Use of real-world connected vehicle data in identifying high-risk locations based on a new surrogate safety measure

Xie, Kun; Yang, Di; Özbay, Kaan; Yang, Hong

doi:10.1016/j.aap.2018.07.002

Cited by 100 publications

(47 citation statements)

References 12 publications

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“…This result represents a validation of the applied methodology, providing a link between the microsimulation and observational data. Moreover, these outlines are supported by other studies as in Xie et al [34].…”

Section: Discussion Of Resultssupporting

confidence: 88%

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Comparison Analysis between Real Accident Locations and Simulated Risk Areas in An Urban Road Network

Guido

Vitale

Astarita

et al. 2019

Safety

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Recently, many researchers have employed a microsimulation technique to study the chain of interactions among vehicles, which generates an accident occurrence in some circumstances. This new approach to studying road safety is named traffic conflict technique. The aim of this paper is to assess how the microscopic simulation is a useful tool to identify potentially unsafe vehicle interactions and how high-risk locations identified by a microsimulation technique are similar to the ones identified by using historical accident data. Results show that high-risk locations identified by the simulation framework are superimposable to those identified by using the historical accident database. In particular, the statistical analysis employed based on Pearson’s correlation demonstrates a significative correspondence between a risk rate defined with simulation and an accident rate determined by the observed accidents dataset.

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Section: Discussion Of Resultssupporting

confidence: 88%

“…A GEH of less than 5.0 is considered a good match between the modelled and observed hourly traffic flow volumes. According to [34], at least 85% of the volumes in a traffic model should have a GEH of less than 5.0.…”

Section: The Microsimulation Modelmentioning

confidence: 99%

Comparison Analysis between Real Accident Locations and Simulated Risk Areas in An Urban Road Network

Guido

Vitale

Astarita

et al. 2019

Safety

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“…In addition to the potential direct benefits such as improving mobility and safety, another advantage of CV technology is it can assist transportation managers to develop efficient and cost-effective traffic management strategies for various traffic and weather events in a timely manner. Examples of this potential include but not limited to: development of weatherresponsive variable speed limit (VSL) algorithms (Hammit et al, 2017), real-time identification of traffic operation status (Fountoulakis et al, 2017), and identification of high-risk locations (Xie et al, 2019). Nevertheless, in reality there is a trade-off between mobility and safety benefits (Tian et al, 2018), such as a lower VSL tends to reduce the risk of traffic crash, while it will also bring a longer delay.…”

Section: Discussionmentioning

confidence: 99%

Performance evaluation framework of Wyoming connected vehicle pilot deployment program: summary of Phase 2 pre-deployment efforts and lessons learned

Ahmed

Yang

Gaweesh

et al. 2019

JICV

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Purpose This paper aims to present a summary of the performance measurement and evaluation plan of the Wyoming connected vehicle (CV) Pilot Deployment Program (WYDOT Pilot). Design/methodology/approach This paper identified 21 specific performance measures as well as approaches to measure the benefits of the WYDOT Pilot. An overview of the expected challenges that might introduce confounding factors to the evaluation effort was outlined in the performance management plan to guide the collection of system performance data. Findings This paper presented the data collection approaches and analytical methods that have been established for the real-life deployment of the WYDOT CV applications. Five methodologies for assessing 21 specific performance measures contained within eight performance categories for the operational and safety-related aspects. Analyses were conducted on data collected during the baseline period, and pre-deployment conditions were established for 1 performance measures. Additionally, microsimulation modeling was recommended to aid in evaluating the mobility and safety benefits of the WYDOT CV system, particularly when evaluating system performance under various CV penetration rates and/or CV strategies. Practical implications The proposed performance evaluation framework can guide other researchers and practitioners identifying the best performance measures and evaluation methodologies when conducting similar research activities. Originality/value To the best of the authors’ knowledge, this is the first research that develops performance measures and evaluation plan for low-volume rural freeway CV system under adverse weather conditions. This paper raised some early insights into how CV technology might achieve the goal of improving safety and mobility and has the potential to guide similar research activities conducted by other agencies.

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“…Transportation modes can be inferred from mobile phones and other data (Stenneth et al 2011;Efthymiou et al 2019). Connected vehicle data may greatly reduce the time necessary for identifying high-risk locations, as compared with historical crash data (Xie et al 2019). Smart cards used for transit systems offer insights for passenger market segmentation (Kieu et al 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Emerging transportation technologies, such as mobilityas-a-service (MAAS), and connected and automated vehicles, might leverage big data to improve safety and other outcomes (Krishnamurthy et al 2017;Zmud et al 2018;Xie et al 2019;Legacy et al 2019). Transportation network companies also use big data to predict demand and allocate drivers and vehicles efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

J. Big Data Anal. Transp.

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Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

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Use of real-world connected vehicle data in identifying high-risk locations based on a new surrogate safety measure

Cited by 100 publications

References 12 publications

Comparison Analysis between Real Accident Locations and Simulated Risk Areas in An Urban Road Network

Comparison Analysis between Real Accident Locations and Simulated Risk Areas in An Urban Road Network

Performance evaluation framework of Wyoming connected vehicle pilot deployment program: summary of Phase 2 pre-deployment efforts and lessons learned

Mitigating Bias in Big Data for Transportation

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