NDLI: Adopting Machine Learning Methods to Predict Red-light Running Violations

Content Provider	IEEE Xplore Digital Library
Author	Jahangiri, A. Rakha, H.A. Dingus, T.A.
Copyright Year	2015
Description	Author affiliation: Civil & Environ. Eng. Dept., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA (Jahangiri, A.; Rakha, H.A.; Dingus, T.A.)
Abstract	Statistics demonstrate that a large number of crashes occur at signalized intersections due to traffic violations, specifically red light running (RLR). In order to prevent/mitigate intersection-related crashes, these violations need to be identified before they occur, so appropriate actions can be taken. Several factors such as vehicle speed, Time to Intersection (TTI), Distance to Intersection (DTI), age, gender, etc. influence the drivers behavior when approaching intersections. However, the driver-related factors (i.e. age, gender) are more difficult to obtain in practice. On the other hand, kinetic factors (e.g. speed, acceleration) can be obtained by monitoring the movement of vehicles through video cameras installed on the infrastructure or through on-board devices installed on the vehicles. Hence, the problem of interest is to develop models to predict RLR violations using kinetic information of vehicles. A monitoring period was defined to extract data from each vehicle before reaching the intersection. Machine learning techniques, namely Support Vector Machine (SVM) and Random Forest (RF), were adopted to develop prediction models. The minimum Redundancy Maximum Relevance (mRMR) feature selection method was used to identify the most important factors for model development. To evaluate the performance of the models the K-fold cross-validation and out-of-bag (OOB) errors were used for the SVM and RF models, which contributed to high prediction accuracies of 97.9 and 93.6 percent, respectively. It was shown that other than the critical instant at which the traffic signal changes to yellow, an appropriate monitoring period with respect to the yellow onset can provide additional useful information ensuring that the driver decision occurs during that period.
Starting Page	650
Ending Page	655
File Size	309157
Page Count	6
File Format	PDF
ISSN	21530017
e-ISBN	9781467365963
DOI	10.1109/ITSC.2015.112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Monitoring Support vector machines Radio frequency Diffusion tensor imaging Predictive models Vehicle crash testing machine learning driver violation red light running signalized intersection violation prediction support vector machine random forest
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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