Abstract
Big data and data-driven analysis could be utilized for traffic management to improve road safety and the performance of transportation systems. This paper introduces a web-based proactive traffic safety management (PATM) and real-time big data visualization tool, which is based on an award-winning system that won the US Department of Transportation (USDOT) Solving for Safety Visualization Challenge and was selected as one of the USDOT Safety Data Initiative (SDI) Beta Tools. State-of-the-art research, especially for real-time crash prediction and PATM, are deployed in this study. A significant amount of real-time data is accessed by the system in order to conduct data-driven analysis, such as traffic data, weather data, and video data from closed-circuit television (CCTV) live streams. Based on the data, multiple modules have been developed, including real-time crash/secondary crash prediction, CCTV-based expedited detection, PATM recommendation, data sharing, and report generation. Both real-time data and the system outputs are visualized at the front end using interactive maps and various types of figures to represent the data distribution and efficiently reveal hidden patterns. Evaluation of the real-time crash prediction outputs is conducted based on one-month real-world crash data and the prediction results from the system. The comparison results indicate excellent prediction performance. When considering spatial-temporal tolerance, the sensitivity and false alarm rate of the prediction results [i.e., high crash potential event (HCPE)] are 0.802 and 0.252, respectively. Current and potential implementation are also discussed in this paper.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Part of this research was supported by the US DOT’s Notice of Funding Opportunity (NOFO) in collaboration with MetroPlan Orlando and FDOT. All results and opinions are those of the authors only and do not reflect the opinion or position of the US DOT and FDOT. The authors would also like to thank the team that contributed to the system, including Jaeyoung Lee, Jinghui Yuan, Zubayer Islam, Cheng Yuan, Jiajia Dong, Jinyu Pei, Jacob Lites, Shile Zhang, Jorge Ugan, Lishengsa Yue, Morgan Morris, Md. Hasibur Rahman, Nada Mahmoud, and Samiul Hasan.
References
Abdel-Aty, M., R. J. Cunningham, V. V. Gayah, and L. Hsia. 2008. “Dynamic variable speed limit strategies for real-time crash risk reduction on freeways.” Transp. Res. Rec. 2078 (1): 108–116. https://doi.org/10.3141/2078-15.
Abdel-Aty, M., A. Dhindsa, and V. Gayah. 2007. “Considering various ALINEA ramp metering strategies for crash risk mitigation on freeways under congested regime.” Transp. Res. Part C Emerging Technol. 15 (2): 113–134. https://doi.org/10.1016/j.trc.2007.02.003.
Abdel-Aty, M., J. Dilmore, and A. Dhindsa. 2006. “Evaluation of variable speed limits for real-time freeway safety improvement.” Accid. Anal. Prev. 38 (2): 335–345. https://doi.org/10.1016/j.aap.2005.10.010.
Abdel-Aty, M., and V. Gayah. 2010. “Real-time crash risk reduction on freeways using coordinated and uncoordinated ramp metering approaches.” J. Transp. Eng. 136 (5): 410–423. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000100.
Abdel-Aty, M., and A. Pande. 2005. “Identifying crash propensity using specific traffic speed conditions.” J. Saf. Res. 36 (1): 97–108. https://doi.org/10.1016/j.jsr.2004.11.002.
Abdel-Aty, M., N. Uddin, and A. Pande. 2005. “Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways.” Transp. Res. Rec. 1908 (1): 51–58. https://doi.org/10.1177/0361198105190800107.
Abdel-Aty, M., N. Uddin, A. Pande, F. Abdalla, and L. Hsia. 2004. “Predicting freeway crashes from loop detector data by matched case-control logistic regression.” Transp. Res. Rec. 1897 (1): 88–95. https://doi.org/10.3141/1897-12.
Abdel-Aty, M. A., H. M. Hassan, M. Ahmed, and A. S. Al-Ghamdi. 2012. “Real-time prediction of visibility related crashes.” Transp. Res. Part C Emerging Technol. 24 (Oct): 288–298. https://doi.org/10.1016/j.trc.2012.04.001.
Abdel-Aty, M. A., and R. Pemmanaboina. 2006. “Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data.” IEEE Trans. Intell. Transp. Syst. 7 (2): 167–174. https://doi.org/10.1109/TITS.2006.874710.
Abdelraouf, A., M. Abdel-Aty, and Y. Wu. 2022. “Using vision transformers for spatial-context-aware rain and road surface condition detection on freeways.” IEEE Trans. Intell. Transp. Syst. 23 (10): 18546–18556. https://doi.org/10.1109/TITS.2022.3150715.
Ahmed, M., M. Abdel-Aty, and R. Yu. 2012. “Assessment of interaction of crash occurrence, mountainous freeway geometry, real-time weather, and traffic data.” Transp. Res. Rec. 2280 (1): 51–59. https://doi.org/10.3141/2280-06.
Ahmed, M. M., and M. A. Abdel-Aty. 2011. “The viability of using automatic vehicle identification data for real-time crash prediction.” IEEE Trans. Intell. Transp. Syst. 13 (2): 459–468. https://doi.org/10.1109/TITS.2011.2171052.
Cai, Q., M. Abdel-Aty, J. Yuan, J. Lee, and Y. Wu. 2020. “Real-time crash prediction on expressways using deep generative models.” Transp. Res. Part C Emerging Technol. 117 (Aug): 102697. https://doi.org/10.1016/j.trc.2020.102697.
DOT. 2020. “US safety data initiative.” Accessed January 1, 2020. https://www.transportation.gov/content/safety-data-initiative.
FDOT. 2018. “District 5 SunStore.” Accessed January 1, 2018. https://sunstore.cflsmartroads.com/.
Hassan, H. M., and M. A. Abdel-Aty. 2013. “Predicting reduced visibility related crashes on freeways using real-time traffic flow data.” J. Saf. Res. 45 (Jun): 29–36. https://doi.org/10.1016/j.jsr.2012.12.004.
Hill, E., M. Abdel-Aty, Y. Wu, J. Dilmore, L. Bouck, and R. Cunningham. 2021. State and local government data analysis tools to support policy and decision making for roadway safety—Crash predictions for expedited detection (CPED). Washington, DC: DOT.
Huang, H., and M. Abdel-Aty. 2010. “Multilevel data and Bayesian analysis in traffic safety.” Accid. Anal. Prev. 42 (6): 1556–1565. https://doi.org/10.1016/j.aap.2010.03.013.
Islam, Z., M. Abdel-Aty, Q. Cai, and J. Yuan. 2021. “Crash data augmentation using variational autoencoder.” Accid. Anal. Prev. 151 (Mar): 105950. https://doi.org/10.1016/j.aap.2020.105950.
Karlaftis, M. G., S. P. Latoski, N. J. Richards, and K. C. Sinha. 1999. “ITS impacts on safety and traffic management: An investigation of secondary crash causes.” J. Intell. Transp. Syst. 5 (1): 39–52. https://doi.org/10.1080/10248079908903756.
Li, P., and M. Abdel-Aty. 2022. “A hybrid machine learning model for predicting real-time secondary crash likelihood.” Accid. Anal. Prev. 165 (Feb): 106504. https://doi.org/10.1016/j.aap.2021.106504.
Li, P., M. Abdel-Aty, Q. Cai, and C. Yuan. 2020a. “The application of novel connected vehicles emulated data on real-time crash potential prediction for arterials.” Accid. Anal. Prev. 144: 105658. https://doi.org/10.1016/j.aap.2020.105658.
Li, P., M. Abdel-Aty, and J. Yuan. 2020b. “Real-time crash risk prediction on arterials based on LSTM-CNN.” Accid. Anal. Prev. 135 (Feb): 105371. https://doi.org/10.1016/j.aap.2019.105371.
OpenWeather. 2011. “Weather API.” Accessed April 1, 2023. https://openweathermap.org/api.
Park, H., and A. Haghani. 2016. “Real-time prediction of secondary incident occurrences using vehicle probe data.” Transp. Res. Part C Emerging Technol. 70 (Sep): 69–85. https://doi.org/10.1016/j.trc.2015.03.018.
Shi, Q., and M. Abdel-Aty. 2015. “Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways.” Transp. Res. Part C Emerging Technol. 58 (Part B): 380–394. https://doi.org/10.1016/j.trc.2015.02.022.
UCF-SST. 2018. “UCF smart and safe transportation lab website.” Accessed January 1, 2018. http://ucfsst.com/.
USDOT. 2019. “U.S. transportation secretary announces solving for safety visualization challenge winner.” Accessed January 1, 2019. https://www.bts.gov/newsroom/us-transportation-secretary-announces-solving-safety-visualization-challenge-winner.
Wang, L., M. Abdel-Aty, and J. Lee. 2017. “Implementation of active traffic management strategies for safety on congested expressway weaving segments.” Transp. Res. Rec. 2635 (1): 28–35. https://doi.org/10.3141/2635-04.
Wang, L., M. Abdel-Aty, J. Lee, and Q. Shi. 2019. “Analysis of real-time crash risk for expressway ramps using traffic, geometric, trip generation, and socio-demographic predictors.” Accid. Anal. Prev. 122 (Jan): 378–384. https://doi.org/10.1016/j.aap.2017.06.003.
Wang, L., M. Abdel-Aty, Q. Shi, and J. Park. 2015a. “Real-time crash prediction for expressway weaving segments.” Transp. Res. Part C Emerging Technol. 61 (Dec): 1–10. https://doi.org/10.1016/j.trc.2015.10.008.
Wang, L., Q. Shi, and M. Abdel-Aty. 2015b. “Predicting crashes on expressway ramps with real-time traffic and weather data.” Transp. Res. Rec. 2514 (1): 32–38. https://doi.org/10.3141/2514-04.
Wu, Y., M. Abdel-Aty, L. Wang, and M. S. Rahman. 2020a. “Combined connected vehicles and variable speed limit strategies to reduce rear-end crash risk under fog conditions.” J. Intell. Transp. Syst. 24 (5): 494–513. https://doi.org/10.1080/15472450.2019.1634560.
Wu, Y., M. Abdel-Aty, O. Zheng, and J. Yuan. 2020b. Using roadside cameras to analyze traffic conflicts at intersections based on vehicle key points detection transportation research board 100th annual meeting transportation research board. Washington, DC: Transportation Research Board.
Xu, C., P. Liu, B. Yang, and W. Wang. 2016. “Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data.” Transp. Res. Part C Emerging Technol. 71 (Oct): 406–418. https://doi.org/10.1016/j.trc.2016.08.015.
Yu, R., and M. Abdel-Aty. 2013. “Utilizing support vector machine in real-time crash risk evaluation.” Accid. Anal. Prev. 51 (Mar): 252–259. https://doi.org/10.1016/j.aap.2012.11.027.
Yu, R., and M. Abdel-Aty. 2014. “An optimal variable speed limits system to ameliorate traffic safety risk.” Transp. Res. Part C Emerging Technol. 46 (Sep): 235–246. https://doi.org/10.1016/j.trc.2014.05.016.
Yu, R., M. Abdel-Aty, and M. Ahmed. 2013. “Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors.” Accid. Anal. Prev. 50 (Jan): 371–376. https://doi.org/10.1016/j.aap.2012.05.011.
Yuan, J., M. Abdel-Aty, Y. Gong, and Q. Cai. 2019. “Real-time crash risk prediction using long short-term memory recurrent neural network.” Transp. Res. Rec. 2673 (4): 314–326. https://doi.org/10.1177/0361198119840611.
Yuan, J., M. Abdel-Aty, L. Wang, J. Lee, R. Yu, and X. Wang. 2018. “Utilizing Bluetooth and adaptive signal control data for real-time safety analysis on urban arterials.” Transp. Res. Part C Emerging Technol. 97 (Dec): 114–127. https://doi.org/10.1016/j.trc.2018.10.009.
Yuan, J., M. A. Abdel-Aty, L. Yue, and Q. Cai. 2020. “Modeling real-time cycle-level crash risk at signalized intersections based on high-resolution event-based data.” IEEE Trans. Intell. Transp. Syst. 22 (11): 6700–6715. https://doi.org/10.1109/TITS.2020.2994126.
Zhan, C., A. Gan, and M. Hadi. 2009. “Identifying secondary crashes and their contributing factors.” Transp. Res. Rec. 2102 (1): 68–75. https://doi.org/10.3141/2102-09.
Zhang, S., M. Abdel-Aty, Y. Wu, and O. Zheng. 2020. “Modeling pedestrians’ near-accident events at signalized intersections using gated recurrent unit (GRU).” Accid. Anal. Prev. 148 (Dec): 105844. https://doi.org/10.1016/j.aap.2020.105844.
Zhang, S., M. Abdel-Aty, Y. Wu, and O. Zheng. 2021. “Pedestrian crossing intention prediction at red-light using pose estimation.” IEEE Trans. Intell. Transp. Syst. 23 (3): 2331–2339. https://doi.org/10.1109/TITS.2021.3074829.
Zheng, O., M. Abdel-Aty, L. Yue, A. Abdelraouf, Z. Wang, and N. Mahmoud. 2022. “CitySim: A drone-based vehicle trajectory dataset for safety oriented research and digital twins.” Preprint, submitted August 23, 2022. https://arxiv.org/abs/2208.11036.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 8 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 11, 2022
Accepted: Feb 20, 2023
Published online: May 17, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 17, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Xiao-chi Ma, Jian Lu, Yiik Diew Wong, Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety, Journal of Advanced Transportation, 10.1155/2023/2780961, 2023, (1-16), (2023).
- Weisi Chen, Zoran Milosevic, Fethi A. Rabhi, Andrew Berry, Real-Time Analytics: Concepts, Architectures, and ML/AI Considerations, IEEE Access, 10.1109/ACCESS.2023.3295694, 11, (71634-71657), (2023).
- Mohamed Abdel-Aty, Zijin Wang, Ou Zheng, Amr Abdelraouf, Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators, Accident Analysis & Prevention, 10.1016/j.aap.2023.107191, 191, (107191), (2023).