01550573

Component

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 warnings can be issued. Several factors influence the drivers’ behavior when approaching intersections. These include the vehicle speed, Time to Intersection (TTI), Distance to Intersection (DTI), age, gender, etc. 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 red light running (RLR) violations using kinetic information of individual drivers/vehicles. 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 technique 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 96.7 and 94.2 percent, respectively. It was shown that other than the critical instant at which the traffic signal changes to yellow, an appropriate time window with respect to the yellow onset can provide additional useful information ensuring that the driver decision occurs during that time window.

This paper was sponsored by TRB committee ABJ70 Artificial Intelligence and Advanced Computing Applications.

01550057

15-2910

English

13p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References (49) ; Tables

Crash causes; Decision making; Dilemma zone; Mathematical models; Red light running; Speed

Automated warning systems

Highways; Safety and Human Factors; I83: Accidents and the Human Factor

Transportation Research Board Annual Meeting 2015 Paper #15-2910

TRIS, TRB, ATRI

Dec 30 2014 12:58PM