Leveraging autonomous vehicles crash narratives to understand the patterns of parking-related crashes
DOI:
https://doi.org/10.55329/fiqq8731Keywords:
autonomous vehicles, crash narratives, machine learning, parking-related crashes, text networkAbstract
Autonomous vehicles (AVs) parking has been a subject of interest from various researchers; however, the focus has been on the parking demand, algorithm, and policies, while the safety aspect has received less attention, perhaps due to the lack of AV crash data. This study evaluated the magnitude and pattern of AV parking-related crashes that occurred between January 2017 and August 2022 in California. The study applied descriptive analysis, unsupervised text mining, and supervised text mining (Support Vector Machine, Naïve Bayes, Logitboost, Random Forest, and Neural network) with resampling techniques. It was indicated that parking-related crashes constitute about 16% of all AV crashes, most of them are likely to impact the AV on the rear or left side. The unsupervised text mining results showed that AVs in the conventional mode of operation, reversing, and parallel parking are among the key themes associated with parking-related crashes. The Support Vector Machine, Logitboost, Random Forest, and Neural network showed relatively high prediction accuracy. The important features from these supervised text mining approaches were conventional mode, reservsing, passenger vehicle, parallel parking, which confirm the preliminary findings in the unsupervised text mining. The implications of the findings to operators and policymakers are included in the study. Findings from this paper could be used to introduce measures to reduce AV parking-related crashes.
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Author Biographies
Boniphace Kutela, Texas A&M Transportation Institute, the United States of America
Boniphace Kutela is an Associate Research Engineer at the Texas A&M Transportation Institute (TTI). He holds a Ph.D. in Transportation Engineering from University of Nevada Las Vegas. Dr. Kutela has over nine years of research experience in traffic safety and operations, intelligent transportation systems, railroad network analysis and safety, and connected and autonomous vehicles. His major areas of expertise include Roadway Data Analysis Methodologies, Machine Learning, Predictive Modeling, and Natural Language Processing with a focus on traffic safety and operations.
CRediT contribution: Conceptualization, Methodology, Data curation, Investigation, Writing—original draft.
Richard Dzinyela, Texas A&M University, the United States of America
Richard Dzinyela is a graduate student in Texas A&M University, college Station. He holds a bachelor’s degree in civil engineering from Kwame Nkrumah University of Science and Technology, Ghana. Mr. Dzinyela has over three years of research experience in traffic safety and operations. His major areas of expertise include crash data modeling using Econometric, Spatial and Machine Learning Models.
CRediT contribution: Conceptualization, Methodology, Data curation, Investigation, Writing—original draft.
Henrick Haule, University of Arizona, the United States of America
Henrick Haule is a Research Assistant Professor at the University of Arizona (UA) and a Manager for the UA Center for Applied Transportation Sciences (CATS). He worked as Postdoctoral Research Fellow at Florida Atlantic University. He received his Ph.D. in Civil Engineering from Florida International University (FIU), an M.S. in Civil Engineering from the University of North Florida, and a B.S. in Civil Engineering from the University of Dar es Salaam in Tanzania. His research focuses on Transportation Systems Management and Operations (TSMO) strategies, traffic incident management, highway safety and crash modeling, and applications of real-time traffic data in transportation.
CRediT contribution: Data curation, Investigation, Writing—original draft.
Abbas Sheykhfard, Babol Noshirvani University of Technology, Islamic Republic of Iran
Abbas Sheykhfard is a researcher at the Department of Civil Engineering, Babol Noshirvani University of Technology, Iran. He received his Ph.D. degree in road and transportation engineering at the same university in 2021. He carried out part of his Ph.D. thesis work at Delft University of Technology, the Netherlands. His research interests include road safety and road user behavioral analysis.
CRediT statement: Writing—original draft.
Kelvin Msechu, Atkins North America Inc., the United States of America
Kelvin Msechu is an Intelligent Transportation Systems (ITS)/Traffic Engineer at Atkins North America Inc. He holds a Master of Civil Engineering from the University of Tennessee at Chattanooga. Kelvin’s areas of interests and experience include roadway safety, drone deployment for roadway data collection and safety analysis, traffic, and ITS designs. At Atkins, Kelvin has reviewed hundreds of police narratives on roadway crash reports, he has performed numerous traffic mobility analyses, ITS inspections, and traffic signal design. Kelvin is also involved in journal publications of existing roadway safety issues and emerging technologies.
CRediT contribution: Data curation, Investigation, Writing—original draft.
References
Arteaga, C., A. Paz, J. W. Park (2020), 'Injury severity on traffic crashes: A text mining with an interpretable machine-learning approach', Safety Science, 132, 104988. DOI: https://doi.org/10.1016/j.ssci.2020.104988
Ashraf, M. T., K. Dey, S. Mishra, M. T. Rahman (2021), 'Extracting Rules from Autonomous-Vehicle-Involved Crashes by Applying Decision Tree and Association Rule Methods', Transportation Research Record: Journal of the Transportation Research Board, 2675(11), 522–533. DOI: https://doi.org/10.1177/03611981211018461
Bahrami, S., M. Roorda (2022), 'Autonomous vehicle parking policies: A case study of the City of Toronto', Transportation Research Part A: Policy and Practice, 155, 283–296. DOI: https://doi.org/10.1016/j.tra.2021.11.003
Benoit, K., K. Watanabe, H. Wang, P. Nulty, A. Obeng, S. Müller, A. Matsuo (2018), 'quanteda: An R package for the quantitative analysis of textual data', Journal of Open Source Software, 3(30), 774. DOI: https://doi.org/10.21105/joss.00774
Blaheta, D., M. Johnson (2011), 'Unsupervised learning of multi-word verbs', Proceedings of the ACL Workshop on Collocations, 54–60, https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=6bb607eea1875141b4fa89f2c2d361335026c592.
Boggs, A. M., B. Wali, A. J. Khattak (2020), 'Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach', Accident Analysis & Prevention, 135, 105354. DOI: https://doi.org/10.1016/j.aap.2019.105354
Chan, T. K., C. S. Chin, Z. Vale, J. Ball, M. Ricco (2021), 'Review of Autonomous Intelligent Vehicles for Urban Driving and Parking', Electronics 2021, 10(9), 1021. DOI: https://doi.org/10.3390/electronics10091021
Chen, H., H. Chen, R. Zhou, Z. Liu, X. Sun (2021), 'Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning', Mathematical Problems in Engineering, 2021, 1–10. DOI: https://doi.org/10.1155/2021/5524356
Das, S., A. Dutta, I. Tsapakis (2020), 'Automated vehicle collisions in California: Applying Bayesian latent class model', IATSS Research, 44(4), 300–308. DOI: https://doi.org/10.1016/j.iatssr.2020.03.001
DMV, (n.d), 'Autonomous Vehicle Collision Report', State of California, Department of Motor Vehicles, https://www.dmv.ca.gov/portal/vehicle-industry-services/autonomous-vehicles/autonomous-vehicle-collision-reports/, accessed 2023-06-30.
Favarò, F., S. Eurich, N. Nader (2018), 'Autonomous vehicles’ disengagements: Trends, triggers, and regulatory limitations', Accident Analysis & Prevention, 110, 136–148. DOI: https://doi.org/10.1016/j.aap.2017.11.001
Gao, L., P. Lu, Y. Ren (2021), 'A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents', Reliability Engineering & System Safety, 216, 108019. DOI: https://doi.org/10.1016/j.ress.2021.108019
Hsieh, M. F., . U. Özguner (2008), 'A parking algorithm for an autonomous vehicle', 2008 IEEE Intelligent Vehicles Symposium, IEEE, Eindhoven, Netherlands, 4 - 6 June 2008.
Hunter, S. (2014), 'A Novel Method of Network Text Analysis', Open Journal of Modern Linguistics, 04(02), 350–366. DOI: https://doi.org/10.4236/ojml.2014.42028
Jiang, C., C. R. Bhat, W. H. K. Lam (2020), 'A bibliometric overview of Transportation Research Part B: Methodological in the past forty years (1979-2019)', Transportation Research Part B: Methodological, 138, 268–291. DOI: https://doi.org/10.1016/j.trb.2020.05.016
Joachims, T. (1998), ' Text categorization with Support Vector Machines: Learning with many relevant features', Machine Learning: ECML-98, Springer Link, 137-142. DOI: https://doi.org/10.1007/BFb0026683
Khattak, Z. H., M. D. Fontaine, B. L. Smith (2020), 'Exploratory Investigation of Disengagements and Crashes in Autonomous Vehicles Under Mixed Traffic: An Endogenous Switching Regime Framework', IEEE Transactions on Intelligent Transportation Systems, 22(12), 7485–7495. DOI: https://doi.org/10.1109/TITS.2020.3003527
Kitali, A. E., P. Alluri, T. Sando, W. Wu (2019), 'Identification of Secondary Crash Risk Factors using Penalized Logistic Regression Model', Transportation Research Record: Journal of the Transportation Research Board, 2673(11), 901–904. DOI: https://doi.org/10.1177/0361198119849053
Kutela, B., S. Das, B. Dadashova (2022), 'Mining patterns of autonomous vehicle crashes involving vulnerable road users to understand the associated factors', Accident Analysis & Prevention, 165, 106473. DOI: https://doi.org/10.1016/j.aap.2021.106473
Kutela, B., C. Kadeha, R. T. Magehema, R. E. Avelar, P. Alluri (2023), 'Leveraging text mining approach to explore research roadmap and future direction of wrong-way driving crash studies. Data and Information Management', Data and Information Management, 100044. DOI: https://doi.org/10.1016/j.dim.2023.100044
Kutela, B., N. Langa, S. Mwende, E. Kidando, A. E. Kitali, P. Bansal (2021), 'A text mining approach to elicit public perception of bike-sharing systems', Travel Behaviour and Society, 24, 113–123. DOI: https://doi.org/10.1016/j.tbs.2021.03.002
Kutela, B., R. T. Magehema, N. Langa, F. Steven, R. J. Mwekh’iga (2022), 'A comparative analysis of followers’ engagements on bilingual tweets using regression-text mining approach. A case of Tanzanian-based airlines', International Journal of Information Management Data Insights, 2(2), 100123. DOI: https://doi.org/10.1016/j.jjimei.2022.100123
Kutela, B., N. Novat, N. Langa (2021), 'Exploring geographical distribution of transportation research themes related to COVID-19 using text network approach', Sustainable Cities and Society, 67, 102729. DOI: https://doi.org/10.1016/j.scs.2021.102729
Kwayu, K. M., V. Kwigizile, J. Zhang, O. Jun-Seok (2020), 'Semantic N-Gram Feature Analysis and Machine Learning-Based Classification of Drivers’ Hazardous Actions at Signal-Controlled Intersections', Journal of Computing in Civil Engineering, 34(4). DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000895
Lee, C. K., C. L. Lin, B. M. Shiu (2009), 'Autonomous Vehicle Parking Using Hybrid Artificial Intelligent Approach', Journal of Intelligent and Robotic Systems, 56(3), 319–343. DOI: https://doi.org/10.1007/s10846-009-9319-9
Lee, S., R. Arvin, A. J. Khattak (2023), 'Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis', Accident Analysis & Prevention, 181, 106932. DOI: https://doi.org/10.1016/j.aap.2022.106932
Liu, Q., X. Wang, W. Xiangbin, Y. Glaser, H. Linjia (2021), 'Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology', Accident Analysis & Prevention, 159, 106281. DOI: https://doi.org/10.1016/j.aap.2021.106281
Mahdavian, A., A. Shojaei, A. Oloufa (2019), 'Assessing the long-and mid-term effects of connected and automated vehicles on highways', International Conference on Sustainable Infrastructure 2019: Leading Resilient Communities through the 21st Century, Los Angeles, CA, Nov 6-9, pp. 263-273. DOI: https://doi.org/10.1061/9780784482650.027
Morando, M. M., Q. Tian, L. T. Truong, V. H L (2018), 'Studying the safety impact of autonomous vehicles using simulation-based surrogate safety measures', Journal of Advanced Transportation, 6135183. DOI: https://doi.org/10.1155/2018/6135183
Morris, C., J. J. Yang (2021), 'Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling', Accident Analysis & Prevention, 159, 106240. DOI: https://doi.org/10.1016/j.aap.2021.106240
Mousavi, M., S. Lord, D. Dadashova, B. Mousavi, S. (2020), 'Can Autonomous vehicles enhance traffic safety at unsignalized intersections?', International Conference on Transportation and Development 2020, Seattle, Washington, USA, 26–29 May 2020.
Mousavi, S. M., O. A. Osman, D. Lord, K. K. Dixon, B. Dadashova (2021), 'Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial', Accident Analysis & Prevention, 152, 105982. DOI: https://doi.org/10.1016/j.aap.2021.105982
Mujalli, R. O., G. López, L. Garach (2016), 'Bayes classifiers for imbalanced traffic accidents datasets', Accident Analysis & Prevention, 88, 37–51. DOI: https://doi.org/10.1016/j.aap.2015.12.003
Nakrani, N. M., M. M. Joshi (2022), 'A human-like decision intelligence for obstacle avoidance in autonomous vehicle parking', Applied Intelligence, 52(4), 3728–3747. DOI: https://doi.org/10.1007/s10489-021-02653-3
NHSTA, (2022), 'AV TEST Initiative | Automated Vehicle Tracking Tool', https://www.nhtsa.gov/automated-vehicle-test-tracking-tool, accessed 2023-06-24.
Novat, N., E. Kidando, B. Kutela, A. E. Kitali (2023), 'A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic inferences', Journal of Safety Research, 84, 251–260. DOI: https://doi.org/10.1016/j.jsr.2022.11.001
Paranyushkin, D. (2012), 'Visualization of Text’s Polysingularity Using Network Analysis', NODUS LABS, https://noduslabs.com/research/visualization-text-polysingularity-network-analysis/, accessed 2023-06-27.
Parsa, A. B., R. Shabanpour, A. Mohammadian, J. Auld, T. Stephens (2021), 'A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow', Transportation Letters, 13(10), 687–695. DOI: https://doi.org/10.1080/19427867.2020.1776956
Pokorny, P., A. Høye (2022), 'Descriptive analysis of reports on autonomous vehicle collisions in California: January 2021–June 2022', Traffic Safety Research, 2, 000011. DOI: https://doi.org/10.55329/xydm4000
Pranckevičius, T., V. Marcinkevičius (2017), 'Comparison of Naïve Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification', Baltic Journal of Modern Computing, 5(2), 221–232. DOI: https://doi.org/10.22364/bjmc.2017.5.2.05
Ren, W., B. Yu, Y. Chen, K. Gao (2022), 'Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach', International Journal of Environmental Research and Public Health, 19(18), 11358. DOI: https://doi.org/10.3390/ijerph191811358
Schoettle, B., M. Sivak (2018), 'A Preliminary Analysis of Real-World Crashes involving Self-Driving Vehicles', Transportation Research Institute, The University of Michigan, UMTRI-2015-34, http://websites.umich.edu/~umtriswt/PDF/UMTRI-2015-34.pdf.
Song, Y., M. Chitturi, D. A. Noyce (2021), 'Automated vehicle crash sequences: Patterns and potential uses in safety testing', Accident Analysis & Prevention, 153, 106017. DOI: https://doi.org/10.1016/j.aap.2021.106017
Statistica, (2022), 'Worldwide - AV market penetration 2030', https://www.statista.com/statistics/875080/av-market-penetration-worldwide-forecast/, accessed 2023-06-24.
Xu, C., Z. Ding, C. Wang, Z. Li (2019), 'Statistical analysis of the patterns and characteristics of connected and autonomous vehicle involved crashes', Journal of Safety Research, 71, 41–47. DOI: https://doi.org/10.1016/j.jsr.2019.09.001
Yuan, J., M. Abdel-Aty, Y. Gong, Q. Cai (2019), 'Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network', Transportation Research Record: Journal of the Transportation Research Board, 2673(4), 314–326. DOI: https://doi.org/10.1177/0361198119840611
Zhou, B., A. M. Roshandeh, S. Zhang, Z. Ma (2016), 'Analysis of Factors Contributing to Hit-and-Run Crashes Involved with Improper Driving Behaviors', Procedia Engineering, 137, 554–562. DOI: https://doi.org/10.1016/j.proeng.2016.01.292
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Copyright (c) 2023 Boniphace Kutela, Richard Dzinyela, Henrick Haule, Abbas Sheykhfard, Kelvin Msechu
This work is licensed under a Creative Commons Attribution 4.0 International License.
