T, Da Rocha Neto W, Rothenberg CE, Obraczka K, Barakat C, Turletti T. Machine learning for next-generation intelligent transportation systems: A survey. Trans Emerg Telecommun Technol 2022;33. https://doi.org/10.1002/ett.4427.
[54]   Maghami MR, Asl S navabi, Rezadad M esmaeil, Ale Ebrahim N, Gomes C. Qualitative and quantitative analysis of solar hydrogen generation literature from 2001 to 2014. Scientometrics 2015;105. https://doi.org/10.1007/s11192-015-1730-3.
[55]   Rowley J, Slack F. Conducting a literature review. Manag Res News 2004;27. https://doi.org/10.1108/01409170410784185.
[56]   Jafarzadeh-Ghoushchi S, Dorost S, Hashempour S. Qualitative and quantitative analysis of Green Supply Chain Management (GSCM) literature from 2000 to 2015. Int J Supply Chain Manag 2018;7.
[57]   Shaffiee Haghshenas S, Careddu N, Jafarzadeh Ghoushchi S, Mikaeil R, Kim T-H, Geem ZW. Quantitative and Qualitative Analysis of Harmony Search Algorithm in Geomechanics and Its Applications, 2022, p. 13–23. https://doi.org/10.1007/978-981-19-2948-9_2.
[58]   Haghshenas SS, Guido G, Vitale A, Ghoushchi SJ. Quantitative and Qualitative Analysis of Internet of Things (IoT) in Smart Cities and its Applications. 2022 IEEE Intl Conf Dependable, Auton. Secur. Comput. Intl Conf Pervasive Intell. Comput. Intl Conf Cloud Big Data Comput. Intl Conf Cyber Sci. Technol. Congr., IEEE; 2022, p. 1–6. https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927793.
[59]   Dimitrakopoulos G, Demestichas P. Intelligent Transportation Systems. IEEE Veh Technol Mag 2010;5:77–84. https://doi.org/10.1109/MVT.2009.935537.
[60]   Qureshi KN, Abdullah AH. A survey on intelligent transportation systems. Middle East J Sci Res 2013;15. https://doi.org/10.5829/idosi.mejsr.2013.15.5.11215.
[61]   Guerrero-Ibáñez J, Zeadally S, Contreras-Castillo J. Sensor technologies for intelligent transportation systems. Sensors (Switzerland) 2018;18. https://doi.org/10.3390/s18041212.
[62]   Zhao L, Chai H, Han Y, Yu K, Mumtaz S. A Collaborative V2X Data Correction Method for Road Safety. IEEE Trans Reliab 2022;71:951–62. https://doi.org/10.1109/TR.2022.3159664.
[63]   Abou El Hassan A, Kerrakchou I, El Mehdi A, Saber M. Road Safety Enhancement of Intelligent Transportation Systems: From Cellular LTE-V2X Toward 5G-V2X, 2022, p. 745–54. https://doi.org/10.1007/978-3-031-02447-4_77.
[64]   Mahmood A, Siddiqui SA, Sheng QZ, Zhang WE, Suzuki H, Ni W. Trust on wheels: Towards secure and resource efficient IoV networks. Computing 2022;104. https://doi.org/10.1007/s00607-021-01040-7.
[65]   Smart Roadside, Intelligent Transportation Systems n.d.
[66]   Abboud K, Omar HA, Zhuang W. Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey. IEEE Trans Veh Technol 2016;65. https://doi.org/10.1109/TVT.2016.2591558.
[67]   Lei A, Cruickshank H, Cao Y, Asuquo P, Ogah CPA, Sun Z. Blockchain-Based Dynamic Key Management for Heterogeneous Intelligent Transportation Systems. IEEE Internet Things J 2017;4. https://doi.org/10.1109/JIOT.2017.2740569.
[68]   Stanton NA, Salmon PM. Human error taxonomies applied to driving: A generic driver error taxonomy and its implications for intelligent transport systems. Saf Sci 2009;47. https://doi.org/10.1016/j.ssci.2008.03.006.
[69]   Rahman MM, Lesch MF, Horrey WJ, Strawderman L. Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accid Anal Prev 2017;108. https://doi.org/10.1016/j.aap.2017.09.011.
[70]   Liu CC, Hosking SG, Lenné MG. Predicting driver drowsiness using vehicle measures: Recent insights and future challenges. J Safety Res 2009;40. https://doi.org/10.1016/j.jsr.2009.04.005.
[71]   Bali RS, Kumar N, Rodrigues JJPC. Clustering in vehicular ad hoc networks: Taxonomy, challenges and solutions. Veh Commun 2014;1. https://doi.org/10.1016/j.vehcom.2014.05.004.
[72]   Wang FY, Zheng NN, Cao D, Martinez CM, Li L, Liu T. Parallel driving in CPSS: A unified approach for transport automation and vehicle intelligence. IEEE/CAA J Autom Sin 2017;4. https://doi.org/10.1109/JAS.2017.7510598.
[73]   Zaklouta F, Stanciulescu B. Real-time traffic sign recognition in three stages. Rob Auton Syst 2014;62. https://doi.org/10.1016/j.robot.2012.07.019.
[74]   Eze EC, Zhang S, Liu E. Vehicular ad hoc networks (VANETs): Current state, challenges, potentials and way forward. ICAC 2014 - Proc. 20th Int. Conf. Autom. Comput. Futur. Autom. Comput. Manuf., 2014. https://doi.org/10.1109/IConAC.2014.6935482.
[75]   Pauzié A. A method to assess the driver mental workload: The driving activity load index (DALI). IET Intell. Transp. Syst., vol. 2, 2008. https://doi.org/10.1049/iet-its:20080023.

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History

  • Receive Date: 20 January 2023
  • Revise Date: 09 April 2023
  • Accept Date: 12 April 2023
  • First Publish Date: 13 April 2023

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