Utilising disturbance metrics in traffic safety estimation

Abstract

There have been limited efforts to investigate the potential of using detailed trajectory data obtained from connected vehicles and/or other sensors in deriving metrics for use in real-time assessment of traffic safety. This study investigated the use of disturbance metrics for this purpose. The disturbance metrics considered were the number of oscillations (NoO) and a measure of disturbance durations in terms of the time exposed time-to-collision index (TETIndex). The time exposed time-to-collision (TET) has been widely used as a safety surrogate measure, but there is no study on the identification of its thresholds to justify activating plans to mitigate unsafe conditions in real-time operations. This study found a TETIndex of 0.03 and NoO of 10% can be used to recognise unsafe conditions in the case study of this research. Since estimation of the TETIndex requires the location and speed of both leading and following vehicles and therefore cannot be measured accurately with low sample sizes of vehicle trajectories, a regression model was derived to estimate the TETIndex based on speed parameters. It was also found that the investigated disturbance metrics and associated models and thresholds are significantly related to crash occurrence and can thus be used in the activation of transportation management strategies to reduce the probability of unsafe traffic and ease traffic disturbances that have an adverse impact on traffic safety.

Using Traffic Disturbance Metrics to Estimate and Predict Freeway Traffic Breakdown and Safety Events

“…As can be seen from Figure 4, the most important variables in predicting the occurrence of crashes are the TETIndex and NO. ( 37 ).…”

“…As can be seen, the developed model of the crash frequency for the Michigan test segment corresponds more with adding disturbance metrics to input variables than excluding them. This indicates that the disturbances metrics used are good indicators of traffic safety and they can be used as inputs to predict crash in real-time operations ( 37 ).…”

MAGIC Dataset: Multiple Conditions Unmanned Aerial Vehicle Group-Based High-Fidelity Comprehensive Vehicle Trajectory Dataset

“…From a review of the existing studies related to expressways and Interstate highways, the authors found that most of the current studies are based on the NGSIM dataset, which was collected by the U.S. Department of Transportation’s Joint Program Office for Intelligent Transportation Systems in 2005 using cameras on top of high-rise buildings ( 11 ). From NGSIM, the dataset recorded on U.S. Highway 101 (US-101) has 15 min of congestion data, covering a maximum of 640 m. It should be noted that the NGSIM US-101 dataset has the best data quality compared with the other three NGSIM datasets, and some new datasets are often compared with the quality of US-101 ( 13 ).…”

“…The second is based on the global positioning system ( 16 – 18 ), which can obtain a wide range of moving vehicle trajectories, even at city level, but it often has trajectory drift and cannot provide data with lane-level accuracy. The third method is based on aerial video ( 11 , 19 ). This method was first implemented by the use of helicopters or camera platforms on high-rise buildings.…”