What Road Elements are More Important than Others for Safe Driving on Urban Roads?
DOI:
https://doi.org/10.7307/ptt.v35i6.394Keywords:
urban road digitalization, road elements, importance ranking, driver heterogeneity, statistical differenceAbstract
Road elements are increasingly digitalized to provide drivers advanced assistance especially in the emergent or adverse conditions. It is challenging and expensive to accurately digitalize all the road elements especially on the urban roads with many infrastructures and complex designs, where we may focus on the most important ones at the first stage. This research designs a questionnaire to ask the drivers to rank the importance of the road elements in various driving conditions. Driver characteristics are also collected, including age, driving style, accident experience, and accumulated driving distance, to explore their effect on drivers’ cognition of road elements importance. It is found that driving is a complex activity, and the moving elements (e.g. surrounding cars) are more important than the non-moving ones. Attention should be paid to the road elements even distant from the ego car, to get prepared to the potential driving risk or penalty. Statistical difference between the experienced and non-experienced drivers recommends that driver assistance system should be sufficiently trained in various conditions, to build up autonomous driving tactics and skills. This research promotes the understanding of driving cognition pattern to provide insights into the development of road digitalization.
References
Zhao J, Knoop VL, Wang M. Microscopic traffic modeling inside intersections: Interactions between drivers. Transportation Science. 2022;57(1):135-155. DOI: 10.1287/trsc.2022.1163.
Tselentis DI, Vlahogianni EI, Yannis G. Driving safety efficiency benchmarking using smartphone data. Transportation Research Part C: Emerging Technologies. 2019;109:343-357. DOI: 10.1016/j.trc.2019.11.006.
Werling M, et al. Optimal trajectories for time-critical street scenarios using discretized terminal manifolds. The International Journal of Robotics Research. 2012;31(3):346-359. DOI: 10.1177/0278364911423042.
Wei L, et al. Autonomous driving strategies at intersections: Scenarios, state-of-the-art, and future outlooks. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). 2021;44-51. DOI: 10.1109/ITSC48978.2021.9564518.
Lyu N, et al. A field operational test in China: Exploring the effect of an advanced driver assistance system on driving performance and braking behavior. Transportation Research Part F: Traffic Psychology and Behaviour. 2019;65:730-747. DOI: 10.1016/j.trf.2018.01.003.
Chen S, et al. Brain-inspired cognitive model with attention for self-driving cars. IEEE Transactions on Cognitive and Developmental Systems. 2017;11(1):13-25. DOI: 10.1109/TCDS.2017.2717451.
Perret J, Gribaudi M, Berthelemy M. Roads and cities of 18th century France. Scientific Data. 2015;2(1):1-7. DOI: 10.1038/sdata.2015.48.
Boykov VN, Skvortsov AV, Gurev VA. InfraBIM open paradigm as the driver of informatization of the road sector in Russia. IOP Conference Series: Materials Science and Engineering. IOP Publishing. 2020;832(1):012045. DOI: 10.1088/1757-899X/832/1/012045.
Singh R, et al. Highway 4.0: Digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning. Safety Science. 2021;143:105407. DOI: 10.1016/j.ssci.2021.105407.
Hui Z, et al. Road centerline extraction from airborne LiDAR point cloud based on hierarchical fusion and optimization. ISPRS Journal of Photogrammetry and Remote Sensing. 2016;118:22-36. DOI: 10.1016/j.isprsjprs.2016.04.003.
Zhao J, Knoop VL, Wang M. Two-dimensional vehicular movement modelling at intersections based on optimal control. Transportation Research Part B: Methodological. 2020;138:1-22. DOI: 10.1016/j.trb.2020.04.001.
El-Wakeel AS, et al. Robust positioning for road information services in challenging environments. IEEE Sensors Journal. 2019;20(6):3182-3195. DOI: 10.1109/JSEN.2019.2958791.
Dikaiakos MD, et al. Location-aware services over vehicular ad-hoc networks using car-to-car communication. IEEE Journal on Selected Areas in Communications. 2007;25(8):1590-1602. DOI: 10.1109/JSAC.2007.071008.
Laubis K, et al. Enabling crowdsensing-based road condition monitoring service by intermediary. Electronic Markets. 2019;29(1):125-140. DOI: 10.1007/s12525-018-0292-7.
Wahid N, et al. Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi - speed scheduler. IET Intelligent Transport Systems. 2020;14(10):1200-1209. DOI: 10.1049/iet-its.2020.0048.
Wang Y, Wang Y, Choudhury C. Modelling heterogeneity in behavioral response to peak-avoidance policy utilizing naturalistic data of Beijing subway travelers. Transportation Research Part F: Traffic Psychology and Behaviour. 2020;73:92-106. DOI: 10.1016/j.trf.2020.06.016.
Kolekar S, de Winter J, Abbink D. Which parts of the road guide obstacle avoidance? Quantifying the driver's risk field. Applied Ergonomics. 2020;89:103196. DOI: 10.1016/j.apergo.2020.103196.
Wang Y, et al. Be green and clearly be seen: How consumer values and attitudes affect adoption of bicycle sharing. Transportation Research Part F: Traffic Psychology and Behaviour. 2018;58:730-742. DOI: 10.1016/j.trf.2018.06.043.
Magaña VC, et al. Beside and behind the wheel: Factors that influence driving stress and driving behavior. Sustainability. 2021;13(9):4775. DOI: 10.3390/su13094775.
Özkan T, Lajunen T. What causes the differences in driving between young men and women? The effects of gender roles and sex on young drivers’ driving behaviour and self-assessment of skills. Transportation Research Part F: Traffic Psychology and Behaviour. 2006;9(4):269-277. DOI: 10.1016/j.trf.2006.01.005.
Hole GJ. The psychology of driving. Psychology Press; 2014.
Padilla JL, et al. Adaptation of the multidimensional driving styles inventory for Spanish drivers: Convergent and predictive validity evidence for detecting safe and unsafe driving styles. Accident Analysis & Prevention. 2020;136:105413. DOI: 10.1016/j.aap.2019.105413.
Mian M, Jaffry W. Modeling of individual differences in driver behavior. Journal of Ambient Intelligence and Humanized Computing. 2020;11(2):705-718. DOI: 10.1007/s12652-019-01313-2.
Herzberg PY. Beyond “accident-proneness”: Using five-factor model prototypes to predict driving behavior. Journal of Research in Personality. 2009;43(6):1096-1100. DOI: 10.1016/j.jrp.2009.08.008.
Papilloud T, et al. Flood exposure analysis of road infrastructure–Comparison of different methods at national level. International Journal of Disaster Risk Reduction. 2020;47:101548. DOI: 10.1016/j.ijdrr.2020.101548.
Jiang X, et al. Investigating macro-level hotzone identification and variable importance using big data: A random forest models approach. Neurocomputing. 2016;181:53-63. DOI: 10.1016/j.neucom.2015.08.097.
You J, Wang J, Guo J. Real-time crash prediction on freeways using data mining and emerging techniques. Journal of Modern Transportation. 2017;25:116-123. DOI: 10.1007/s40534-017-0129-7.
Dong C, et al. An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities. Transportation Research Part B: Methodological. 2018;118:407-428. DOI: 10.1016/j.trb.2018.10.020.
Zheng Z, Lu P, Lantz B. Commercial truck crash injury severity analysis using gradient boosting data mining model. Journal of Safety Research. 2018;65:115-124. DOI: 10.1016/j.jsr.2018.03.002.
Lian Y, et al. Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles. Accident Analysis & Prevention. 2020;146:105711. DOI: 10.1016/j.aap.2020.105711.
Wang X, Yu R, Zhong C. A field investigation of red-light-running in Shanghai, China. Transportation Research Part F: Traffic Psychology and Behaviour. 2016;37:144-153. DOI: 10.1016/j.trf.2015.12.010.
Apostolakis G. The concept of probability in safety assessments of technological systems. Science. 1990;250(4986):1359-1364. DOI: 10.1126/science.2255906.
Baraldi P, Zio E, Compare M. A method for ranking components importance in presence of epistemic uncertainties. Journal of Loss Prevention in the Process Industries. 2009;22(5):582-592. DOI: 10.1016/j.jlp.2009.02.013.
Öztuna D, Elhan AH, Tüccar E. Investigation of four different normality tests in terms of type 1 error rate and power under different distributions. Turkish Journal of Medical Sciences. 2006;36(3):171-176.
Sawilowsky SS, Hillman SB. Power of the independent samples t test under a prevalent psychometric measure distribution. Journal of Consulting and Clinical Psychology. 1992;60(2):240. DOI: 10.1037/0022-006X.60.2.240.
Kim TK. T test as a parametric statistic. Korean Journal of Anesthesiology. 2015;68(6):540-546. DOI: 10.4097/kjae.2015.68.6.540.
Xi J, et al. Analysis of influencing factors for rear-end collision on the freeway. Advances in Mechanical Engineering. 2019;11(7):1687814019865079. DOI: 10.1177/1687814019865079.
Li L, et al. Dynamic driving risk potential field model under the connected and automated vehicles environment and its application in car-following modeling. IEEE Transactions on Intelligent Transportation Systems. 2020;23(1):122-141. DOI: 10.1109/ACCESS.2020.3044909.
Lu B, et al. Adaptive potential field-based path planning for complex autonomous driving scenarios. IEEE Access. 2020;8:225294-225305. DOI: 10.1109/TITS.2020.3008284.
Kolekar S, de Winter J, Abbink D. Human-like driving behaviour emerges from a risk-based driver model. Nature Communications. 2020;11(1):4850. DOI: 10.1038/s41467-020-18353-4.
Park DE, Park SE. Factors affecting perceived safety and enjoyment based on driver experience. Transportation Research Part F: Traffic Psychology and Behaviour. 2021;83:148-163. DOI: 10.1016/j.trf.2021.10.006.
Robbins C, Chapman P. How does drivers’ visual search change as a function of experience? A systematic review and meta-analysis. Accident Analysis & Prevention. 2019;132:105266. DOI: 10.1016/j.aap.2019.105266.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Hui XU, Jianping WU
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.