Simulation-Based Secondary Incident Filtering Method

Chou, Chih-Sheng; Miller-Hooks, Elise

doi:10.1061/(asce)te.1943-5436.0000134

Cited by 36 publications

(17 citation statements)

References 3 publications

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“…Static 2-mile and 15 minutes Chang et al 37 Static 2-mile, 2 hours and 0.5-mile, 0.5 hour (opposite direction) Sun et al 30 Dynamic Incident progression curve Zhang et al 38 Dynamic Queue length estimations Zhan et al 34 Dynamic Cumulative arrival and departure plots Chou et al 39 Dynamic Simulation-based methods Yang et al 40 , Xu et al 41 , Kitali et al 42 , Chung et al 31…”

Section: Discussionmentioning

confidence: 99%

Point-Process modeling of Secondary Crashes

Motagi¹,

Namilae²,

Gbaguidi³

et al. 2021

Preprint

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Secondary crashes or crashes that occur in the wake of a preceding or primary crash are among the most critical incidents occurring on highways, due to the exceptional danger they present to the first responders and victims of the primary crash. In this work, we developed a self-exciting temporal point process to analyze crash events data and classify it into primary and secondary crashes. Our model uses a self-exciting function to describe secondary crashes while primary crashes are modeled using a background rate function. We fit the model to crash incidents data from the Florida Department of Transportation, on Interstate-4 (I-4) highway for the years 2015-2017, to determine the model parameters. These are used to estimate the probability that a given crash is secondary crash and to find queue times. To represent the periodically varying traffic levels and crash incidents, we model the background rate, as a stationary function, a sinusoidal non-stationary function, and a piecewise non-stationary function. We show that the sinusoidal non-stationary background rate fits the traffic data better and replicates the daily and weekly peaks in crash events due to traffic rush hours. Secondary crashes are found to account for up to 15.09% of the traffic incident, depending on the city on the I4 Highway.

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Section: Discussionmentioning

confidence: 99%

Point-Process modeling of Secondary Crashes

Motagi¹,

Namilae²,

Gbaguidi³

et al. 2021

Preprint

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“…For instance, the DM which use queue length estimations need detailed queuing information. However, this information is not always available [10], [6], [15]- [17]. Another DM which use incident progression curve utilize only one identical curve for all SC, which may lead to unreliable results [10].…”

Section: A Two-step Identification Methods Of Sc a The Initial Idmentioning

confidence: 99%

“…However, this information is not always available [10], [6], [15]- [17]. Another DM which use incident progression curve utilize only one identical curve for all SC, which may lead to unreliable results [10]. In order to overcome the drawbacks accompanied with the STM and DM, this study applied a method based on the speed contour figure to initially identify SC.…”

Section: A Two-step Identification Methods Of Sc a The Initial Idmentioning

confidence: 99%

“…However, the limitation of STM is that the determination of temporal and spatial range is too subjective to give a objective The associate editor coordinating the review of this manuscript and approving it for publication was Xu Chen. and accuracy identification of SC [1], [8]- [10]. Recently, a number of researchers proposed different dynamic methods (DM) to overcome the limitation of STM, such as automatic tracking of moving jams, speed contour, shock wave principles and vehicle probe data [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Freeway Secondary Crashes With a Two-Step Method by Loop Detector Data

Yang

Guo

2019

IEEE Access

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A two-step identification method of secondary crashes (SCs) is developed in this paper, and the effects of traffic variables on the SC risks on freeways are captured. Crash-related data were obtained from the I-880 freeway in California, USA. Combined with the speed contour map, the SCs were identified by the real shock wave speed index and the SC identification index. The random effect logit regression was applied to analyze the significantly contributing factors and their mechanism on the SCs. The results showed that the significantly contributing factors are different between the initial SC model and SC models (Threshold = 20mile/h, 15mile/h, 10mile/h, 5mile/h), except for the variable of the standard deviation of 30s vehicle speed during 5-10 min. In the SC models, the number of significant contributing factors increases with the increase in the threshold value. The results of the elasticity analysis showed that the elasticity values of the hit object crash, wet surface road, the average 30-s vehicle speed during 5-10 min, and the average 30-s vehicle count during 5-10 min are greater than 10%.

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“…Dynamic thresholds overcome some of these limitations [23,24,29,30]. Chou and Miler-Hooks [29] created a "simulation-based secondary incident filtering" method (SPSIF).…”

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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Simulation-Based Secondary Incident Filtering Method

Cited by 36 publications

References 3 publications

Point-Process modeling of Secondary Crashes

Point-Process modeling of Secondary Crashes

Analysis of Freeway Secondary Crashes With a Two-Step Method by Loop Detector Data

Crash Classification by Congestion Type for Highways

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