Review
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Year 2024, Volume: 42 Issue: 5, 1670 - 1682, 04.10.2024

Abstract

References

  • REFERENCES
  • [1] Road Transport Accidents [Internet]. 2019 [cited 2024 Aug 14]. Available from: https://ncrb.gov.in/sites/default/files/Chapter-1A-Traffic-Accidents_2019.pdf
  • [2] Lefler J. The Black Spot. Xlibris; 2017.
  • [3] Gartner G, Huang H. Progress in Location-Based Services 2016. Springer; 2017. [CrossRef]
  • [4] Zou X, Yue WL, Li C. Road traffic accident prediction using an SCGM (1, 1) c-Markov model. Asian Transp Stud. 2018;5:191205.
  • [5] Strnad B. Road Safety Audit and Road Safety Inspection on the TEM network. USA: United Nations; 2018.
  • [6] Kustra W, Jamroz K, Budzynski M. Safety PL–a support tool for road safety impact assessment. Transp Res Procedia 2016;14:34563465. [CrossRef]
  • [7] Shinar D. Crash causes, countermeasures, and safety policy implications. Accid Anal Prev 2019;125:224231. [CrossRef]
  • [8] Papadimitriou E, Filtness A, Theofilatos A, Ziakopoulos A, Quigley C, Yannis G. Review and ranking of crash risk factors related to the road infrastructure. Accid Anal Prev 2019;125:8597. [CrossRef]
  • [9] Rolison JJ, Regev S, Moutari S, Feeney A. What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accid Anal Prev 2018;115:1124. [CrossRef]
  • [10] Ahlström C, Anund A, Fors C, Åkerstedt T. Effects of the road environment on the development of driver sleepiness in young male drivers. Accid Anal Prev 2018;112:127134. [CrossRef]
  • [11] Alvaro PK, Burnett NM, Kennedy GA, Min WYX, McMahon M, Barnes M, et al. Driver education: Enhancing knowledge of sleep, fatigue and risky behaviour to improve decision making in young drivers. Accid Anal Prev 2018;112:7783. [CrossRef]
  • [12] Ando R, Higuchi K, Mimura Y. Data analysis on traffic accident and urban crime: a case study in Toyota City. Int J Transp Sci Technol 2018;7:103113. [CrossRef]
  • [13] Savolainen PT, Mannering FL, Lord D, Quddus MA. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accid Anal Prev 2011;43:16661676. [CrossRef]
  • [14] Lu H, Huang S, Li Y, Yang Y. Panel data analysis via variable selection and subject clustering. In: Data Mining for Service. 2014. p. 6-76. [CrossRef]
  • [15] Lu P, Chen S, Zheng Y. Artificial intelligence in civil engineering. Math Probl Eng. 2012;2012:324050. [CrossRef]
  • [16] Ekapun P, Pang TY. Design and performance analysis of an electromagnetic tricycle operated in an airport. Procedia Eng. 2015;99:13301338. [CrossRef]
  • [17] Underwood P, Waterson P. Accident analysis models and methods: guidance for safety professionals. Loughborough University; 2013.
  • [18] Gutierrez-Osorio C, Pedraza C. Modern data sources and techniques for analysis and forecast of road accidents: A review. J Traffic Transp Eng (Engl Ed) 2020;7:432446. [CrossRef]
  • [19] Silva PB, Andrade M, Ferreira S. Machine learning applied to road safety modeling: A systematic literature review. J Traffic Transp Eng (Engl Ed). 2020;7:775790. [CrossRef]
  • [20] Mannering F. Temporal instability and the analysis of highway accident data. Anal Methods Accid Res 2018;17:113. [CrossRef]
  • [21] Petkov G. Symptom-based context quantification for dynamic accident analysis. Saf Sci 2020;121:666678. [CrossRef]
  • [22] Zhuang X, Wu C. Display of required crossing speed improves pedestrian judgment of crossing possibility at clearance phase. Accid Anal Prev 2018;112:1520. [CrossRef]
  • [23] Intini P, Berloco N, Colonna P, Ranieri V, Ryeng E. Exploring the relationships between drivers’ familiarity and two-lane rural road accidents: A multi-level study. Accid Anal Prev 2018;111:280296. [CrossRef]
  • [24] Liu C, Zhao M, Li W, Sharma A. Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes. Anal Methods Accid Res 2018;17:3246.
  • [CrossRef] [25] Chin HC, Zhou M. A study of at-fault older drivers in light-vehicle crashes in Singapore. Accid Anal Prev 2018;112:5055. [CrossRef]
  • [26] Ma Z, Shao C, Yue H, Ma S. Analysis of the logistic model for accident severity on urban road environment. In: 2009 IEEE Intelligent Vehicles Symposium. 2009. p. 983987. [CrossRef]
  • [27] Li Y, Xing L, Wang W, Liang M, Wang H. Evaluating the impact of Mobike on automobile-involved bicycle crashes at the road network level. Accid Anal Prev 2018;112:6976. [CrossRef]
  • [28] Wang Y, Zhang D, Liu Y, Dai B, Lee LH. Enhancing transportation systems via deep learning: A survey. Transp Res Part C Emerg Technol 2019;99:144163. [CrossRef]
  • [29] Gargoum SA, Tawfeek MH, El-Basyouny K, Koch JC. Available sight distance on existing highways: Meeting stopping sight distance requirements of an aging population. Accid Anal Prev 2018;112:5668. [CrossRef]
  • [30] Mridula G. Traffic accident analysis and mitigation measures at kariyad (NH-544), Ernakulam, Kerala. Int J Sci Eng Technol 2016;4:725735.
  • [31] Chen F, Chen S. Injury severities of truck drivers in single-and multi-vehicle accidents on rural highways. Accid Anal Prev 2011;43:16771688. [CrossRef]
  • [32] Hickman JS, Hanowski RJ, Bocanegra J. A synthetic approach to compare the large truck crash causation study and naturalistic driving data. Accid Anal Prev. 2018;112:1114. [CrossRef]
  • [33] Malyshkina NV, Mannering FL. Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents. Accid Anal Prev 2010;42:131139. [CrossRef]
  • [34] Bener A, Özkan T, Lajunen T. The driver behaviour questionnaire in Arab Gulf countries: Qatar and United Arab Emirates. Accid Anal Prev 2008;40:14111417. [CrossRef]
  • [35] Squillante R Jr, Fo DJS, Maruyama N, Junqueira F, Moscato LA, Nakamoto FY, et al. Modeling accident scenarios from databases with missing data: A probabilistic approach for safety-related systems design. Saf Sci 2018;104:119134. [CrossRef]
  • [36] Sullivan JL, Novak DC, Aultman-Hall L, Scott DM. Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach. Transp Res Part A Policy Pract 2010;44:323336. [CrossRef]
  • [37] Chand S, Dixit VV. Application of Fractal theory for crash rate prediction: Insights from random parameters and latent class tobit models. Accid Anal Prev 2018;112:3038. [CrossRef]
  • [38] Fountas G, Sarwar MT, Anastasopoulos PC, Blatt A, Majka K. Analysis of stationary and dynamic factors affecting highway accident occurrence: a dynamic correlated grouped random parameters binary logit approach. Accid Anal Prev 2018;113:330340. [CrossRef]
  • [39] Lee J, Chae J, Yoon T, Yang H. Traffic accident severity analysis with rain-related factors using structural equation modeling–A case study of Seoul City. Accid Anal Prev 2018;112:110.
  • [CrossRef]
  • [40] Egbendewe-Mondzozo A, Higgins LM, Shaw WD. Red-light cameras at intersections: Estimating preferences using a stated choice model. Transp Res Part A Policy Pract. 2010;44:281290. [CrossRef]
  • [41] Xu T, Cheng L, Chen Z. Traffic Incident Detection Algorithm Based on Wavelet Support Vector Machine. In: ICCTP 2011: Towards Sustainable Transportation Systems. 2011. p. 541554. [CrossRef]
  • [42] Wu Y, Tan H, Qin L, Ran B, Jiang Z. A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part C Emerg Technol 2018;90:166180. [CrossRef]
  • [43] Zhang J, Kwigizile V, Oh JS. Automated hazardous action classification using natural language processing and machine-learning techniques. In: CICTP 2016. 2016. p. 15791590. [CrossRef]
  • [44] Zhang Z, He Q, Gao J, Ni M. A deep learning approach for detecting traffic accidents from social media data. Transp Res Part C Emerg Technol 2018;86:580596. [CrossRef]
  • [45] Ihueze CC, Onwurah UO. Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria. Accid Anal Prev 2018;112:2129. [CrossRef]
  • [46] Park H, Haghani A, Samuel S, Knodler MA. Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accid Anal Prev 2018;112:3949. [CrossRef]
  • [47] Robb D, Barnes T. Accident rates and the impact of daylight saving time transitions. Accid Anal Prev 2018;111:193201. [CrossRef]
  • [48] Kargah-Ostadi N. Comparison of machine learning techniques for developing performance prediction models. In: Computing in Civil and Building Engineering 2014;2014:12221229. [CrossRef]
  • [49] Dhamaniya A. Development of accident prediction model under mixed traffic conditions: A case study. In: Urban Public Transportation Systems 2013. 2013. p. 124135. [CrossRef]
  • [50] Goel R. Modelling of road traffic fatalities in India. Accid Anal Prev 2018;112:105115. [CrossRef]
  • [51] Ding C, Chen P, Jiao J. Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach. Accid Anal Prev 2018;112:116126. [CrossRef]
  • [52] Shafabakhsh GA, Famili A, Bahadori MS. GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran. J Traffic Transp Eng (Engl Ed) 2017;4:290299. [CrossRef]
  • [53] Çela L, Shiode S, Lipovac K. Integrating GIS and spatial analytical techniques in an analysis of road traffic accidents in Serbia. Int J Traffic Transp Eng 2013;3:115. [CrossRef]
  • [54] Wang L, Li R, Wang C, Liu Z. Driver injury severity analysis of crashes in a western China's rural mountainous county: Taking crash compatibility difference into consideration. J Traffic Transp Eng (Engl Ed) 2021;8:703714. [CrossRef]
  • [55] Gu Y, Liu D, Arvin R, Khattak AJ, Han LD. Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest. Accid Anal Prev 2023;179:106880. [CrossRef]
  • [56] Ahmed S, Hossain MA, Ray SK, Bhuiyan MMI, Sabuj SR. A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance. Transp Res Interdiscip Perspect 2023;19:100814. [CrossRef]
  • [57] Cheng W, Gill GS, Zhang Y, Cao Z. Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions. Accid Anal Prev 2018;112:8493. [CrossRef]
  • [58] Stylianou K, Dimitriou L, Abdel-Aty M. Big data and road safety: A comprehensive review. In: Mobility Patterns, Big Data and Transport Analytics. 2019. p. 297343. [CrossRef]

Enhancing road safety through blackspots mitigative measures-A review

Year 2024, Volume: 42 Issue: 5, 1670 - 1682, 04.10.2024

Abstract

Precise identification of black spots can ensure the timely adoption of remedial measures to mitigate road accidents. The review of various trends of approaches towards identifying the black spots by studying the evolved-up techniques towards a pre-emptive investigation of crash events is the focus of the paper. This study contributes to briefing all the open research issues that existing researchers have merely explored. The paper also discusses addressing such open-end problems that offer more edge to the precise analysis of black spots, followed by a possible plan towards solving these issues to mitigate blackspots.

References

  • REFERENCES
  • [1] Road Transport Accidents [Internet]. 2019 [cited 2024 Aug 14]. Available from: https://ncrb.gov.in/sites/default/files/Chapter-1A-Traffic-Accidents_2019.pdf
  • [2] Lefler J. The Black Spot. Xlibris; 2017.
  • [3] Gartner G, Huang H. Progress in Location-Based Services 2016. Springer; 2017. [CrossRef]
  • [4] Zou X, Yue WL, Li C. Road traffic accident prediction using an SCGM (1, 1) c-Markov model. Asian Transp Stud. 2018;5:191205.
  • [5] Strnad B. Road Safety Audit and Road Safety Inspection on the TEM network. USA: United Nations; 2018.
  • [6] Kustra W, Jamroz K, Budzynski M. Safety PL–a support tool for road safety impact assessment. Transp Res Procedia 2016;14:34563465. [CrossRef]
  • [7] Shinar D. Crash causes, countermeasures, and safety policy implications. Accid Anal Prev 2019;125:224231. [CrossRef]
  • [8] Papadimitriou E, Filtness A, Theofilatos A, Ziakopoulos A, Quigley C, Yannis G. Review and ranking of crash risk factors related to the road infrastructure. Accid Anal Prev 2019;125:8597. [CrossRef]
  • [9] Rolison JJ, Regev S, Moutari S, Feeney A. What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accid Anal Prev 2018;115:1124. [CrossRef]
  • [10] Ahlström C, Anund A, Fors C, Åkerstedt T. Effects of the road environment on the development of driver sleepiness in young male drivers. Accid Anal Prev 2018;112:127134. [CrossRef]
  • [11] Alvaro PK, Burnett NM, Kennedy GA, Min WYX, McMahon M, Barnes M, et al. Driver education: Enhancing knowledge of sleep, fatigue and risky behaviour to improve decision making in young drivers. Accid Anal Prev 2018;112:7783. [CrossRef]
  • [12] Ando R, Higuchi K, Mimura Y. Data analysis on traffic accident and urban crime: a case study in Toyota City. Int J Transp Sci Technol 2018;7:103113. [CrossRef]
  • [13] Savolainen PT, Mannering FL, Lord D, Quddus MA. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accid Anal Prev 2011;43:16661676. [CrossRef]
  • [14] Lu H, Huang S, Li Y, Yang Y. Panel data analysis via variable selection and subject clustering. In: Data Mining for Service. 2014. p. 6-76. [CrossRef]
  • [15] Lu P, Chen S, Zheng Y. Artificial intelligence in civil engineering. Math Probl Eng. 2012;2012:324050. [CrossRef]
  • [16] Ekapun P, Pang TY. Design and performance analysis of an electromagnetic tricycle operated in an airport. Procedia Eng. 2015;99:13301338. [CrossRef]
  • [17] Underwood P, Waterson P. Accident analysis models and methods: guidance for safety professionals. Loughborough University; 2013.
  • [18] Gutierrez-Osorio C, Pedraza C. Modern data sources and techniques for analysis and forecast of road accidents: A review. J Traffic Transp Eng (Engl Ed) 2020;7:432446. [CrossRef]
  • [19] Silva PB, Andrade M, Ferreira S. Machine learning applied to road safety modeling: A systematic literature review. J Traffic Transp Eng (Engl Ed). 2020;7:775790. [CrossRef]
  • [20] Mannering F. Temporal instability and the analysis of highway accident data. Anal Methods Accid Res 2018;17:113. [CrossRef]
  • [21] Petkov G. Symptom-based context quantification for dynamic accident analysis. Saf Sci 2020;121:666678. [CrossRef]
  • [22] Zhuang X, Wu C. Display of required crossing speed improves pedestrian judgment of crossing possibility at clearance phase. Accid Anal Prev 2018;112:1520. [CrossRef]
  • [23] Intini P, Berloco N, Colonna P, Ranieri V, Ryeng E. Exploring the relationships between drivers’ familiarity and two-lane rural road accidents: A multi-level study. Accid Anal Prev 2018;111:280296. [CrossRef]
  • [24] Liu C, Zhao M, Li W, Sharma A. Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes. Anal Methods Accid Res 2018;17:3246.
  • [CrossRef] [25] Chin HC, Zhou M. A study of at-fault older drivers in light-vehicle crashes in Singapore. Accid Anal Prev 2018;112:5055. [CrossRef]
  • [26] Ma Z, Shao C, Yue H, Ma S. Analysis of the logistic model for accident severity on urban road environment. In: 2009 IEEE Intelligent Vehicles Symposium. 2009. p. 983987. [CrossRef]
  • [27] Li Y, Xing L, Wang W, Liang M, Wang H. Evaluating the impact of Mobike on automobile-involved bicycle crashes at the road network level. Accid Anal Prev 2018;112:6976. [CrossRef]
  • [28] Wang Y, Zhang D, Liu Y, Dai B, Lee LH. Enhancing transportation systems via deep learning: A survey. Transp Res Part C Emerg Technol 2019;99:144163. [CrossRef]
  • [29] Gargoum SA, Tawfeek MH, El-Basyouny K, Koch JC. Available sight distance on existing highways: Meeting stopping sight distance requirements of an aging population. Accid Anal Prev 2018;112:5668. [CrossRef]
  • [30] Mridula G. Traffic accident analysis and mitigation measures at kariyad (NH-544), Ernakulam, Kerala. Int J Sci Eng Technol 2016;4:725735.
  • [31] Chen F, Chen S. Injury severities of truck drivers in single-and multi-vehicle accidents on rural highways. Accid Anal Prev 2011;43:16771688. [CrossRef]
  • [32] Hickman JS, Hanowski RJ, Bocanegra J. A synthetic approach to compare the large truck crash causation study and naturalistic driving data. Accid Anal Prev. 2018;112:1114. [CrossRef]
  • [33] Malyshkina NV, Mannering FL. Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents. Accid Anal Prev 2010;42:131139. [CrossRef]
  • [34] Bener A, Özkan T, Lajunen T. The driver behaviour questionnaire in Arab Gulf countries: Qatar and United Arab Emirates. Accid Anal Prev 2008;40:14111417. [CrossRef]
  • [35] Squillante R Jr, Fo DJS, Maruyama N, Junqueira F, Moscato LA, Nakamoto FY, et al. Modeling accident scenarios from databases with missing data: A probabilistic approach for safety-related systems design. Saf Sci 2018;104:119134. [CrossRef]
  • [36] Sullivan JL, Novak DC, Aultman-Hall L, Scott DM. Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach. Transp Res Part A Policy Pract 2010;44:323336. [CrossRef]
  • [37] Chand S, Dixit VV. Application of Fractal theory for crash rate prediction: Insights from random parameters and latent class tobit models. Accid Anal Prev 2018;112:3038. [CrossRef]
  • [38] Fountas G, Sarwar MT, Anastasopoulos PC, Blatt A, Majka K. Analysis of stationary and dynamic factors affecting highway accident occurrence: a dynamic correlated grouped random parameters binary logit approach. Accid Anal Prev 2018;113:330340. [CrossRef]
  • [39] Lee J, Chae J, Yoon T, Yang H. Traffic accident severity analysis with rain-related factors using structural equation modeling–A case study of Seoul City. Accid Anal Prev 2018;112:110.
  • [CrossRef]
  • [40] Egbendewe-Mondzozo A, Higgins LM, Shaw WD. Red-light cameras at intersections: Estimating preferences using a stated choice model. Transp Res Part A Policy Pract. 2010;44:281290. [CrossRef]
  • [41] Xu T, Cheng L, Chen Z. Traffic Incident Detection Algorithm Based on Wavelet Support Vector Machine. In: ICCTP 2011: Towards Sustainable Transportation Systems. 2011. p. 541554. [CrossRef]
  • [42] Wu Y, Tan H, Qin L, Ran B, Jiang Z. A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part C Emerg Technol 2018;90:166180. [CrossRef]
  • [43] Zhang J, Kwigizile V, Oh JS. Automated hazardous action classification using natural language processing and machine-learning techniques. In: CICTP 2016. 2016. p. 15791590. [CrossRef]
  • [44] Zhang Z, He Q, Gao J, Ni M. A deep learning approach for detecting traffic accidents from social media data. Transp Res Part C Emerg Technol 2018;86:580596. [CrossRef]
  • [45] Ihueze CC, Onwurah UO. Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria. Accid Anal Prev 2018;112:2129. [CrossRef]
  • [46] Park H, Haghani A, Samuel S, Knodler MA. Real-time prediction and avoidance of secondary crashes under unexpected traffic congestion. Accid Anal Prev 2018;112:3949. [CrossRef]
  • [47] Robb D, Barnes T. Accident rates and the impact of daylight saving time transitions. Accid Anal Prev 2018;111:193201. [CrossRef]
  • [48] Kargah-Ostadi N. Comparison of machine learning techniques for developing performance prediction models. In: Computing in Civil and Building Engineering 2014;2014:12221229. [CrossRef]
  • [49] Dhamaniya A. Development of accident prediction model under mixed traffic conditions: A case study. In: Urban Public Transportation Systems 2013. 2013. p. 124135. [CrossRef]
  • [50] Goel R. Modelling of road traffic fatalities in India. Accid Anal Prev 2018;112:105115. [CrossRef]
  • [51] Ding C, Chen P, Jiao J. Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach. Accid Anal Prev 2018;112:116126. [CrossRef]
  • [52] Shafabakhsh GA, Famili A, Bahadori MS. GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran. J Traffic Transp Eng (Engl Ed) 2017;4:290299. [CrossRef]
  • [53] Çela L, Shiode S, Lipovac K. Integrating GIS and spatial analytical techniques in an analysis of road traffic accidents in Serbia. Int J Traffic Transp Eng 2013;3:115. [CrossRef]
  • [54] Wang L, Li R, Wang C, Liu Z. Driver injury severity analysis of crashes in a western China's rural mountainous county: Taking crash compatibility difference into consideration. J Traffic Transp Eng (Engl Ed) 2021;8:703714. [CrossRef]
  • [55] Gu Y, Liu D, Arvin R, Khattak AJ, Han LD. Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest. Accid Anal Prev 2023;179:106880. [CrossRef]
  • [56] Ahmed S, Hossain MA, Ray SK, Bhuiyan MMI, Sabuj SR. A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance. Transp Res Interdiscip Perspect 2023;19:100814. [CrossRef]
  • [57] Cheng W, Gill GS, Zhang Y, Cao Z. Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions. Accid Anal Prev 2018;112:8493. [CrossRef]
  • [58] Stylianou K, Dimitriou L, Abdel-Aty M. Big data and road safety: A comprehensive review. In: Mobility Patterns, Big Data and Transport Analytics. 2019. p. 297343. [CrossRef]
There are 60 citations in total.

Details

Primary Language English
Subjects Biochemistry and Cell Biology (Other)
Journal Section Reviews
Authors

Prasad Manoj This is me 0000-0002-7185-5992

Kotagi B Punith This is me 0000-0001-5504-0534

Kerekoppa Chandrashekar Manjunath This is me 0000-0002-0482-5910

Publication Date October 4, 2024
Submission Date March 2, 2023
Published in Issue Year 2024 Volume: 42 Issue: 5

Cite

Vancouver Manoj P, B Punith K, Manjunath KC. Enhancing road safety through blackspots mitigative measures-A review. SIGMA. 2024;42(5):1670-82.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/