Modeling Analysis of Incident and Roadway Clearance Time

Zhou, Huaguo; Tian, Zhaofeng

doi:10.1016/j.sbspro.2012.04.108

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…PSE travel demand models are also studied at various scales, including microscopic, mesoscopic and macroscopic. The study by Zhou and Tian [26] who investigated the link between incident clearing time and highway clearance time by using micro-simulation software in order to run multiple traffic incident scenarios. Another study focuses on the microscopic models by Florez et al [27] who analyzed and highlight the most essential features related with the choice of walking as a mode of transportation, based on interviews done with spectators during three FIFA Confederations Cup matches performed in June 2013 at Maracan stadium in Rio de Janeiro.…”

Section: Literature Reviewmentioning

confidence: 99%

Joint Logit Model Approach to Analyze Soccer Spectators’ Arrival Time and Location Preferences for Interim Activities in Istanbul

2022

IJE

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Planned Special Event (PSE) is a public activity that has a defined location and time and has an influence on transportation system operations as a consequence of increases in travel demand or decreases in road capacity. Apart from the event itself, PSEs might generate additional activities based on location, time and duration of the event, and individual preferences. This paper focuses on the interim activities of soccer spectators in Istanbul. This paper is motivated by the mostly disregarded but significantly important demand for these activities by jointly analyzing the arrival time and location preferences for the interim activities carried out before the main activity. For this aim, a joint logit model capturing the factors influencing the arrival time and location choice collectively within the PSE circumstances is estimated. In this estimation, each trip and behavior of spectator groups are modeled separately. According to the results of the models, one significant and interesting finding is the behavioral differences of supporters of different teams which is mostly influenced by the activity opportunities present in the surrounding of the venues. Last motorized trips of the Besiktas and Fenerbahce's spectators end at the sub-centers in general, while the spectators of the Galatasaray prefer the stadium as their final destination. Moreover, league matches being on weekdays or weekends does not have a statistically significant effect on the choice of arrival time and location of the spectators. The findings provide useful information that might assist event organizers and decision-makers especially in planning special events.

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Section: Literature Reviewmentioning

confidence: 99%

Joint Logit Model Approach to Analyze Soccer Spectators’ Arrival Time and Location Preferences for Interim Activities in Istanbul

2022

IJE

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“…We should note that, although this paper specifically focuses on practical dataset, simulated datasets are another source of data for traffic incident duration time estimation and prediction [48]. The relationship between incident clearance time and roadway clearance time for different traffic incident scenarios were explored on the basis of micro-simulation VISSIM modelling [49]. Post-incident traffic recovery time along an urban freeway was estimated via a simulation due to the lack of practical datasets for post-incident recovery time [50].…”

Section: Available Data Resourcesmentioning

confidence: 99%

Overview of traffic incident duration analysis and prediction

Pereira

Ben‐Akiva³

2018

Eur. Transp. Res. Rev.

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Introduction: Non-recurrent congestion caused by traffic incident is difficult to predict but should be dealt with in a timely and effective manner to reduce its influence on road capacity reduction and enormous travel time loss. Influence factor analysis and reasonable prediction of traffic incident duration are important in traffic incident management to predict incident impacts and aid in the implementation of appropriate traffic operation strategies. The objective of this study is to conduct a thorough review and discusses the research evolution, mainly including the different phases of incident duration, data resources, and the various methods that are applied in the traffic incident duration influence factor analysis and duration time prediction. Methods: In order to achieve the goal of this study, we presented a systematic review of traffic incident duration time estimation and prediction methods developed based on various data resource, methodologies etc. Results: based on the previous studies, we analyse (i) Data resources and characteristics: different traffic incident time phases, data set size, incident types, duration time distribution, available data resources, significant influence factors and unobserved heterogeneity and randomness, (ii) traffic incident duration analysis methods, mainly including hazard-based duration model and regression and statistical tests, (iii) traffic incident duration prediction methods and evaluation of prediction accuracy. Conclusions: After a comprehensive review of literature, this study identifies and analyses future challenges and what can be achieved in the future to estimate and predict the traffic incident duration time.

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“…In this study, the incidents were simulated in Vissim with the use of bus stops with dwell time equal to incident dura tion on the blocked lanes. Incident modeling was combined with the reduced speed area on the adjacent lanes to emulate a driver slowing to observe the incident (15,17,28). The length of the "reduce speed area" in the lanes in the vicinity of the incident was modified by trial and error to achieve the mentioned expected drop in capacity resulting from the uninterrupted incident.…”

Section: Modeling Incidents In Microscopic Simulationmentioning

confidence: 99%

“…Queuing analysis has been used more widely to identify incident management benefits than has shock wave analysis (13,14). A variety of examples of the use of queuing analysis (13)(14)(15)(16)(17) and shock wave theory analysis (18,19) for assessments of freeway incident effects are available. Rakha and Zhang concluded that queuing theory provides a simple and accurate technique for estimating delays at highway bottlenecks (18).…”

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confidence: 99%

Estimating the Effects of Urban Street Incidents on Capacity

Massahi

Hadi

Cutillo

et al. 2017

Transportation Research Record

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The effect of incidents on capacity is the most critical parameter in estimating the influence of incidents on network performance. The Highway Capacity Manual 2010 (HCM 2010) provides estimates of the drop in capacity resulting from incidents as a function of the number of blocked lanes and the total number of lanes in the freeway section. However, there is limited information on the effects of incidents on the capacity of urban streets. This study investigated the effects on capacity of the interaction between the drop in capacity below demand at a midblock urban street segment location and upstream and downstream of signalized intersection operations. A model was developed to estimate the drop in capacity at the incident location as a function of the number of blocked lanes, the distance from the downstream intersection, and the green time–to–cycle length (g:C) ratio of the downstream signal. A second model was developed to estimate the reduction in the upstream intersection capacity resulting from the drop in capacity at the midblock incident location as estimated by the first model. The second model estimated the drop in capacity of the upstream links feeding the incident locations as a function of incident duration time, the volume-to-capacity (V/C) ratio at the incident location, and distance from an upstream signalized intersection. The models were developed on the basis of data generated with the use of a microscopic simulation model calibrated by comparison with parameters suggested in HCM 2010 for incident and no-incident conditions and by comparison with field measurements.

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Modeling Analysis of Incident and Roadway Clearance Time

Cited by 11 publications

References 6 publications

Joint Logit Model Approach to Analyze Soccer Spectators’ Arrival Time and Location Preferences for Interim Activities in Istanbul

Joint Logit Model Approach to Analyze Soccer Spectators’ Arrival Time and Location Preferences for Interim Activities in Istanbul

Overview of traffic incident duration analysis and prediction

Estimating the Effects of Urban Street Incidents on Capacity

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