Assessment of non-recurrent traffic congestion caused by freeway work zones and its statistical analysis with unobserved heterogeneity

Chung, Younshik

doi:10.1016/j.tranpol.2011.02.003

Cited by 22 publications

(14 citation statements)

References 16 publications

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“…At extant traffic condition (congestion) can be classified as either recurrent or non-recurrent. A definition of non-recurrent congestion is as follows; delay caused by an incident, a work zone, adverse weather, or other non-repetitive event [12,[19][20][21][22][23][24][25]. Chung [21] defines non-recurrent congestion as the extra delay caused by incidents compared with the annual average section travel speed.…”

Section: Literature Reviewmentioning

confidence: 99%

Crash Classification by Congestion Type for Highways

Song

Kim²,

Williams

et al. 2020

Applied Sciences

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Effective management of highway networks requires a thorough understanding of the conditions under which vehicular crashes occur. Such an understanding can and should inform related operational and resource allocation decisions. This paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. The classification methodology uses link-based speed data. Unlike previous secondary collision identification schemes, it neither requires an a priori identification of the precipitating incident nor definition of the precipitating incident’s impact area. To accomplish this objective, the methodology makes use of a novel scheme for distinguishing between recurrent and non-recurrent congestion. A 500-crash case study was performed using a 274 km section of the I-40 in North Carolina. Twelve percent of the case study crashes were classified as occurring in non-recurrent congestion. Thirty-seven percent of the crashes in non-recurrent congestion classified were identified within unreported primary incidents or crashes influence area. The remainder was classified as primary crashes occurring in either uncongested conditions (84%) or recurrent congestion (4%). The methodology can be implemented in any advanced traffic management system for which crash time and link location are available along with corresponding archived link speed data are available.

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Section: Literature Reviewmentioning

confidence: 99%

Crash Classification by Congestion Type for Highways

Song

Kim²,

Williams

et al. 2020

Applied Sciences

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“…However, survival analysis can be applied to a wide variety of random variables beyond those strictly representing the end of life or failure of a machine. Applications in the transportation area include, for example, the occurrence of traffic crashes [24,25], incident duration [26][27][28][29][30], households' vehicle ownership duration [31][32][33][34], and traffic congestion [3,23,[35][36][37][38].…”

Section: Survival Analysismentioning

confidence: 99%

Identification of Critical Factors for Non-Recurrent Congestion Induced by Urban Freeway Crashes and Its Mitigating Strategies

Chung

2017

Sustainability

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Given the extreme difficulty of estimating crash likelihoods, the most important aspect of the development of congestion management strategies is the identification of the factors that affect non-recurrent congestion caused by crashes. Such factors must be identified to develop crash management strategies and congestion management strategies. The objectives of this study are to identify causal factors that affect non-recurrent congestion and to propose some operational strategies for mitigating crash-induced non-recurrent traffic congestion. To achieve these objectives, a case study was conducted to identify spatiotemporal non-recurrent congestion regions using a previously developed method based on historical inductance loop detector data collected from six major freeways in Orange County, California. Based on the case study results, potentially significant factors in non-recurrent congestion were identified using the Cox proportional hazard model. Additionally, with the factors identified as significant, operational strategies were proposed for mitigating non-recurrent congestion due to freeway crashes.

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“…; n obs < n min obs (1) whereŝ j (t m ) is any speed not drawn from the distribution of s jn (t m ),s j (t m ) and σ s j (t m ) are the mean and standard deviation speeds drawn from the distribution of s jn (t m ), and α is a positive value. Additionally, n min obs represents a set a threshold regarding the minimum number of observations in order to have some confidence in the statistical calculations for mean and standard deviation.…”

Section: ) Step 3-identification Of Real Congested Regionmentioning

confidence: 99%

“…Therefore, the capacity reduction based on the number of closed lanes may be meaningless in this case, and the estimation of nonrecurrent traffic congestion by using this approach may lead to erroneous results [1]. Moreover, these methods make it difficult to separate the genuine amount of non-recurrent congestion from the identified congestion area when an accident occurred in a recurrently congested area.…”

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confidence: 99%

Frailty Models for the Estimation of Spatiotemporally Maximum Congested Impact Information on Freeway Accidents

Chung

Recker

2015

IEEE Trans. Intell. Transport. Syst.

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The objective of this paper is to develop models for the estimation of the temporal and spatial extent of congestion impact caused by accidents. Although there have been various approaches based on the deterministic queuing diagrams and kinematic wave (or shockwave) theory, only a few studies have been able to estimate the spatiotemporal congested region based on field data, such as ubiquitous loop detector data. Accordingly, this paper applies a previously developed procedure to capture the spatiotemporal accident impacts based on binary integer programming (BIP). The procedure provides a foundation for models of the following: 1) maximum spatial distance to the end of the congestion region affected by each accident and 2) maximum time affected by congestion resulting from each accident. Based on these models, the objective of this paper is to estimate two statistical models for providing maximum congested distance and time information due to freeway accidents. Since various observations from BIP were censored with respect to time and space, survival analysis-specifically, frailty models to account for unobserved heterogeneity-is applied to identify factors critical to spatiotemporal congestion impacts of freeway accidents.

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Assessment of non-recurrent traffic congestion caused by freeway work zones and its statistical analysis with unobserved heterogeneity

Cited by 22 publications

References 16 publications

Crash Classification by Congestion Type for Highways

Crash Classification by Congestion Type for Highways

Identification of Critical Factors for Non-Recurrent Congestion Induced by Urban Freeway Crashes and Its Mitigating Strategies

Frailty Models for the Estimation of Spatiotemporally Maximum Congested Impact Information on Freeway Accidents

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