Modeling the Effects of Weather and Traffic on the Risk of Secondary Incidents

Vlahogianni, Eleni I.; Karlaftis, Matthew G.; Orfanou, Foteini

doi:10.1080/15472450.2012.688384

Cited by 70 publications

(49 citation statements)

References 21 publications

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“…In addition, the following four variables are added. Traffic condition variables were found to influence significantly the probability of having a secondary incident (Vlahogianni et al, 2012). In contrast to Vlahogianni et al's (2012) models, this study uses sequentially predicted clearance duration to predict the probability of having a secondary incident.…”

Section: Model Resultsmentioning

confidence: 91%

“…The developed model can be extended to analyze relative significance of the different input variables to the performance of the secondary incident prediction model (see Vlahogianni et al, 2012;Park et al, 2015 revision). These information can be used to reveal mutual relationship between incidents at each site.…”

Section: Conclusion and Future Studymentioning

confidence: 99%

“…As previous models (e.g. Vlahogianni et al, 2012) consider the spatial and temporal influences of a primary incident on road users, our analytical models capture dynamics in traffic congestion and detect potential secondary incidents. We sequentially predict clearance times considering influential factors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time prediction of secondary incident occurrences using vehicle probe data

Park

Haghani

2016

Transportation Research Part C: Emerging Technologies

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Section: Model Resultsmentioning

confidence: 91%

Section: Conclusion and Future Studymentioning

confidence: 99%

See 1 more Smart Citation

Real-time prediction of secondary incident occurrences using vehicle probe data

Park

Haghani

2016

Transportation Research Part C: Emerging Technologies

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“…This helps the safety researches better understand the traffic flow state before the crash. Vlahogianni et al (2012) also developed neural network models by analyzing different variables associated with secondary accident likelihood and suggested that traffic speed/duration of the primary accident, hourly volume, rainfall intensity and number of vehicles involved in the primary accident are the top associated factors. Hossain and Muromachi (2012) pointed out some of the major shortcomings of the previous models including location of detectors, variable space and modelling methods which result in the implemented scenario being impractical.…”

Section: Introductionmentioning

confidence: 99%

Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction

Fang

Xie

Wang

et al. 2016

Accident Analysis & Prevention

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“…Studies have found that the factors significantly associated with the occurrence of secondary incidents are primary incident characteristic (i.e., duration, incident type, and lane blockage etc. ), the number and type of involved vehicles, traffic demand, road geometry, land use, season, and weather conditions (Karlaftis et al, 1999;Khattak et al, 2009Khattak et al, , 2010Raub, 1997;Vlahogianni et al, 2012;Zhan et al, 2009). However, multiple associated incidents, that is, primary-secondary incident pairs, have not been analyzed fully.…”

Section: Introductionmentioning

confidence: 99%

Joint Analysis of Queuing Delays Associated With Secondary Incidents

Zhang

Cetin

Khattak

2015

Journal of Intelligent Transportation Systems

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Incidents cause substantial travel delays on urban freeways. Travel delays due to incidents are typically estimated for each incident independently. However, when multiple incidents occur on the same stretch of a roadway, treating them independently and ignoring their interactions leads to inaccurate results, which ultimately may lead to inefficient incident management strategies. This study examines delays due to primary-secondary incident pairs that occur on the same stretch of a freeway within a short time gap. Depending on their correlation in time and space, they can have varying degrees of impacts on the traffic flow. This article assesses the total delays induced by primary-secondary incident pairs by jointly modeling their occurrences. First, incident data combined with roadway inventory data from Hampton Roads, Virginia, are analyzed to understand the attributes of primary-secondary incident pairs, e.g., durations, lane blockages, and time gaps (between start times of the primary and its secondary incidents). Then using microscopic simulation as a primary evaluation tool, the effects of three critical parameters are investigated: time gap, distance between primary and secondary incidents, and traffic demand level. The historical incident analysis shows that, on average, primary-secondary incident pairs have substantially longer durations than single incidents. The joint analysis of queuing delays induced by primary incident and its secondary incident suggests that time gap and distance between a primary incident and its secondary incident are significantly associated with the total delays after controlling for other factors (e.g., the two associated incidents' durations and lane blockages). Furthermore, based on the scenarios tested (i.e., two incidents both blocking the right lane in a three-lane basic freeway section), it is found that the sum of delays induced by a primary incident and its secondary incident separately will overor underestimate the actual delays. For those secondary incidents that end after their associated primary incidents, total delays increase as time gap increases; increasing the distance between primary and secondary incidents is associated with reduction in delays. Other results and the implications of the findings for traffic operations are presented in the article.

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Modeling the Effects of Weather and Traffic on the Risk of Secondary Incidents

Cited by 70 publications

References 21 publications

Real-time prediction of secondary incident occurrences using vehicle probe data

Real-time prediction of secondary incident occurrences using vehicle probe data

Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction

Joint Analysis of Queuing Delays Associated With Secondary Incidents

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