Validity of failure-caused traffic conflicts as surrogates of rear-end collisions in naturalistic driving studies

Tarko, Andrew P.; Lizarazo, Cristhian

doi:10.1016/j.aap.2020.105863

Cited by 18 publications

(9 citation statements)

References 18 publications

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“…Once the traffic probabilities for each of a set of evasive events and initial conditions are calculated, the expected number of traffic conflicts corresponding to the observed conflicts can be obtained by summing the probabilities [51]. Tarko & Lizarazo [5] demonstrated the use of causal relationship in their study of estimation rear end conflicts using driving simulator data.…”

Section: ) Safety Modelsmentioning

confidence: 99%

“…Previous research expended substantial effort to justify using SSMs to assess traffic safety [5,91]; however, those efforts focused exclusively on homogenous TV streams. It is unclear whether SSMs validated for a TV traffic stream are applicable to mixed traffic.…”

Section: ) Exploring the Validity Of Current Ssmsmentioning

confidence: 99%

“…Assuming the relation between conflicts and crashes is known, it is theoretically possible to estimate the frequency of crashes based on the traffic conflict measurement [4]. Nonetheless, the portion of traffic conflicts that may result in crashes is not fixed; instead, it varies with different traffic compositions and across sites [5].…”

Section: Introductionmentioning

confidence: 99%

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Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Das

Samandar²,

Autry

et al. 2023

IEEE Access

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Surrogate safety measures (SSMs) are critical tools for evaluating the safety performance of mixed traffic. Crashes are rare events, and historical crash data is scarce for mixed traffic that includes autonomous and/ or connected vehicles. Recent safety review papers focus on traditional human-driven vehicles (TVs) and do not encompass advanced technology vehicles such as Autonomous Vehicles (AVs), Connected Vehicles (CVs), and Connected-Autonomous Vehicles (CAVs). This study examines the development, implementation, and shortcomings of SSMs, and SSM-based models used for mixed traffic safety evaluation. It reviews the current relevant literature and applies a case study analysis using a real-world mixed traffic dataset. The study summarizes the fundamental SSM guiding concepts, as well as their most significant metrics including threshold values employed in the past for SSMs and SSM-based models. Primary benefits and limitations of examined SSMs and SSM-based models are also underlined. This review reveals significant gaps in the literature that might guide future research paths in SSM-based mixed traffic safety assessment. Critical gaps include the absence of robust SSM threshold selection criteria, the suitability of current SSMs in mixed traffic research, microsimulation modeling that lacks proper calibration and validation, and the absence of a framework for selecting or combining multiple SSMs.INDEX TERMS Mixed traffic, surrogate safety measures, real-world mixed traffic dataset, autonomous vehicles, connected vehicles, connected-autonomous vehicles

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Section: ) Safety Modelsmentioning

confidence: 99%

Section: ) Exploring the Validity Of Current Ssmsmentioning

confidence: 99%

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Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Das

Samandar²,

Autry

et al. 2023

IEEE Access

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“…This approach added the much-needed quantifiable result, which is the number of collisions, to be directly used as a safety indication. The technique has been validated against actual collision records in several studies ( 39 , 40 ). This approach was used recently to assess the safety of pedestrians interacting with AVs in urban areas ( 19 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Active Road User Interactions With Autonomous Vehicles: Proactive Safety Assessment

Alozi

Hussein

2023

Transportation Research Record: Journal of the Transportation R

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This study aims to conduct a thorough assessment of pedestrian and cyclist safety in autonomous vehicle (AV) environments. To that end, the study utilized AV sensor data of over 1,500 driving hours from five sources in Canada, the United States, and Singapore. The sensor data were used to extract conflicts between AVs and active road users. The conflicts were then processed to develop accurate estimates of AV collisions involving pedestrians and cyclists based on the extreme value theory. Further in-depth assessments were conducted on the identified conflicts, by type and location, to highlight potential issues leading to risky conflicts. The results showed that the total number of predicted AV collisions involving active road users was 2.17 collisions per million AV kilometers travelled. Collisions involving pedestrians were slightly higher than those involving cyclists. Also, collisions in clear weather conditions slightly exceeded collisions in adverse weather conditions, although the difference was not statistically significant. The relative risk of collisions was developed for both pedestrian and cyclist conflicts per AV movement type. The results showed that for pedestrians, interactions with right-turning AVs are the riskiest, while interactions with left-turning AVs are the riskiest for cyclists. A thorough analysis of conflicts revealed many issues, including a higher tendency for pedestrian violations when interacting with AVs, aggressive AV behavior (particularly when interacting with pedestrians while making a right turn), AVs struggling to predict the path of cyclists (mainly because of cyclist violations), and AVs failing to interpret pedestrian intentions in some cases.

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“…The author compared different approaches for the estimation of parameters, with the use of synthetic data: Maximum Likelihood, Probability-Weighted Moments, and Single Parameter Estimation (SPE). The latter was found to be more accurate and efficient, and was applied in subsequent realworld case studies on road departures (Tarko, 2020) and rear-end collisions (Tarko and Lizarazo, 2021). For a thorough and rigorous theoretical justification of the distribution and the method applied, the reader is referred to Tarko (2018bTarko ( , 2019Tarko ( , 2021; more details on the crash estimation procedure are given in Section 3.3.…”

Section: From Traffic Conflicts To Crash Probabilitiesmentioning

confidence: 99%

Before-after safety analysis of a shared space implementation

Orsini

Batista

Friedrich

et al. 2023

Case Studies on Transport Policy

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Validity of failure-caused traffic conflicts as surrogates of rear-end collisions in naturalistic driving studies

Cited by 18 publications

References 18 publications

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Active Road User Interactions With Autonomous Vehicles: Proactive Safety Assessment

Before-after safety analysis of a shared space implementation

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