This study investigated the main factors affecting the severity of injury to pedestrians in taxi–pedestrian crashes on urban arterial roads. Video data recorded by an in-car black box were used. Because the video data provided direct crash observation, they were more reliable than the crash data, and video images and speed profiles retrieved from the black box were advantageous for safety studies. For analysis of the black box data, this study defined new explanatory variables that affected injury severity; these variables could not have been identified by the conventional method, which was based on crash reports. A multiple-indicator and multiple-cause model was used to investigate the relationship between the explanatory variables and injury severity. A total of 484 taxi–pedestrian crash scenes over 2 years was used for the multivariate analysis in the city of Incheon, South Korea. The crash characteristics most strongly associated with increased crash severity were failure by the pedestrian to watch for approaching vehicles, jaywalking by the pedestrian, the pedestrian being elderly, excessive vehicle speed, failure by the driver to immediately stop, limited driver vision, and nighttime. This study emphasized the potential of individualized black box video recording data for crash severity analysis and investigation of the causal factors of crashes.
Get full access to this article
View all access options for this article.
References
1.
PedenM.World Report on Road Traffic Injury Prevention. World Health Organization, Geneva, 2004.
PoseyC., RobertsT. L., BenjaminP., and BennetR. J.. Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modelling Technique: A Tutorial and an Annotated Example. Communications of the Association for Information Systems, Vol. 36, Article11, 2015.
4.
HaleemK., AlluriP., and GanA.. Analyzing Pedestrian Crash Injury Severity at Signalized and Non-Signalized Locations. Accident Analysis and Prevention, Vol. 81, 2015, pp. 14–23. https://doi.org/10.1016/j.aap.2015.04.025.
5.
KoopmansJ. M., FriedmanL., KwonS., and SheehanK.. Urban Crash-Related Child Pedestrian Injury Incidence and Characteristics Associated with Injury Severity. Accident Analysis and Prevention, Vol. 77, 2015, pp. 127–136. https://doi.org/10.1016/j.aap.2015.02.005.
6.
NiebuhrT., JungeM., and RosenE.. Pedestrian Injury Risk and the Effect of Age. Accident Analysis and Prevention, Vol. 86, 2016, pp. 121–128. https://doi.org/10.1016/j.aap.2015.10.026.
7.
FontaineH., and GourletY.. Fatal Pedestrian Accidents in France: A Typological Analysis. Accident Analysis and Prevention, Vol. 29, No. 3, 1997, pp. 303–312. https://doi.org/10.1016/S0001-4575(96)00084-X.
8.
KimJ.-K., UlfarssonG. F., ShankarV. N., and ManneringF. L.. A Note on Modeling Pedestrian-Injury Severity in Motor-Vehicle Crashes with the Mixed Logit Model. Accident Analysis and Prevention, Vol. 42, No. 6, 2010, pp. 1751–1758. https://doi.org/10.1016/j.aap.2010.04.016.
9.
ZegeerC. V., StuttsJ. C., HuangH., ZhouM., and RodgmanE.. Analysis of Elderly Pedestrian Accidents and Recommended Countermeasures. Transportation Research Record, No. 1405, 1993, pp. 56–63.
10.
ZhangG., YauK. K., and ZhangX.. Analyzing Fault and Severity in Pedestrian–Motor Vehicle Accidents in China. Accident Analysis and Prevention, Vol. 73, 2014, pp. 141–150. https://doi.org/10.1016/j.aap.2014.08.018.
11.
AzizH. M. A., UkkusuriS. V., and HasanS.. Exploring the Determinants of Pedestrian–Vehicle Crash Severity in New York City. Accident Analysis and Prevention, Vol. 50, 2013, pp. 1298–1309. https://doi.org/10.1016/j.aap.2012.09.034.
12.
Al-GhamdiA. S.Pedestrian–Vehicle Crashes and Analytical Techniques for Stratified Contingency Tables. Accident Analysis and Prevention, Vol. 34, No. 2, 2002, pp. 205–214. https://doi.org/10.1016/S0001-4575(01)00015-X.
13.
ZajacS. S., and IvanJ. N.. Factors Influencing Injury Severity of Motor Vehicle–Crossing Pedestrian Crashes in Rural Connecticut. Accident Analysis and Prevention, Vol. 35, No. 3, 2003, pp. 369–379. https://doi.org/10.1016/S0001-4575(02)00013-1.
14.
Coding Manual: Motor Vehicle and Traffic Collision Analysis on Connecticut Streets and Highways. Connecticut Department of Transportation, Newington, 1986.
15.
ZhangJ., LindsayJ., ClarkeK., RobbinsG., and MaoY.. Factors Affecting the Severity of Motor Vehicle Traffic Crashes Involving Elderly Drivers in Ontario. Accident Analysis and Prevention, Vol. 32, No. 1, 2000, pp. 117–125. https://doi.org/10.1016/S0001-4575(99)00039-1.
16.
ShankarV. N., UlfarssonG. F., PendyalaR. M., and NebergallM. L. B.. Modeling Crashes Involving Pedestrians and Motorized Traffic. Safety Science, Vol. 41, No. 7, 2003, pp. 627–640. https://doi.org/10.1016/S0925-7535(02)00017-6.
17.
ChangL.-Y., and WangH.-W.. Analysis of Traffic Injury Severity: An Application of Non-Parametric Classification Tree Techniques. Accident Analysis and Prevention, Vol. 38, No. 5, 2006, pp. 1019–1027. https://doi.org/10.1016/j.aap.2006.04.009.
18.
KimJ.-K., UlfarssonG. F., ShankarV. N., and KimS.. Age and Pedestrian Injury Severity in Motor-Vehicle Crashes: A Heteroskedastic Logit Analysis. Accident Analysis and Prevention, Vol. 40, No. 5, 2008, pp. 1695–1702. https://doi.org/10.1016/j.aap.2008.06.005.
19.
ChoiJ.-S., KimS.-Y., HwangK.-S., and BaikS.-Y.. Severity Analysis of the Pedestrian Crash Patterns Based on the Ordered Logit Model. International Journal of Highway Engineering, Vol. 11, No. 1, 2009, pp. 153–164.
20.
NiehoffP., GablerH. C., BrophyJ., ChidesterC., HinchJ., and RaglandC.. Evaluation of Event Data Recorders in Full Systems Crash Tests. In Proceedings of the 19th International Technical Conference on the Enhanced Safety of Vehicles, NHTSA, U.S. Department of Transportation, 2005.
21.
BoonR.Post-Accident Analysis of Digital Sources for Traffic Accidents. University of Twente, Enschede, Netherlands, 2014.
22.
GablerH., HamptonC., and RostonT. A.. Estimating Crash Severity: Can Event Data Recorders Replace Crash Reconstruction? In Proceedings of the 18th International Technical Conference on the Enhanced Safety of Vehicles, NHTSA, U.S. Department of Transportation, 2003.
23.
WuB.-F., ChenY.-H., and YehC.-H.. Driving Behaviour-Based Event Data Recorder. IET Intelligent Transport Systems, Vol. 8, No. 4, 2014, pp. 361–367. https://doi.org/10.1049/iet-its.2013.0009.
24.
YoungC.-P., ChangB. R., and WeiT.-Y.. Highway Vehicle Accident Reconstruction Using Cooperative Collision Warning Based Motor Vehicle Event Data Recorder. In 2009 IEEE Intelligent Vehicles Symposium, IEEE, New York, 2009. https://doi.org/10.1109/IVS.2009.5164441.
25.
GermanA., ComeauJ.-L., MonkB., McClaffertyK. J., TiessenP. F., and ChanJ.. The Use of Event Data Recorders in the Analysis of Real-World Crashes. In Proceedings of the Canadian Multidisciplinary Road Safety Conference 12, 2001, pp. 10–13.
26.
IsmailK., SayedT., and SaunierN.. Automated Analysis of Pedestrian–Vehicle Conflicts: Context for Before-and-After Studies. Transportation Research Record: Journal of the Transportation Research Board, No. 2198, 2010, pp. 52–64. https://doi.org/10.3141/2198-07.
27.
TwaddleH., SchendzielorzT., FaklerO., and AminiS.. Use of Automated Video Analysis for the Evaluation of Bicycle Movement and Interaction. IS&T/SPIE Electronic Imaging, 2014.
28.
ChungY., and ChangI.. How Accurate Is Accident Data in Road Safety Research? An Application of Vehicle Black Box Data Regarding Pedestrian-to-Taxi Accidents in Korea. Accident Analysis and Prevention, Vol. 84, 2015, pp. 1–8. https://doi.org/10.1016/j.aap.2015.08.001.
VlahogianniE. I., KarlaftisM. G., PapageorgiouN., and TselentisD. I.. Factors Influencing Freeway Traffic Upstream of an Incident. Advances in Transportation Studies, Vol. 1, 2014, pp. 17–36.
31.
Di CiommoF., MonzonA., and Fernandez-HerediaA.. Improving the Analysis of Road Pricing Acceptability Surveys by Using Hybrid Models. Transportation Research Part A: Policy and Practice, Vol. 49, 2013, pp. 302–316.
32.
HuL., and BentlerP. M.. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling, Vol. 6, No. 1, 1999, pp. 1–55. https://doi.org/10.1080/10705519909540118.
