Difference between reported crash time and speed disturbances in urban signalized intersections: a case study in Fortaleza-Brazil
DOI:
https://doi.org/10.14295/transportes.v28i5.2290Keywords:
Road safety, Speed disturbance, Reported crash time, Crash time estimationAbstract
The advent of new technologies for monitoring and controlling traffic allows the development of more robust road safety studies, based on the collection of disaggregated traffic data at intervals of 1 to 15 minutes or even in real time. However, in order to relate the crashes to their precursor conditions, applying this type of data requires a better knowledge on the accuracy of crash reported times. This work aims to present an analysis between the reported times of crashes and disturbances in the traffic flow conditions in urban signalized intersections in Fortaleza, Brazil. Vehicle flow disturbances were detected from speed oscillations by using an algorithm based on comparing speeds between “typical” and “crash” conditions and by validating the detections with visual analysis. The results of investigating 291 crashes showed an average difference of 20 minutes (sd = 23 min) between the reported time of crash and the occurrence of the speed disturbance. This is an indication that, while developing road safety disaggregated analyses, one should examine the database accuracy regarding the crash reported time.
Downloads
References
Abdel-Aty, M.; H. M. Hassan; M. Ahmed and A. S. Al-Ghamdi (2012) Real-time prediction of visibility related crashes. Transportation Research Part C: Emerging Technologies, v. 24, p. 288–298. doi:10.1016/j.trc.2012.04.001
Abdel-Aty, M. and A. Pande (2005) Identifying crash propensity using specific traffic speed conditions. Journal of Safety Research, v. 36, n. 1, p. 97–108. doi:10.1016/j.jsr.2004.11.002
Christoforou, Z.; S. Cohen and M. G. Karlaftis (2011) Identifying crash type propensity using real-time traffic data on freeways. Journal of Safety Research, v. 42, n. 1, p. 43–50. doi:10.1016/j.jsr.2011.01.001
Cunto, F. J. C.; M. M. Castro Neto and D. S. Barreira (2011) Modelos de Previsão de Acidentes de Trânsito em Interseções Semaforizadas de Fortaleza. Transportes, v. 20, n. 2, p. 55–62. DOI: https://doi.org/10.4237/transportes.v20i2.558
Day, C. M. and D. M. Bullock (2017) Investigation of self-organizing traffic signal control with graphical signal performance measures. Transportation Research Record, v. 2620, p. 69–82. doi:10.3141/2620-07
Essa, M. and T. Sayed (2019) Full Bayesian conflict-based models for real time safety evaluation of signalized intersections. Accident Analysis and Prevention, v. 129, p. 367–381. doi:10.1016/j.aap.2018.09.017
Feng, Y.; M. Zamanipour; K. L. Head and S. Khoshmagham (2016) Connected vehicle-based adaptive signal control and applications. Transportation Research Record, v. 2558, p. 11–19. doi:10.3141/2558-02
Golob, T. F.; W. Recker and Y. Pavlis (2008) Probabilistic models of freeway safety performance using traffic flow data as predictors. Safety Science, v. 46, n. 9, p. 1306–1333. doi:10.1016/j.ssci.2007.08.007
Hauer, E. (2004) Statistical Road Safety Modeling. Transportation Research Record: Journal of the Transportation Research Board, v. 1897, p. 81–87. doi:10.3141/1897-11
Hojati, A. T.; L. Ferreira; S. Washington; P. Charles and A. Shobeirinejad (2014) Modelling total duration of traffic incidents including incident detection and recovery time. Accident Analysis and Prevention, v. 71, p. 296–305. doi:10.1016/j.aap.2014.06.006
Huang, Z.; Z. Gao; R. Yu; X. Wang and K. Yang (2017) Utilizing latent class logit model to predict crash risk. Proceedings - 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017, p. 161–165. doi:10.1109/ICIS.2017.7959987
Imprialou, M. I. M.; M. Quddus; D. E. Pitfield and D. Lord (2016) Re-visiting crash-speed relationships: A new perspective in crash modelling. Accident Analysis and Prevention. v. 86, p. 173-185. doi:10.1016/j.aap.2015.10.001
Lee, C.; M. Abdel-Aty and L. Hsia (2006) Potential Real-Time Indicators of Sideswipe Crashes on Freeways. Transportation Research Record, v. 1953, n. 6, p. 41–49. doi:10.3141/1953-05
Lee, C.; B. Hellinga and F. Saccomanno (2003) Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic. Transportation Research Record: Journal of the Transportation Research Board, v. 1840, p. 67–77. doi:10.3141/1840-08
Lee, C.; F. Saccomanno and B. Hellinga (2002) Analysis of Crash Precursors on Instrumented Freeways. Transportation Research Record, v. 1784, n. 1, p. 1–8. doi:10.3141/1784-01
Pande, A. and M. Abdel-Aty (2006) Comprehensive Analysis of the Relationship Between Real-Time Traffic Surveillance Data and Rear-End Crashes on Freeways. Transportation Research Record, v. 1953, p. 31–40. doi:10.3141/1953-04
Pirdavani, A.; E. De Pauw; T. Brijs; S. Daniels; M. Magis; T. Bellemans and G. Wets (2015) Application of a Rule-Based Approach in Real-Time Crash Risk Prediction Model Development Using Loop Detector Data. Traffic Injury Prevention, v. 16, n. 8, p. 786–791. DOI: 10.1080/15389588.2015.1017572
Qin, X.; J. N. Ivan; N. Ravishanker and J. Liu (2005) Hierarchical Bayesian Estimation of Safety Performance Functions for Two-Lane Highways Using Markov Chain Monte Carlo Modeling. Journal of Transportation Engineering, v. 131, n. 5, p. 345–351. doi:10.1061/(ASCE)0733-947X(2005)131:5(345)
Quddus, M. A.; C. Wang and G. S. Ison (2010) Road Traffic Congestion and Crash Severity: Econometric Analysis Using Ordered Response Models. Journal of Transportation Engineering, v. 136, n. 5, p. 424-435. doi:10.1061/(ASCE)TE.1943-5436.0000044
Roshandel, S.; Z. Zheng and S. Washington (2015) Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis. Accident Analysis and Prevention, v. 79, p. 198–211. doi:10.1016/j.aap.2015.03.013
Shi, Q. and M. Abdel-Aty (2015) Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C: Emerging Technologies, v. 58, p. 380–394. doi:10.1016/j.trc.2015.02.022
Solomon, D. (1964) Crashes on main rural highways related to speed, driver and vehicle. Bureau of Public Roads.
Stempfel, J.; S. I. Guler; M. Menéndez and W. M. Brucks (2016) Effects of urban congestion on safety of networks. Journal of Transportation Safety & Security, v. 8, n. 3, p. 214–229. doi:10.1080/19439962.2015.1007193
Taylor, C. and D. Meldrum (2000) Evaluation of a fuzzy logic ramp metering algorithm: a comparative study among three ramp metering algorithms used in the greater Seattle area. WA-RD Technical Report no. 481.2. Federal Highway Administration.
USDOT (2014) Intelligent Transportation Systems (ITS) Strategic Plan 2015-2019. Report no. FHWA-JPO-14-145. US Department of Transportation.
Yang, B. Z; and B. P. Y. Loo (2016) Land use and traffic collisions: A link-attribute analysis using Empirical Bayes method. Accident Analysis and Prevention, v. 95, p. 236–249. doi:10.1016/j.aap.2016.07.002
Zheng, Z. (2012) Empirical Analysis on Relationship between Traffic Conditions and Crash Occurrences. Procedia - Social and Behavioral Sciences, v. 43, p. 302–312. doi:10.1016/j.sbspro.2012.04.103
Zheng, Z.; S. Ahn and C. M. Monsere (2010) Impact of traffic oscillations on freeway crash occurrences. Accident Analysis and Prevention, v. 42, n. 2, 626–636. doi:10.1016/j.aap.2009.10.009
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers for publication by TRANSPORTES agree to the following terms:
- Authors retain copyright and grant TRANSPORTES the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of this journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in TRANSPORTES.
- Authors are allowed and encouraged to post their work online (e.g., in institutional repositories or on their website) after publication of the article. Authors are encouraged to use links to TRANSPORTES (e.g., DOIs or direct links) when posting the article online, as TRANSPORTES is freely available to all readers.
- Authors have secured all necessary clearances and written permissions to published the work and grant copyright under the terms of this agreement. Furthermore, the authors assume full responsibility for any copyright infringements related to the article, exonerating ANPET and TRANSPORTES of any responsibility regarding copyright infringement.
- Authors assume full responsibility for the contents of the article submitted for review, including all necessary clearances for divulgation of data and results, exonerating ANPET and TRANSPORTES of any responsibility regarding to this aspect.