Police-reported crash data are essential for transportation safety analysis, but their utility is often hampered by issues including underreporting and inconsistencies. Comparing crash videos with police reports provides a valuable method for identifying these discrepancies and improving data accuracy. This study identifies discrepancies in police reports by comparing them to crash videos. Iowa crash reports were compared to videos from the Iowa Advanced Traffic Management System (ATMS) for 2022. A spatiotemporal matching approach was used. Key crash elements were manually reduced from corresponding videos and compared to those from the corresponding crash reports. The accuracy of each element, along with its 95% confidence interval, was computed. Crash elements observable on-site, such as environmental conditions, light conditions, and vehicle damage, demonstrated high accuracy levels. Elements requiring inference and reliance on witness accounts, such as the manner of crash or collision, the first harmful event, and the sequence of events during the incident, showed lower accuracy and greater variability. The results highlight inconsistencies in derived elements, particularly the major cause of the crash, which relies on the accuracy of the elements it is derived from. These findings underscore the benefit of incorporating video data into crash data workflows to enhance reporting accuracy and reliability. Policymakers and safety professionals can use the insights on these inconsistencies to improve traffic safety measures and make informed decisions. Additionally, outcomes can be used to help train officers in coding crash reports. This study highlights the importance of developing systematic methods to integrate multiple data sources for improved traffic safety analytics.
Imprialou, M. and Quddus, M. (2019) Crash Data Quality for Road Safety Research: Current State and Future Directions. Accident Analysis & Prevention, 130, 84-90. https://doi.org/10.1016/j.aap.2017.02.022
[3]
Ahmed, A., Sadullah, A.F.M. and Yahya, A.S. (2019) Errors in Accident Data, Its Types, Causes and Methods of Rectification-Analysis of the Literature. Accident Analysis & Prevention, 130, 3-21.
[4]
Abay, K.A. (2015) Investigating the Nature and Impact of Reporting Bias in Road Crash Data. Transportation Research Part A: Policy and Practice, 71, 31-45. https://doi.org/10.1016/j.tra.2014.11.002
[5]
Wood, J.S., Donnell, E.T. and Fariss, C.J. (2016) A Method to Account for and Estimate Underreporting in Crash Frequency Research. Accident Analysis & Prevention, 95, 57-66. https://doi.org/10.1016/j.aap.2016.06.013
[6]
Zeng, Q., Wen, H., Huang, H., Wang, J. and Lee, J. (2020) Analysis of Crash Frequency Using a Bayesian Underreporting Count Model with Spatial Correlation. Physica A: Statistical Mechanics and Its Applications, 545, Article 123754. https://doi.org/10.1016/j.physa.2019.123754
[7]
Janstrup, K., Kaplan, S., Barfod, M.B. and Prato, C.G. (2017) Evaluating the Police Service Quality for Handling Traffic Crash Reporting: A Combined MCDA and LCA Approach. Policing: An International Journal of Police Strategies & Management, 40, 410-425. https://doi.org/10.1108/pijpsm-03-2016-0032
[8]
Hauer, E. and Hakkert, S. (1988) Extent and Some Implications of Incomplete Accident Reporting. Transportation Research Record. https://onlinepubs.trb.org/Onlinepubs/trr/1988/1185/1185-001.pdf
[9]
Farmer, C.M. (2003) Reliability of Police-Reported Information for Determining Crash and Injury Severity. Traffic Injury Prevention, 4, 38-44. https://doi.org/10.1080/15389580309855
[10]
Milne, R., Nunan, J., Hope, L., Hodgkins, J. and Clarke, C. (2022) From Verbal Account to Written Evidence: Do Written Statements Generated by Officers Accurately Represent What Witnesses Say? Frontiers in Psychology, 12, Article 774322. https://doi.org/10.3389/fpsyg.2021.774322
[11]
Hanley, P.F. and Sikka, N. (2012) Bias Caused by Self-Reporting Distraction and Its Impact on Crash Estimates. Accident Analysis & Prevention, 49, 360-365. https://doi.org/10.1016/j.aap.2012.02.008
[12]
Regev, S., Rolison, J.J., Feeney, A. and Moutari, S. (2017) Driver Distraction Is an Under-Reported Cause of Road Accidents: An Examination of Discrepancy between Police Officers’ Views and Road Accident Reports. 2017 Proceedings of the 5th International Conference on Driver Distraction and Inattention, Paris, 20-22 March 2017, 1-21.
[13]
Kamaluddin, N.A., Andersen, C.S., Larsen, M.K., Meltofte, K.R. and Várhelyi, A. (2018) Self-Reporting Traffic Crashes—A Systematic Literature Review. European Transport Research Review, 10, Article No. 26. https://doi.org/10.1186/s12544-018-0301-0
[14]
Bailey, L. and Huft, D. (2008) Improving Crash Reporting: Study of Crash Reporting Practice on Nine Indian Reservations. Transportation Research Record, 2008, 72-79.
[15]
Dopfer, A. and Wang, C. (2013) What Can We Learn from Accident Videos? 2013 CACS International Automatic Control Conference (CACS), Nantou, 2-4 December 2013, 68-73. https://doi.org/10.1109/cacs.2013.6734109
[16]
Van Der Horst, R. (2013) Video-Recorded Accidents, Conflicts and Road User Behaviour: A Step forward in Traffic Safety Research. 20th ICTCT Workshop Proceedings Session IV Methodological Issues 1 Video-Recorded Accidents, Conflicts and Road User Behaviour: A Step Forward in Traffic Safety Research, Valencia, 25-26 October 2007, 69-80.
[17]
Goodall, N. and Lee, E. (2019) Comparison of Waze Crash and Disabled Vehicle Records with Video Ground Truth. Transportation Research Interdisciplinary Perspectives, 1, Article 100019. https://doi.org/10.1016/j.trip.2019.100019
[18]
Alsop, J. and Langley, J. (2001) Under-Reporting of Motor Vehicle Traffic Crash Victims in New Zealand. Accident Analysis & Prevention, 33, 353-359. https://doi.org/10.1016/s0001-4575(00)00049-x
[19]
Ye, F. and Lord, D. (2011) Investigation of Effects of Underreporting Crash Data on Three Commonly Used Traffic Crash Severity Models. Transportation Research Record: Journal of the Transportation Research Board, 2241, 51-58. https://doi.org/10.3141/2241-06
[20]
Yasmin, S. (2015) Advanced Econometric Approaches to Modeling Driver Injury Severity. McGill University.
[21]
Patil, S., Geedipally, S.R. and Lord, D. (2012) Analysis of Crash Severities Using Nested Logit Model—Accounting for the Underreporting of Crashes. Accident Analysis & Prevention, 45, 646-653. https://doi.org/10.1016/j.aap.2011.09.034
[22]
Amoros, E., Martin, J. and Laumon, B. (2006) Under-Reporting of Road Crash Casualties in France. Accident Analysis & Prevention, 38, 627-635. https://doi.org/10.1016/j.aap.2005.11.006
[23]
Cryer, P.C., Westrup, S., Cook, A.C., Ashwell, V., Bridger, P. and Clarke, C. (2001) Investigation of Bias after Data Linkage of Hospital Admissions Data to Police Road Traffic Crash Reports. Injury Prevention, 7, 234-241. https://doi.org/10.1136/ip.7.3.234
[24]
TraCS—Iowa Department of Transportation. https://iowadot.gov/tracs
[25]
Clopper, C.J. and Pearson, E.S. (1934) The Use of Confidence or Fiducial Limits Illustrated in the CASE of the Binomial. Biometrika, 26, 404-413. https://doi.org/10.1093/biomet/26.4.404
[26]
Thulin, M. (2014) The Cost of Using Exact Confidence Intervals for a Binomial Proportion. Electron Journal of Statististics, 8, 817-840. http://arxiv.org/abs/1303.1288
