Research Article
BibTex RIS Cite
Year 2024, , 34 - 39, 31.12.2024
https://doi.org/10.53635/jit.1563541

Abstract

References

  • World Health Organization. (2019). Global status report on road safety 2018. World Health Organization.
  • National Highway Traffic Safety Administration. (2020). Fatality and injury reporting system tool (FIRST). National Highway Traffic Safety Administration.
  • Hao, W., & Daniel, J. (2016). Driver injury severity related to inclement weather at highway–rail grade crossings in the United States. Traffic injury prevention, 17(1), 31-38. https://doi.org/10.1080/15389588.2015.1034274
  • Umar, I. K., & Bashır, S. (2020). Investigation of the factors contributing to truck driver’s involvement in an injury accident. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(3), 402-408. https://doi.org/10.5505/pajes.2019.65391
  • Mutlu, M. M., & Alver, Y. (2014). Genç sürücülerin trafik kural ihlalleri ve sosyo-ekonomik yapıları arasındaki ilişkiler: Aydın ve Malatya örnekleri. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 20(9), 344-350. https://doi.org/10.5505/pajes.2014.19970
  • Pitaksringkarn, J., Tanwanichkul, L., & Yamthale, K. (2018). A correlation between pavement skid resistance and wet-pavement related accidents in Thailand. In MATEC Web of Conferences. 192. 02049. https://doi.org/10.1051/matecconf/201819202049
  • Lee, J., Chae, J., Yoon, T., & Yang, H. (2018). Traffic accident severity analysis with rain-related factors using structural equation modeling–A case study of Seoul City. Accident Analysis & Prevention, 112, 1-10. https://doi.org/10.1016/j.aap.2017.12.013
  • Mondal, P. (2011). Are road accidents affected by rainfall? A case study from a large Indian metropolitan city. Current Journal of Applied Science and Technology, 1(2), 16-26. https://doi.org/10.9734/BJAST/2011/106
  • Saplioglu, M., Yuzer, E., Aktas, B., & Eriskin, E. (2013). Investigation of the skid resistance at accident occurred at urban intersections. Journal of Traffic and Transportation Engineering, 1(12), 2328-2142.
  • Kassu, A., & Anderson, M. (2018). Analysis of Nonsevere Crashes on Two‐and Four‐Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition. Journal of Advanced Transportation, 2018(1), 2871451. https://doi.org/10.1155/2018/2871451
  • Liu, X., Cao, Q., Wang, H., Chen, J., & Huang, X. (2019). Evaluation of vehicle braking performance on wet pavement surface using an integrated tire-vehicle modeling approach. Transportation research record, 2673(3), 295-307. https://doi.org/10.1177/0361198119832886
  • Zhilin, Y. D., Kharaldin, N. A., Cvetkov, P. S., Stepanov, A. V., Aleshin, M. V., & Borovkov, A. I. (2020). Tire tread optimization method to improve to push aside the water from the road contact patch. In IOP Conference Series: Materials Science and Engineering. 986(1), 012037). https://doi.org/10.1088/1757-899X/986/1/012037
  • Zhu, X., Pang, Y., Yang, J., & Zhao, H. (2022). Numerical analysis of hydroplaning behaviour by using a tire–water-film–runway model. International Journal of Pavement Engineering, 23(3), 784-800. https://doi.org/10.1080/10298436.2020.1774587
  • Das, S., Dutta, A., Dey, K., Jalayer, M., & Mudgal, A. (2020). Vehicle involvements in hydroplaning crashes: Applying interpretable machine learning. Transportation research interdisciplinary perspectives, 6, 100176. https://doi.org/10.1016/j.trip.2020.100176
  • Spitzhüttl, F., Goizet, F., Unger, T., & Biesse, F. (2020). The real impact of full hydroplaning on driving safety. Accident Analysis & Prevention, 138, 105458. https://doi.org/10.1016/j.aap.2020.105458
  • Wang, B., Zhang, C., Zhang, M., Liu, C., Xie, Z., & Zhang, H. (2022). Digital twin analysis for driving risks based on virtual physical simulation technology. IEEE Journal of Radio Frequency Identification, 6, 938-942. https://doi.org/10.1109/JRFID.2022.3203694
  • Şimşek, N., & Kirisci, M. (2023). A new risk assessment method for autonomous vehicle driving systems: Fermatean fuzzy AHP approach. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 22(44), 292-309. https://doi.org/10.55071/ticaretfbd.1300893
  • Ayala-Romero, J. A., Garcia-Saavedra, A., & Costa-Perez, X. (2024, March). Risk-Aware Continuous Control with Neural Contextual Bandits. In Proceedings of the AAAI Conference on Artificial Intelligence 38(19), 20930-20938. https://doi.org/10.1609/aaai.v38i19.30083
  • Yoo, G., Park, J., & Woo, H. (2024). Risk-Conditioned Reinforcement Learning: A Generalized Approach for Adapting to Varying Risk Measures. In Proceedings of the AAAI Conference on Artificial Intelligence 38(15), 16513-16521. https://doi.org/10.1609/aaai.v38i15.29589
  • Zhang, X., Huang, H., Yang, J., & Jiang, S. (2024). Research on Deep Learning-Based Vehicle and Pedestrian Object Detection Algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 213-220. https://doi.org/10.5194/isprs-archives-XLVIII-4-W10-2024-213-2024
  • Hight, P. V., Wheeler, J. B., Reust, T. J., & Birch, N. (1990). The effects of right-side water drag on vehicle dynamics and accident causation (No. 900105). SAE Technical Paper.
  • Ivan, J. N., Ravishanker, N., Jackson, E., Aronov, B., & Guo, S. (2012). A statistical analysis of the effect of wet-pavement friction on highway traffic safety. Journal of Transportation Safety & Security, 4(2), 116-136. https://doi.org/10.1080/19439962.2011.620218
  • Penmetsa, P., Pulugurtha, S. S., & Duddu, V. R. (2018). Factors associated with crashes due to overcorrection or oversteering of vehicles. IATSS research, 42(1), 24-29. https://doi.org/10.1016/j.iatssr.2017.06.001
  • Keogh, J., Doig, G., & Diasinos, S. (2014). Flow compressibility effects around an open-wheel racing car. The Aeronautical Journal, 118(1210), 1409-1431. https://doi.org/10.1017/S0001924000010125
  • Eksioglu, M., & Kızılaslan, K. (2008). Steering-wheel grip force characteristics of drivers as a function of gender, speed, and road condition. International journal of industrial ergonomics, 38(3-4), 354-361. https://doi.org/10.1016/j.ergon.2008.01.004

Gender-based risk analysis of vehicle control loss due to localized water puddles: An analytical approach

Year 2024, , 34 - 39, 31.12.2024
https://doi.org/10.53635/jit.1563541

Abstract

Adverse weather conditions significantly increase the risk of traffic accidents, yet most studies focus on uniform conditions across all tires. This study investigates the impact of asymmetric drag forces when only one tire encounters a local water puddle, causing potential vehicle control loss. Using analytical methods, we calculate drag forces and their transmission to the steering wheel, accounting for variations in water film thickness, vehicle speed, and driver gender. The results indicate that female drivers face higher risks of control loss, with critical speeds decreasing as water film thickness increases. The study highlights that localized water puddles, especially at low speeds and water depths exceeding 3 cm, pose significant risks. These findings can inform road safety guidelines and vehicle design standards to mitigate accident risks in adverse weather conditions.

References

  • World Health Organization. (2019). Global status report on road safety 2018. World Health Organization.
  • National Highway Traffic Safety Administration. (2020). Fatality and injury reporting system tool (FIRST). National Highway Traffic Safety Administration.
  • Hao, W., & Daniel, J. (2016). Driver injury severity related to inclement weather at highway–rail grade crossings in the United States. Traffic injury prevention, 17(1), 31-38. https://doi.org/10.1080/15389588.2015.1034274
  • Umar, I. K., & Bashır, S. (2020). Investigation of the factors contributing to truck driver’s involvement in an injury accident. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(3), 402-408. https://doi.org/10.5505/pajes.2019.65391
  • Mutlu, M. M., & Alver, Y. (2014). Genç sürücülerin trafik kural ihlalleri ve sosyo-ekonomik yapıları arasındaki ilişkiler: Aydın ve Malatya örnekleri. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 20(9), 344-350. https://doi.org/10.5505/pajes.2014.19970
  • Pitaksringkarn, J., Tanwanichkul, L., & Yamthale, K. (2018). A correlation between pavement skid resistance and wet-pavement related accidents in Thailand. In MATEC Web of Conferences. 192. 02049. https://doi.org/10.1051/matecconf/201819202049
  • Lee, J., Chae, J., Yoon, T., & Yang, H. (2018). Traffic accident severity analysis with rain-related factors using structural equation modeling–A case study of Seoul City. Accident Analysis & Prevention, 112, 1-10. https://doi.org/10.1016/j.aap.2017.12.013
  • Mondal, P. (2011). Are road accidents affected by rainfall? A case study from a large Indian metropolitan city. Current Journal of Applied Science and Technology, 1(2), 16-26. https://doi.org/10.9734/BJAST/2011/106
  • Saplioglu, M., Yuzer, E., Aktas, B., & Eriskin, E. (2013). Investigation of the skid resistance at accident occurred at urban intersections. Journal of Traffic and Transportation Engineering, 1(12), 2328-2142.
  • Kassu, A., & Anderson, M. (2018). Analysis of Nonsevere Crashes on Two‐and Four‐Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition. Journal of Advanced Transportation, 2018(1), 2871451. https://doi.org/10.1155/2018/2871451
  • Liu, X., Cao, Q., Wang, H., Chen, J., & Huang, X. (2019). Evaluation of vehicle braking performance on wet pavement surface using an integrated tire-vehicle modeling approach. Transportation research record, 2673(3), 295-307. https://doi.org/10.1177/0361198119832886
  • Zhilin, Y. D., Kharaldin, N. A., Cvetkov, P. S., Stepanov, A. V., Aleshin, M. V., & Borovkov, A. I. (2020). Tire tread optimization method to improve to push aside the water from the road contact patch. In IOP Conference Series: Materials Science and Engineering. 986(1), 012037). https://doi.org/10.1088/1757-899X/986/1/012037
  • Zhu, X., Pang, Y., Yang, J., & Zhao, H. (2022). Numerical analysis of hydroplaning behaviour by using a tire–water-film–runway model. International Journal of Pavement Engineering, 23(3), 784-800. https://doi.org/10.1080/10298436.2020.1774587
  • Das, S., Dutta, A., Dey, K., Jalayer, M., & Mudgal, A. (2020). Vehicle involvements in hydroplaning crashes: Applying interpretable machine learning. Transportation research interdisciplinary perspectives, 6, 100176. https://doi.org/10.1016/j.trip.2020.100176
  • Spitzhüttl, F., Goizet, F., Unger, T., & Biesse, F. (2020). The real impact of full hydroplaning on driving safety. Accident Analysis & Prevention, 138, 105458. https://doi.org/10.1016/j.aap.2020.105458
  • Wang, B., Zhang, C., Zhang, M., Liu, C., Xie, Z., & Zhang, H. (2022). Digital twin analysis for driving risks based on virtual physical simulation technology. IEEE Journal of Radio Frequency Identification, 6, 938-942. https://doi.org/10.1109/JRFID.2022.3203694
  • Şimşek, N., & Kirisci, M. (2023). A new risk assessment method for autonomous vehicle driving systems: Fermatean fuzzy AHP approach. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 22(44), 292-309. https://doi.org/10.55071/ticaretfbd.1300893
  • Ayala-Romero, J. A., Garcia-Saavedra, A., & Costa-Perez, X. (2024, March). Risk-Aware Continuous Control with Neural Contextual Bandits. In Proceedings of the AAAI Conference on Artificial Intelligence 38(19), 20930-20938. https://doi.org/10.1609/aaai.v38i19.30083
  • Yoo, G., Park, J., & Woo, H. (2024). Risk-Conditioned Reinforcement Learning: A Generalized Approach for Adapting to Varying Risk Measures. In Proceedings of the AAAI Conference on Artificial Intelligence 38(15), 16513-16521. https://doi.org/10.1609/aaai.v38i15.29589
  • Zhang, X., Huang, H., Yang, J., & Jiang, S. (2024). Research on Deep Learning-Based Vehicle and Pedestrian Object Detection Algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 213-220. https://doi.org/10.5194/isprs-archives-XLVIII-4-W10-2024-213-2024
  • Hight, P. V., Wheeler, J. B., Reust, T. J., & Birch, N. (1990). The effects of right-side water drag on vehicle dynamics and accident causation (No. 900105). SAE Technical Paper.
  • Ivan, J. N., Ravishanker, N., Jackson, E., Aronov, B., & Guo, S. (2012). A statistical analysis of the effect of wet-pavement friction on highway traffic safety. Journal of Transportation Safety & Security, 4(2), 116-136. https://doi.org/10.1080/19439962.2011.620218
  • Penmetsa, P., Pulugurtha, S. S., & Duddu, V. R. (2018). Factors associated with crashes due to overcorrection or oversteering of vehicles. IATSS research, 42(1), 24-29. https://doi.org/10.1016/j.iatssr.2017.06.001
  • Keogh, J., Doig, G., & Diasinos, S. (2014). Flow compressibility effects around an open-wheel racing car. The Aeronautical Journal, 118(1210), 1409-1431. https://doi.org/10.1017/S0001924000010125
  • Eksioglu, M., & Kızılaslan, K. (2008). Steering-wheel grip force characteristics of drivers as a function of gender, speed, and road condition. International journal of industrial ergonomics, 38(3-4), 354-361. https://doi.org/10.1016/j.ergon.2008.01.004
There are 25 citations in total.

Details

Primary Language English
Subjects Transportation and Traffic
Journal Section Research Articles
Authors

Ekinhan Erişkin 0000-0002-0087-0933

Publication Date December 31, 2024
Submission Date October 8, 2024
Acceptance Date December 18, 2024
Published in Issue Year 2024

Cite

APA Erişkin, E. (2024). Gender-based risk analysis of vehicle control loss due to localized water puddles: An analytical approach. Journal of Innovative Transportation, 5(2), 34-39. https://doi.org/10.53635/jit.1563541