Estimating Magnitude and Duration of Incident Delays

Garib, Atef M.; Radwan, A E; Al-Deek, Haitham

doi:10.1061/(asce)0733-947x(1997)123:6(459)

Cited by 170 publications

(86 citation statements)

References 4 publications

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“…On the basis of these previous studies (see also [10,11]), it can be concluded that each method seems to have its own strengths and weaknesses, thus no single method is expected to be the best method under all circumstances. If the full incident duration prediction horizon is to be covered, a combination of methods seems to be the best option.…”

Section: Previous Studies On Incident Duration Predictionmentioning

confidence: 97%

A comparative study of models for the incident duration prediction

Valenti

Lelli

Cucina

2010

Eur. Transp. Res. Rev.

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Purpose This study is intended to investigate the reliability of different incident duration prediction models for real time application with a view to contribute to the development of a decision aid tool within the incident management process context where rough incident duration estimates are currently provided by traffic operators or police on the basis of their skill and past experience. Methods Five predictive models, ranging from parametric models, to non-parametric and neural network models, have been considered and compared evaluating their capacity of predicting incident duration. The data set used in this study for developing and testing the prediction models includes 237 incident events and contains information about the incident characteristics, the personnel and equipment involved to clear the incident and the related response times, including the beginning and ending time of the incident. Results Testing results have demonstrated that the proposed models are able to achieve good performance in terms of prediction accuracy especially for incidents with duration less than 90 min. This finding is partly due to the fact that the dataset has a relatively small number of severe incidents. Furthermore a linear combination of predictions from models was applied with negligible gain in accuracy. Conclusions A deeper investigation is suggested for a future work to evaluate potential improvements from the application of other combination methods. Moreover each proposed model is able to reach best performance for incidents within a particular duration range. Thus a preliminary incident classification scheme could be more convenient in order to select the more appropriate prediction model.

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Section: Previous Studies On Incident Duration Predictionmentioning

confidence: 97%

A comparative study of models for the incident duration prediction

Valenti

Lelli

Cucina

2010

Eur. Transp. Res. Rev.

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“…Traffic incident duration is defined as the elapsed time from the moment an incident is detected until its cause is removed from the scene (Garib et al, 1997;Nam and Mannering, 2000;Smith and Smith, 2001). Charles (2007) defined traffic incident duration in broader terms as the length of time between incident occurrence and return to normal traffic flow as shown in Figure 3.3.…”

Section: Traffic Incident Duration Analysismentioning

confidence: 99%

“…One of the most well-known linear regression models for incident duration was developed by Garib et al (1997) The indication of goodness of fit (R 2 ) of this model was reported as 0.81 indicating promising results. However, the applicability of this model was limited because of the relatively smaller sample size used.…”

Section: Linear Regression Analysesmentioning

confidence: 99%

“…The authors emphasised that the quality of the traffic incident data was critical for the accuracy of the process. Garib et al (1997) developed two separate regression models to estimate the traffic incident delay. These models were based on data from a section along the I-880 freeway in California.…”

Section: Heuristic Analysismentioning

confidence: 99%

“…As discussed in Chapter 3; a number of studies (Garib et al, 1997;Sullivan, 1997;Qi et al, 2009) have highlighted the significance of the 'number of lanes blocked' in the analysis of traffic incidents. This factor was related to the severity of incidents.…”

Section: Empirical Settingmentioning

confidence: 99%

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Modelling the impact of traffic incidents on travel time reliability

Hojati

Ferreira

Washington

et al. 2016

Transportation Research Part C: Emerging Technologies

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Assessing and prioritising cost-effective strategies to mitigate the impacts of non-recurrent congestion on major roads, due to traffic incidents, represents a significant challenge for road network managers. In addition, traffic incidents impose significant unexpected changes in the travel time of travellers.Travel Time Reliability (TTR) has become one of the most important indicators of transport performance. In this regard, the impact of traffic incidents on TTR is crucial for both operators and travellers. An extensive literature review indicated that there is a lack of relevant research in this area. To address and improve the knowledge in this area, this thesis established a four-stage logical framework to achieve the main objective of this research. The research was aimed at modelling traffic incident impacts and quantifying the effects of traffic incidents on TTR on freeways.Based on historical data from an 'integrated database', a robust methodology to identify recurrent and non-recurrent congestion and to recognise traffic incident related congestion was proposed. Further, a number of attributes relating to traffic incidents and traffic measures for both recurrent and non-recurrent congestion were extracted to quantify the impacts of traffic incidents, including incident duration and buffer time as a measure of TTR. This facilitated insight into the factors that affect these two important impacts of incidents.Extra Buffer Time was defined to calculate the extra travel time caused by traffic incidents.This reliability measure indicates how much time is required by travellers to arrive at their destination on time with 95% certainty, in the case of an incident, over and above the travel time that would have been taken under recurrent conditions. The Extra Buffer Time Index (EBTI) was calculated to show the ratio of changes in TTR in the case of an incident.A hazard-based duration modelling approach was considered to model incident duration.Parametric accelerated failure time survival models were developed to consider heterogeneity across duration in the hazard function and in the explanatory variables. In addition, a new approach was proposed to model EBTI using a Tobit model to understand the factors affecting EBTI. Moreover, this research considered both fixed and random parameters to capture unobserved heterogeneity across data. A validation process was performed to assess the level of accuracy of the results of the prediction models.ii An integrated database was established using historical data from different sources including 12 months of incident data, 18 months of traffic data, and weather data for the same period for an Australian freeway network. The results of the analysis showed that the significant variables affecting incident duration include characteristics of the incidents (severity, type, injury, medical required, etc.), infrastructure characteristics (shoulder availability), location, time of day, and traffic characteristics. Moreover, the findings revealed no significant effect...

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Examination of factors affecting freeway incident clearance times: a comparison of the generalized F model and several alternative nested models

Ghosh

Savolainen

Gates

2012

J of Advced Transportation

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Traffic incidents are a principal cause of congestion on urban freeways, reducing capacity and creating risks for both involved motorists and incident response personnel. As incident durations increase, the risk of secondary incidents or crashes also becomes problematic. In response to these issues, many road agencies in metropolitan areas have initiated incident management programs aimed at detecting, responding to, and clearing incidents to restore freeways to full capacity as quickly and safely as possible. This study examined those factors that impact the time required by the Michigan Department of Transportation Freeway Courtesy Patrol to clear incidents that occurred on the southeastern Michigan freeway network. These models were developed using traffic flow data, roadway geometry information, and an extensive incident inventory database. A series of parametric hazard duration models were developed, each assuming a different underlying probability distribution for the hazard function. Although each modeling framework provided results that were similar in terms of the direction of factor effects, there was significant variability in terms of the estimated magnitude of these impacts. The generalized F distribution was shown to provide the best fit to the incident clearance time data, and the use of poorer fitting distributions was shown to result in severe over-estimation or under-estimation of factor effects. Those factors that were found to impact incident clearance times included the time of day and month when the incident occurred, the geometric and traffic characteristics of the freeway segment, and the characteristics of each incident.Note: Data represent 422 1-mile freeway segments. a Radius corresponds to those 248 segments that include at least one horizontal curve.Note: Values indicate degree to which parameters over-estimate or under-estimate effects compared with generalized F.

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Estimating Magnitude and Duration of Incident Delays

Cited by 170 publications

References 4 publications

A comparative study of models for the incident duration prediction

A comparative study of models for the incident duration prediction

Modelling the impact of traffic incidents on travel time reliability

Examination of factors affecting freeway incident clearance times: a comparison of the generalized F model and several alternative nested models

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