The Motorcycle Crash Causation Study (MCCS) is a matched case-control study that contains a very wide list of crash contributing factors associated with motorcycle crash occurrences. It contains information such as motorcycle information, rider information, and associated trip information. This study also provides crash narrative information that presents an in-depth narrative discussion of the crash causation. Because of the plethora of information, it is critical to investigate MCCS-related data. Some studies examined the structured information in MCCS datasets. There is no in-depth study that has examined the unstructured textual contents in the MCCS data. This study aims to mitigate this research gap by applying different natural language processing tools (e.g., text mining, topic modeling). Fatal and non-fatal crash narratives are clustered separately to gain insights pertaining to the injury level. The findings of this study will contribute to the ongoing studies on MCCS to better understand the crash causation mechanism associated with motorcycle crashes.
NazemetzJ. W.BentsF. D.PerryJ. G.ThorC.MohamedshahY. M.Motorcycle Crash Causation Study: Final Report. FHWA-HRT-18-064. FHWA, Washington D.C., 2019.
3.
ChawlaH.KaracaI.SavolainenP. T.Contrasting Crash- and Non-Crash-Involved Riders: Analysis of Data from the Motorcycle Crash Causation Study. Transportation Research Record: Journal of the Transportation Research Board, 2019. 2673: 122–131.
4.
HallmarkS.HsuY.-Y.MazeT.McDonaldT.FitzsimmonsE.Investigating Factors Contributing to Large Truck Lane Departure Crashes Using the Federal Motor Carrier Safety Administration’s Large Truck Crash Causation Study (LTCCS) Database. Center for Transportation Research and Education, Iowa State University, 2009.
5.
RybG. E.DischingerP. C.KuferaJ. A.KernsT. J.; Association for the Advancement of Automotive Medicine (AAAM). Trends in Motorcycle Injuries, Deaths and Case Fatality Rates in the U.S.A. Population. Annals of Advances in Automotive Medicine, Vol. 53, 2009, 2 p.
6.
WaliB.KhattakA. J.AhmadN.Examining Correlations between Motorcyclist’s Conspicuity, Apparel Related Factors and Injury Severity Score: Evidence from New Motorcycle Crash Causation Study. Accident Analysis & Prevention, Vol. 131, 2019: 45–62.
7.
National Highway Traffic Safety Administration (NHTSA). National Motor Vehicle Crash Causation Survey: Report to Congress. Publication DOT HS 811 059. United States Department of Transportation, Washington, D.C., 2008, p. 47.
8.
ChoiE.-H.Crash Factors in Intersection-Related Crashes: An On-Scene Perspective: (621942011-001). Publication HS-811 366. American Psychological Association, 2010.
9.
KostyniukL. P.NationA. D.Motorcycle Crash Trends in Michigan: 2001-2005. University of Michigan Transport Research Institute, Michigan, 2005, p. 50.
10.
MedinaA. M. F.SotoJ. C. T.; Institute of Transportation Engineers (ITE). Roadway Factors Associated with Motorcycle Crashes. ITE, Washington, D.C., 2011.
11.
EustaceD.IndupuruV. K.HoveyP.Identification of Risk Factors Associated with Motorcycle-Related Fatalities Ohio. Journal of Transportation Engineering, Vol. 137, No. 7, 2011, pp. 474–480.
12.
ChenZ.(David) FanW.A Multinomial Logit Model of Pedestrian-Vehicle Crash Severity in North Carolina. International Journal of Transportation Science and Technology, Vol. 8, No. 1, 2019, pp. 43–52. https://doi.org/10.1016/j.ijtst.2018.10.001.
13.
ShaheedM. S.GkritzaK.A Latent Class Analysis of Single-Vehicle Motorcycle Crash Severity Outcomes. Analytic Methods in Accident Research, Vol. 2, 2014, pp. 30–38.
14.
ChengW.GillG. S.SakraniT.DasuM.ZhouJ.Predicting Motorcycle Crash Injury Severity Using Weather Data and Alternative Bayesian Multivariate Crash Frequency Models. Accident Analysis & Prevention, Vol. 108, 2017, pp. 172–180.
15.
FaridA.RezapourM.KsaibatiK.Modeling Severities of Motorcycle Crashes on Low-Volume Roadways. Presented at the 12th International Conference on Low-Volume Roads. Transportation Research Board, MT, 2019.
16.
DasS.DuttaA.DixonK.Minjares-KyleL.GilletteG.Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2018. 2672: 122–134.
17.
Das.S.Fatal Crash Reporting in Media: A Case Study on Bangladesh. Presented at 100th Annual Meeting of theTransportation Research Board, 2021. (virtual).
18.
DasS.An Exploratory Analysis of Unmanned Aircraft Sightings using Text Mining. Transportation Research Record: Journal of Transportation Research Board, 2021. https://doi.org/10.1177/0361198120987230
19.
GuY.QianZ. S.ChenF.From Twitter to Detector: Real-Time Traffic Incident Detection Using Social Media Data. Transportation Research Part C: Emerging Technologies, Vol. 67, 2016, pp. 321–342.
20.
DasS.DuttaA.BrewerM.Case Study of Trend Mining in Transportation Research Record Articles. Transportation Research Record: Journal of Transportation Research Board, 2020. 2674: 1–14.
21.
ChenF.KrishnanR.Transportation Sentiment Analysis for Safety Enhancement. Technologies for Safe and Efficient Transportation. US DOT University Transportation Center, 2013.
22.
S.DasA.DuttaTwelve Years of Transportation Annual Meeting Hashtag: Implications for Networking and Research Trends. Presented at 100th Annual Meeting of theTransportation Research Board, 2021. (virtual). .
23.
DasS.SunX.DuttaA.Investigating User Ridership Sentiments for Bike Sharing Programs. Journal of Transportation Technologies, Vol. 5, 2015, pp. 69–74.
24.
SunX.DasS.User Sentiment Analysis with Louisiana Social Media Data for Better and Effective Crash Countermeasures. Report No. 14-4TIRE. 2015.
25.
DasS.SunX.DuttaA.ZupancichM.Twitter in Circulating Transportation Information: A Case Study on Two Cities. Presented at 96th Annual Meeting of the Transportation Research Board, Washington D.C., 2017.
26.
ZhangZ.NiM.HeQ.GaoJ.GouJ.LiX.Exploratory Study on Correlation between Twitter Concentration and Traffic Surges. Transportation Research Record: Journal of Transportation Engineering, 2016. 2553: 90–98.
27.
DasS.DuttaA.Characterizing Public Emotions and Sentiments in COVID-19 Environment: A Case Study of India. Journal of Human Behavior in the Social Environment, 2020. https://doi.org/10.1080/10911359.2020.1781015.
28.
DasS.ADuttaA.MudgalSDatta.Non-fear-based Road Safety Campaign as a Community Service: Contexts from Social Media. In Innovations for Community Services. I4CS 2020. Communications in Computer and Information Science (RautarayS.EichlerG.ErfurthC.FahrnbergerG., eds.) Vol 1139. Springer, New Mexico, 2020.
29.
DasS.DuttaA.MedinaG.Minjares-KyleL.ElgartZ.Extracting Patterns from Twitter to Promote Biking. IATSS Research, Vol. 43, 2019, pp. 51–59.
30.
BondyJ.LipscombH.GuariniK.GlaznerJ. E.Methods for Using Narrative Text from Injury Reports to Identify Factors Contributing to Construction Injury. American Journal of Industrial Medicine, 2005, 48: 373–380.
31.
BunnT. L.SlavovaS.HallL.Narrative Text Analysis of Kentucky Tractor Fatality Reports. Accident Analysis & Prevention, Vol. 40, 2008, pp. 419–425.
32.
WilliamsonA.FeyerA. M.StoutN.DriscollT.UsherH.Use of Narrative Analysis for Comparisons of the Causes of Fatal Accidents in Three Countries: New Zealand, Australia, and the United States. Injury Prevention, Vol. 7, 2001, pp. 15–20.
33.
ChenW.WheelerK. K.LinS.HuangY.XiangH.Computerized “Learn-As-You-Go” Classification of Traumatic Brain Injuries Using NEISS Narrative Data. Accident Analysis & Prevention, Vol. 89, 2016, pp. 111–117.
34.
Marucci-WellmanH. R.LehtoM. R.CornsH. L.A Practical Tool for Public Health Surveillance: Semi-Automated Coding of Short Injury Narratives from Large Administrative Databases Using Naïve Bayes Algorithms. Accident Analysis & Prevention, Vol. 84, 2015, pp. 165–176.
35.
WangZ.ShahA. D.TateA. R.DenaxasS.Shawe-TaylorJ.HemingwayH.Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning. PLoS One, Vol. 7, 2012, pp. e30412.
36.
ChatterjeeS.A Connectionist Approach for Classifying Accident Narratives. Theses and dissertations. Available from ProQuest, 1998, pp. 1–345.
37.
DasS.MudgalA.DuttaA.GeedipallyS. R.Vehicle Consumer Complaint Reports Involving Severe Incidents: Mining Large Contingency Tables. Transportation Research Record: Journal of the Transportation Research Board, 2018. 2672: 122–134.
38.
BeanlandV.FitzharrisM.YoungK. L.LennéM. G.Driver Inattention and Driver Distraction in Serious Casualty Crashes: Data from the Australian National Crash In-Depth Study. Accident Analysis & Prevention, Vol. 54, 2013, pp. 99–107.
39.
DasS.LeM.DaiB.Application of Machine Learning Tools in Classifying Pedestrian Crash Types: A Case Study. Transportation Safety and Environment, Vol. 2, No. 2, 2020, pp. 106–110.
40.
FitzpatrickC. D.RakasiS.KnodlerM. A.An Investigation of the Speeding-Related Crash Designation through Crash Narrative Reviews Sampled via Logistic Regression. Accident Analysis & Prevention, Vol. 98, 2017, pp. 57–63.
41.
GravesJ. M.WhitehillJ. M.HagelB. E.RivaraF. P.Making the Most of Injury Surveillance Data: Using Narrative Text to Identify Exposure Information in Case-Control Studies. Injury, Vol. 46, 2015, pp. 891–897.
42.
NayakR.PiyatrapoomiN.WeligamageJ.Application of Text Mining in Analysing Road Crashes for Road Asset Management. In Engineering Asset Lifecycle Management (KiritsisD.EmmanouilidisC.KoroniosA.MathewJ. eds.), Springer, London, 2010, pp. 49–58.
43.
FouldsJ.BoylesL.DuBoisC.SmythP.WellingM.Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation. Proc., 19th International Conference on Knowledge Discovery and Data Mining ACM SIGKDD, Chicago, IL, 2013, pp. 446–454.
44.
BleiD. M.NgA. Y.JordanM. I.Latent Dirichlet Allocation. Journal of Machine Learning Research, Vol. 3, 2003, pp. 993–1022.
45.
HoffmanM. D.BleiD. M.WangC.JohnP.Stochastic Variational Inference. Journal of Machine Learning Research, Vol. 14, 2013, pp. 1303–1347.
46.
SilgeJ.RobinsonD.Tidytext: Text Mining and Analysis Using Tidy Data Principles in R. Journal of Open Source Software, Vol. 1, 2016, p. 37.
47.
CramerN.RoseS.EngelD.CowleyW.Automatic Keyword Extraction from Individual Documents. John Wiley & Sons, Ltd., Hoboken, NJ, 2010.
48.
WijffelsJ.Udpipe: Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the ‘UDPipe’‘NLP’ Toolkit. R Package Version 0.8.2, 2019.
