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Tree diagram explaining the probability of events in the sample space.

Tree diagram explaining the probability of events in the sample space.

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Figure 1. Snapshots of the five study sections.
Figure 3. Aggregated hysteresis over the study sections.
Figure 4. Fundamental relation of IHT and distance gap for rear end...
Figure 5. Tree diagram explaining the probability of events in the...
+1 Figure 6. Number of rear-end collision points over the road space on...
Empirical Approach for Identifying Potential Rear-End Collisions Using Trajectory Data
Article
Full-text available
  • Apr 2021
This paper proposes a novel approach for examining rear-end collisions between successive vehicles in a traffic stream. In this approach, a new safety measure of the follower driver's attentiveness is proposed, referred to herein as instantaneous heeding time (IHT), reflecting the subject follower's heeding nature concerning its leader. A safety fr...
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Context 1
... P(V F c ), P(IHT c ), and P(D c ) be the complementary probabilities of the respective events. Further, the probability of each event from the sample space is depicted in the tree diagram shown in Figure 5. ...

Citations

... This further corroborates the theory of MTW followers tending to follow smaller vehicles more closely. It is observed that the values are much lesser than the deceleration rates recommended in earlier literature [57][58][59][60]. This can be attributed to the small values of relative speeds, which are a characteristic of the steady state following. ...
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Analysis of factors affecting occupant injury severity in rear-end crashes by different struck vehicle groups: A random thresholds random parameters hierarchical ordered probit model
Article
  • Jul 2022
This study aims to explore the variability of risk factors affecting injury severity in rear-end crashes when different struck vehicle groups are involved. Two types of rear-end crash data, vehicle-strike-car data, vehicle-strike-truck data, are extracted from the Fatality Analysis Reporting System (FARS). Two likelihood ratio (LR) tests are firstly performed to validate the struck vehicle group variations, and then two separate random thresholds random parameters hierarchical ordered probit (RRHOP) models (Model 1 and Model 2) are established to capture unobserved heterogeneity. The results of LR test show significant differences in the effects of factors included in each model. Moreover, the model results suggest that SUVs, vans, and large trucks as striking vehicles are significant related to injury severity in both models with different effects. Factors such as speeding related, pickup, model year (struck vehicle), disabled damage, adverse weather, speed limit (≥60 mile/h), and young driver (struck vehicle) are found to be statistically significant in only model 1. These results provide a better understanding of differences in contributing factors of rear-end crashes, which help to propose effective countermeasures to mitigate its injury severity.
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