<p><a>Proactive evaluation of road safety is
one of the most important objectives of transportation engineers. While current
practice typically relies on crash-based analysis after the fact to diagnose
safety problems and provide corrective countermeasures on roads, surrogate measures
of safety are emerging as a complementary evaluation that can allow engineers
to proactively respond to safety issues. These surrogate measures attempt to address
the primary limitations of crash data, which include underreporting, lack of
reliable insight into the events leading to the crash, and long data collection
times. </a></p>
<p>Traffic
conflicts are one of the most widely adopted surrogate measures of safety
because they meet the following two conditions for crash surrogacy: (1) they
are non-crash events that can be physically related in a predictable and
reliable way to crashes, and (2) there is a potential for bridging crash
frequency and severity with traffic conflicts. However, three primary issues
were identified in the literature that need to be resolved for the practical
application of conflicts: (1) the lack of consistency in the definition of traffic
conflict, (2) the predictive validity from such events, and (3) the adequacy of traffic conflict observations.</p>
<p>Tarko
(2018) developed a theoretical framework in response to the first two issues
and defined traffic conflicts using counterfactual theory as events where the lack
of timely responses from drivers or road users can produce crashes if there is
no evasive action. The author further introduced a failure-based definition to emphasize conflicts as an undesirable
condition that needs to be corrected to avoid a crash. In this case, the
probability of a crash, given failure, depends on the response delay. The
distribution of this delay is adjusted, and the probability is estimated using
the fitted distribution. As this formal theory addresses the first two issues,
a complete framework for the proper identification of conflicts needs to be
investigated in line with the failure mechanism proposed in this theory.</p>
<p>The
objective of this dissertation, in response to the third issue, is to provide a
generalized framework for proper identification of traffic conflicts by
considering the failure-based definition of traffic conflicts. The framework introduced
in this dissertation is built upon an empirical evaluation of the methods
applied to identify traffic conflicts from naturalistic driving studies and
video-based tracking systems. This dissertation aimed to prove the practicality
of the framework for proactive safety evaluation using emerging technologies
from in-vehicle and roadside instrumentation.</p>
<p>Two
conditions must be met to properly claim observed traffic events as traffic
conflicts: (1) analysis of longitudinal and lateral acceleration profiles for
identification of response due to failure and (2) estimation of the time-to-collision
as the period between the end of the evasion and the hypothetical collision.
Extrapolating user behavior in the counterfactual scenario of no evasion is
applied for identifying the hypothetical collision point.</p>
<p>The
results from the SHRP2 study were particularly encouraging, where the appropriate
identification of traffic conflicts resulted in the estimation of an expected
number of crashes similar to the number reported in the study. The results also
met the theoretical postulates including stabilization of the estimated crashes
at lower proximity values and Lomax-distributed response delays. In terms of area-wide
tracking systems, the framework was successful in identifying and removing failure-free
encounters from the In-Depth understanding of accident causation for Vulnerable
road users (InDeV) program.</p>
<p>This
dissertation also extended the application of traffic conflicts technique by considering
estimation of the severity of a hypothetical crash given that a conflict occurs.
This component is important in order for conflicts to resemble the practical
applications of crashes, including the diagnostics of hazardous locations and evaluating the effectiveness of the countermeasures. Countermeasures should not only reduce the
number of conflicts but also the risk of crash given the conflict. Severity
analysis identifies the environmental, road, driver, and pre-crash conditions
that increase the likelihood of severe impacts. Using dynamic characterization of
crash events, this dissertation structured a probability model to evaluate
crash reporting and its associated severity. Multinomial logistic models were
applied in the estimation; and quasi-complete separation in logistic regression
was addressed by providing a Bayesian estimation of these models.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12954119 |
| Date | 16 December 2020 |
| Creators | Cristhian Lizarazo Jimenez (9375209) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/IDENTIFICATION_OF_FAILURE-CAUSED_TRAFFIC_CONFLICTS_IN_TRACKING_SYSTEMS_A_GENERAL_FRAMEWORK/12954119 |
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