Analysis and prediction of intersection traffic violations using automated enforcement system data

Li, Yunxuan; Li, Meng; Yuan, Jinghui; Lu, Jian; Aty, Mohamed

doi:10.1016/j.aap.2021.106422

Title: Analysis and prediction of intersection traffic violations using automated enforcement system data

Journal Article · Fri Oct 01 00:00:00 EDT 2021 · Accident Analysis and Prevention

DOI:https://doi.org/10.1016/j.aap.2021.106422· OSTI ID:1854480

Li, Yunxuan ^[1]; Li, Meng ^[1];

^[2]; Lu, Jian ^[3]; Aty, Mohamed ^[4]

Tsinghua Univ., Beijing (China)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Southeast Univ., Nanjing (China)
Univ. of Central Florida, Orlando, FL (United States)

We report that the automated enforcement system (AES) is an effective way of supplementing traditional traffic enforcement, and the traffic violation data from AES can also be effectively used for safety research. In this study, traffic violation data were used to analyze the influencing factors associated with traffic violations and to predict the probability of violations at intersections. The potential factors influencing violations include 24 independent factors related to time, space, traffic and weather. Results from a logistic model showed that the midday period, weekends, residential districts, collector roads, congested traffic conditions, high traffic flow, lower wind speed and low temperature would increase the probability of traffic violations. The probability of violations was predicted by the random forest algorithm, which was proven to be the best traffic violation prediction model among logistic regression, Gaussian naive Bayes, and support vector machine. Moreover, the proximity weighted synthetic oversampling technique (ProWSyn) method was applied to reduce the impact of the imbalance ratio (IR) and improve the model’s prediction performance. The receiver operating characteristics (ROC) curves and Precision-Recall (PR) curves illustrated that the random forest algorithm using oversampling data had the best classifier prediction performance than undersampling data. The area under curve (AUC) and out-of-bag (OOB) error with IR = 1 reached 0.914 and 0.0787, which showed the better performance of the random forest algorithm using ProWSyn in dealing with imbalanced traffic violation data.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; National Key Research and Development Program of China

Grant/Contract Number:: AC05-00OR22725; 2018YFB1601600

OSTI ID:: 1854480

Journal Information:: Accident Analysis and Prevention, Vol. 162, Issue 1; ISSN 0001-4575

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (47)

Risk factors associated with traffic violations and accident severity in China Zhang, Guangnan; Yau, Kelvin K. W.; Chen, Guanghan Accident Analysis & Prevention, Vol. 59 https://doi.org/10.1016/j.aap.2013.05.004	journal	October 2013
Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China Zhang, Guangnan; Tan, Ying; Jou, Rong-Chang Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 42 https://doi.org/10.1016/j.trf.2016.08.001	journal	October 2016
Turning off the cameras: Red light running characteristics and rates after photo enforcement legislation expired Porter, Bryan E.; Johnson, Kristie L.; Bland, Johnnie F. Accident Analysis & Prevention, Vol. 50 https://doi.org/10.1016/j.aap.2012.08.017	journal	January 2013
The traffic climate in China: The mediating effect of traffic safety climate between personality and dangerous driving behavior Zhang, Qian; Ge, Yan; Qu, Weina Accident Analysis & Prevention, Vol. 113 https://doi.org/10.1016/j.aap.2018.01.031	journal	April 2018
Identifying factors for traffic safety support in older drivers Mårdh, Selina Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 38 https://doi.org/10.1016/j.trf.2016.01.010	journal	April 2016
Real-time crash prediction on expressways using deep generative models Cai, Qing; Abdel-Aty, Mohamed; Yuan, Jinghui Transportation Research Part C: Emerging Technologies, Vol. 117 https://doi.org/10.1016/j.trc.2020.102697	journal	August 2020
The effects of personality traits on driving-related anger and aggressive behaviour in traffic among Serbian drivers Jovanović, Dragan; Lipovac, Krsto; Stanojević, Predrag Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 14, Issue 1 https://doi.org/10.1016/j.trf.2010.09.005	journal	January 2011
A spatiotemporal approach for traffic data imputation with complicated missing patterns Li, Huiping; Li, Meng; Lin, Xi Transportation Research Part C: Emerging Technologies, Vol. 119 https://doi.org/10.1016/j.trc.2020.102730	journal	October 2020
The relationship between Precision-Recall and ROC curves Davis, Jesse; Goadrich, Mark Proceedings of the 23rd international conference on Machine learning - ICML '06 https://doi.org/10.1145/1143844.1143874	conference	January 2006
Evaluation and spatial analysis of automated red-light running enforcement cameras Ahmed, Mohamed M.; Abdel-Aty, Mohamed Transportation Research Part C: Emerging Technologies, Vol. 50 https://doi.org/10.1016/j.trc.2014.07.012	journal	January 2015
Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling Lee, Dongwoo; Derrible, Sybil; Pereira, Francisco Camara Transportation Research Record: Journal of the Transportation Research Board, Vol. 2672, Issue 49 https://doi.org/10.1177/0361198118796971	journal	September 2018
Effects of Red Light Cameras on Violations and Crashes: A Review of the International Literature Retting, Richard A.; Ferguson, Susan A.; Hakkert, A. Shalom Traffic Injury Prevention, Vol. 4, Issue 1 https://doi.org/10.1080/15389580309858	journal	March 2003
Factors associated with driver injury severity of lane changing crashes involving younger and older drivers Adanu, Emmanuel Kofi; Lidbe, Abhay; Tedla, Elsa Accident Analysis & Prevention, Vol. 149 https://doi.org/10.1016/j.aap.2020.105867	journal	January 2021
Red light cameras revisited. Recent evidence on red light camera safety effects Goldenbeld, Charles; Daniels, Stijn; Schermers, Govert Accident Analysis & Prevention, Vol. 128 https://doi.org/10.1016/j.aap.2019.04.007	journal	July 2019
Analyzing traffic violation behavior at urban intersections: A spatio-temporal kernel density estimation approach using automated enforcement system data Li, Yunxuan; Abdel-Aty, Mohamed; Yuan, Jinghui Accident Analysis & Prevention, Vol. 141 https://doi.org/10.1016/j.aap.2020.105509	journal	June 2020
Automated Enforcement of Red Light Running Technology and Programs: A Review Smith, David M.; McFadden, John; Passetti, Karl A. Transportation Research Record: Journal of the Transportation Research Board, Vol. 1734, Issue 1 https://doi.org/10.3141/1734-05	journal	January 2000
Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers Arhin, Stephen A.; Gatiba, Adam Transportation Safety and Environment, Vol. 2, Issue 2 https://doi.org/10.1093/tse/tdaa012	journal	June 2020
Driver Risk Assessment Using Traffic Violation and Accident Data by Machine Learning Approaches Fang, Aifen; Qiu, Chenlu; Zhao, Lei 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE) https://doi.org/10.1109/ICITE.2018.8492665	conference	September 2018
Impulsivity facets and mobile phone use while driving: Indirect effects via mobile phone involvement Mostyn Sullivan, Beau; George, Amanda M.; Brown, Patricia M. Accident Analysis & Prevention, Vol. 150 https://doi.org/10.1016/j.aap.2020.105907	journal	February 2021
Organizational controls and safety: The varieties of rule-related behaviour Reason, James; Parker, Dianne; Lawton, Rebecca Journal of Occupational and Organizational Psychology, Vol. 71, Issue 4 https://doi.org/10.1111/j.2044-8325.1998.tb00678.x	journal	December 1998
Investigating macro-level hotzone identification and variable importance using big data: A random forest models approach Jiang, Ximiao; Abdel-Aty, Mohamed; Hu, Jia Neurocomputing, Vol. 181 https://doi.org/10.1016/j.neucom.2015.08.097	journal	March 2016
Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network Yuan, Jinghui; Abdel-Aty, Mohamed; Gong, Yaobang Transportation Research Record: Journal of the Transportation Research Board, Vol. 2673, Issue 4 https://doi.org/10.1177/0361198119840611	journal	April 2019
Speeding Violation Type Prediction Based on Decision Tree Method: A Case Study in Wujiang, China Cheng, Zeyang; Lu, Jian; Zu, Zhenshan Journal of Advanced Transportation, Vol. 2019 https://doi.org/10.1155/2019/8650845	journal	June 2019
SMOTE: Synthetic Minority Over-sampling Technique Chawla, N. V.; Bowyer, K. W.; Hall, L. O. Journal of Artificial Intelligence Research, Vol. 16 https://doi.org/10.1613/jair.953	journal	January 2002
CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests Ma, Li; Fan, Suohai BMC Bioinformatics, Vol. 18, Issue 1 https://doi.org/10.1186/s12859-017-1578-z	journal	March 2017
An introduction to ROC analysis Fawcett, Tom Pattern Recognition Letters, Vol. 27, Issue 8 https://doi.org/10.1016/j.patrec.2005.10.010	journal	June 2006
Impact of countdown timer on driving maneuvers after the yellow onset at signalized intersections: An empirical study in Changsha, China Long, Kejun; Liu, Yue; Han, Lee D. Safety Science, Vol. 54 https://doi.org/10.1016/j.ssci.2012.10.007	journal	April 2013
Can traffic violations be traced to gender-role, sensation seeking, demographics and driving exposure? Oppenheim, Ilit; Oron-Gilad, Tal; Parmet, Yisrael Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 43 https://doi.org/10.1016/j.trf.2016.06.027	journal	November 2016
Effectiveness of implementing the criminal administrative punishment law of drunk driving in China: An interrupted time series analysis, 2004-2017 Fei, Gaoqiang; Li, Xinyu; Sun, Qiannan Accident Analysis & Prevention, Vol. 144 https://doi.org/10.1016/j.aap.2020.105670	journal	September 2020
A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution Elamrani Abou Elassad, Zouhair; Mousannif, Hajar; Al Moatassime, Hassan Knowledge-Based Systems, Vol. 205 https://doi.org/10.1016/j.knosys.2020.106314	journal	October 2020
A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset Schlögl, Matthias; Stütz, Rainer; Laaha, Gregor Accident Analysis & Prevention, Vol. 127 https://doi.org/10.1016/j.aap.2019.02.008	journal	June 2019
Crash data augmentation using variational autoencoder Islam, Zubayer; Abdel-Aty, Mohamed; Cai, Qing Accident Analysis & Prevention, Vol. 151 https://doi.org/10.1016/j.aap.2020.105950	journal	March 2021
Evaluation of Functional Efficiency of Automated Traffic Enforcement Systems Kerimov, Mukhtar; Safiullin, Ravil; Marusin, Alexey Transportation Research Procedia, Vol. 20 https://doi.org/10.1016/j.trpro.2017.01.025	journal	January 2017
Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes Champahom, Thanapong; Jomnonkwao, Sajjakaj; Watthanaklang, Duangdao Accident Analysis & Prevention, Vol. 141 https://doi.org/10.1016/j.aap.2020.105537	journal	June 2020
Mobile phone use while driving-literary review Lipovac, Krsto; Đerić, Miroslav; Tešić, Milan Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 47 https://doi.org/10.1016/j.trf.2017.04.015	journal	May 2017
Effect of Red-Light Cameras on Capacity of Signalized Intersections Baratian-Ghorghi, Fatemeh; Zhou, Huaguo; Wasilefsky, Isaac Journal of Transportation Engineering, Vol. 142, Issue 1 https://doi.org/10.1061/(ASCE)TE.1943-5436.0000804	journal	January 2016
Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis Mannering, Fred; Bhat, Chandra R.; Shankar, Venky Analytic Methods in Accident Research, Vol. 25 https://doi.org/10.1016/j.amar.2020.100113	journal	March 2020
Driver distraction and driver inattention: Definition, relationship and taxonomy Regan, Michael A.; Hallett, Charlene; Gordon, Craig P. Accident Analysis & Prevention, Vol. 43, Issue 5 https://doi.org/10.1016/j.aap.2011.04.008	journal	September 2011
Analysis of factors associated with exceeding lawful speed traffic violations in Indian metropolitan city Balasubramanian, Venkatesh; Sivasankaran, Sathish Kumar Journal of Transportation Safety & Security, Vol. 13, Issue 2 https://doi.org/10.1080/19439962.2019.1626962	journal	June 2019
A review of the effect of traffic and weather characteristics on road safety Theofilatos, Athanasios; Yannis, George Accident Analysis & Prevention, Vol. 72 https://doi.org/10.1016/j.aap.2014.06.017	journal	November 2014
Red-light running violation prediction using observational and simulator data Jahangiri, Arash; Rakha, Hesham; Dingus, Thomas A. Accident Analysis & Prevention, Vol. 96 https://doi.org/10.1016/j.aap.2016.06.009	journal	November 2016
Hourly associations between weather factors and traffic crashes: Non-linear and lag effects Xing, Fen; Huang, Helai; Zhan, ZhiYing Analytic Methods in Accident Research, Vol. 24 https://doi.org/10.1016/j.amar.2019.100109	journal	December 2019
Cross-cultural differences in driving skills: A comparison of six countries Özkan, Türker; Lajunen, Timo; Chliaoutakis, Joannes El. Accident Analysis & Prevention, Vol. 38, Issue 5 https://doi.org/10.1016/j.aap.2006.04.006	journal	September 2006
An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets Kovács, György Applied Soft Computing, Vol. 83 https://doi.org/10.1016/j.asoc.2019.105662	journal	October 2019
Continuous risk profile and clustering-based method for investigating the effect of the automated enforcement system on urban traffic collisions Park, Shin Hyung; Park, Shin Hyoung; Kwon, Oh Hoon The Journal of Supercomputing, Vol. 75, Issue 8 https://doi.org/10.1007/s11227-019-02752-6	journal	January 2019
Exploring traffic safety culture and drunk driving: An examination of the community and DUI related fatal crashes in the U.S. (1993–2015) Stringer, Richard J. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 56 https://doi.org/10.1016/j.trf.2018.05.014	journal	July 2018
Does longevity impact the severity of traffic crashes? A comparative study of young-older and old-older drivers Ayuso, Mercedes; Sánchez, Rodrigo; Santolino, Miguel Journal of Safety Research, Vol. 73 https://doi.org/10.1016/j.jsr.2020.02.002	journal	June 2020

Similar Records

Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets

Journal Article · Tue Oct 27 00:00:00 EDT 2020 · Journal of Cheminformatics · OSTI ID:1854480

Idakwo, Gabriel; Thangapandian, Sundar; Luttrell, Joseph; +7 more

WE-E-17A-02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG-PET Images

Journal Article · Sun Jun 15 00:00:00 EDT 2014 · Medical Physics · OSTI ID:1854480

Li, R; Aguilera, T; Shultz, D; +3 more

Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Journal Article · Tue Jul 12 00:00:00 EDT 2022 · Frontiers in Psychology · OSTI ID:1854480

Liu, Meng; Chen, Yan; Guo, Zhenxiang; +5 more

Related Subjects

42 ENGINEERING
Automated enforcement system
traffic violation
random forest
imbalance ratio

Title: Analysis and prediction of intersection traffic violations using automated enforcement system data

Citation Formats

References (47)

Similar Records

Related Subjects