Use of ubiquitous probe vehicle data for identifying secondary crashes

Yang, Hong; Wang, Zhenyu; Xie, Kun; Dai, Dong

doi:10.1016/j.trc.2017.06.016

Cited by 38 publications

(14 citation statements)

References 28 publications

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“…Several researchers have estimated flexible spatiotemporal thresholds based on the PI influence area using other dynamic methods such as speed contour, automatic tracking of moving jams, vehicle probe data, shock wave principles, and so forth ( 5, 18–22 ). These approaches take advantage of the traffic data retrieved from infrastructure-based traffic sensors.…”

Section: Existing Methods For Identification Of Secondary Crashesmentioning

confidence: 99%

Impact of Primary Incident Spatiotemporal Influence Thresholds on the Detection of Secondary Crashes

Kitali

Alluri

Sando

et al. 2019

Transportation Research Record

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Incident management agencies have been investing substantial amount of resources to devise strategies to mitigate secondary crashes (SCs). Nevertheless, detection of SCs is not a straightforward process, as the definition itself is subjective; identification of SCs depends on how the impact area of the primary incident (PI) is defined. Both static and dynamic methods, the two most common approaches used to define the impact area of the PI, have serious limitations that restrict their practical applications. Although the dynamic method is proven to yield accurate results, applying it requires real-time traffic data which are only available on limited locations. On the other hand, the static method’s one-size-fits-all approach of using fixed spatiotemporal thresholds does not yield reliable results. This study explored the impact of PI spatiotemporal influence thresholds on the detection of SCs. To implement the study objective, both static and dynamic approaches were developed. The static method was based on predefined spatiotemporal thresholds, and the dynamic method was based on prevailing traffic speed data from BlueToad® paired devices. Comparison of SC frequencies identified using the static and dynamic methods showed that the static method consistently under and overestimated SC frequencies for smaller and larger spatiotemporal thresholds, respectively. The prevailing traffic conditions were found to play a crucial role in instigating SCs, as more than 75% of SCs occurred during congested traffic conditions. Use of varying spatiotemporal thresholds depending on the prevailing traffic conditions is expected to reduce the biases associated with the subjective thresholds used in the static method.

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Section: Existing Methods For Identification Of Secondary Crashesmentioning

confidence: 99%

Impact of Primary Incident Spatiotemporal Influence Thresholds on the Detection of Secondary Crashes

Kitali

Alluri

Sando

et al. 2019

Transportation Research Record

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“…where, nor q , p q , and sat q respectively denote the normal flow before a PC occurs, the flow when a PC occurs, and the flow under road saturation, nor k , p k , and sat k represent the density, accordingly. The IA of a PC is defined using the triangular area constituted by three vertexes: ( , ) To further improve the identification performance, in a recent work by Yang et al (2017b), a data-driven analysis framework for the identification of SCs was developed. Clustering methods were firstly introduced to automatically classify unlabeled data archived from probe vehicles, and then intelligent approaches including multi-stage approximation algorithm, genetic algorithm, and ant colony algorithm were developed to estimate the boundary of the IA of a PC to further support the automatic identification of SCs.…”

Section: Shockwave-based Approachesmentioning

confidence: 99%

“…In fact, many transportation agencies are using SCs as an important indicator to monitor the safety performance of their systems. The frequency of SCs is used as a key factor in assessing a number of safety programs of the Federal Highway Administration (FHWA) and many state/local agencies consider the determination and reduction of SCs in allocating funding for the development of their traffic incident management (TIM) programs (Yang et al 2017b). For example, Arizona Department of Public Safety (AZPDS) used SCs in the agency's strategic plan and launched a specific program for the prevention of SCs (TIM 2017).…”

Section: Introductionmentioning

confidence: 99%

Methodological evolution and frontiers of identifying, modeling and preventing secondary crashes on highways

Yang

Wang

Xie

et al. 2018

Accident Analysis & Prevention

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Secondary crashes (SCs) or crashes that occur within the boundaries of the impact area of prior, primary crashes are one of the incident types that frequently affect highway traffic operations and safety. Existing studies have made great efforts to explore the underlying mechanisms of SCs and relevant methodologies have been evolving over the last two decades concerning the identification, modeling, and prevention of these crashes. So far there is a lack of a detailed examination on the progress, lessons, and potential opportunities regarding existing achievements in SC-related studies. This paper provides a comprehensive investigation of the state-of-the-art approaches; examines their strengths and weaknesses; and provides guidance in exploiting new directions in SC-related research. It aims to support researchers and practitioners in understanding well-established approaches so as to further explore the frontiers. Published studies focused on SCs since 1997 have been identified, reviewed, and summarized. Key issues concentrated on the following aspects are discussed: (i) static/dynamic approaches to identify SCs; (ii) parametric/non-parametric models to analyze SC risk, and (iii) deployable countermeasures to prevent SCs. Based on the examined issues, needs, and challenges, this paper further provides insights into potential opportunities such as: (a) fusing data from multiple sources for SC identification, (b) using advanced learning algorithms for real-time SC analysis, and (c) deploying connected vehicles for SC prevention in future research. This paper contributes to the research community by providing a one-stop reference for research on secondary crashes.

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“…Bitar uses NPMRDS to develop an optimum classifier to identify non-recurring congestion ( 4 ). Yang et al use probe vehicle data to identify secondary crashes ( 14 ). Li and Chen use a data-mining approach to predict travel times under non-recurring congestion by looking at travel-time data collected on small segments between dual loop detectors ( 15 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Using National Performance Management Research Data Set for Corridor Performance Measures: A US-281N Corridor Case Study

Pandey

Juri

2018

Transportation Research Record

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The National Performance Management Research Data Set (NPMRDS), made available by Federal Highway Administration in 2013, provides fine-resolution travel-time data, which have been used in numerous network performance management and operations applications. This article discusses corridor-level performance measures computed using the NPMRDS. Three measures are analyzed on a 20.2-mile long corridor in San Antonio, Texas, including corridor travel time, corridor travel-time reliability, and day-to-day variation in travel time. The primary contributions of this article are the analysis of the impact of using two different approaches for travel-time aggregation across segments—instantaneous and time-dependent approaches—and defining a mean absolute error-based method to identify days when travel times significantly deviate from typical traffic conditions. The findings suggest that the temporal patterns of corridor travel times obtained using instantaneous and time-dependent aggregation approaches are similar; however, instantaneous travel-time estimates lead to inaccuracies that become more apparent during peak hours and for longer segments. In addition, it is found that a [Formula: see text]-means clustering analysis performed on daily travel-time profiles provides a useful statistic for corridor performance analysis. Using this methodology, 9.23% of weekdays in the 20-month study period are classified as atypical for the corridor. The numerical results reinforce the value of the NPMRDS in estimating corridor performance measures and highlight potential limitations of traditional techniques for evaluating corridor performance measures when applied in practice to support enhanced traffic planning and operations.

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Use of ubiquitous probe vehicle data for identifying secondary crashes

Cited by 38 publications

References 28 publications

Impact of Primary Incident Spatiotemporal Influence Thresholds on the Detection of Secondary Crashes

Impact of Primary Incident Spatiotemporal Influence Thresholds on the Detection of Secondary Crashes

Methodological evolution and frontiers of identifying, modeling and preventing secondary crashes on highways

Using National Performance Management Research Data Set for Corridor Performance Measures: A US-281N Corridor Case Study

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