Delays Caused by Incidents

Snelder, M.; Bakri, Taoufik; Arem, Bart van

doi:10.3141/2333-01

Cited by 27 publications

(16 citation statements)

References 15 publications

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“…Adding an additional lane in combination with creating a parallel road structure reduces the congestion under regular conditions with 53% and improves the robustness by 53% if a hard shoulder is present and by 49% if hard shoulder is not present. Because of the high congestion level under regular conditions, the relative impact of delays caused by incidents is low (3.9% to 5.9%) compared to other roads in the Netherlands since the average share of incidents in the total travel time delays is 20-25% in the Netherlands (Snelder, et al, 2013). Nevertheless, this can make a difference between positive and negative benefit-cost ratios of the project alternatives and the ranking of the project alternatives.…”

Section: Discussionmentioning

confidence: 99%

“…Thereafter, the marginal incident module is calibrated based on the results of a data-analysis of incidents that occurred in 2010 and 2011 on the A12 and the A27. With the method that is described in Snelder, et al (2013) the total delay caused by each of those incidents was computed. This was compared with the results of the marginal incident module.…”

Section: Model Assumptions and Calibrationmentioning

confidence: 99%

“…In many urbanized areas, incidents and other disturbances can cause large delays. In the Netherlands incidents are responsible for 20-25% of the total travel time delay (Snelder, et al, 2013) and roadworks are responsible for 4% of the total travel time delay. In the United States of America, 39% of the total travel time delay is caused by traffic incidents, 18% by different weather conditions and 1% by work zones (TRB, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Section: Discussionmentioning

confidence: 99%

Section: Model Assumptions and Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the robustness effects of infrastructure projects based on their topological and geometrical roadway designs

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“…So, this method also requires traffic incident dataset with accurate start and end time of the incidents (which are often hard to get) for incident detection. Snelder et al (2013) used median instead of mean in order to achieve robust summary statistics computation and used it as a reference case to find out delays caused by incidents.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven parallelizable traffic incident detection using spatio-temporally denoised robust thresholds

Chakraborty

Hegde

Sharma

2019

Transportation Research Part C: Emerging Technologies

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Automatic incident detection (AID) is crucial for reducing non-recurrent congestion caused by traffic incidents. In this paper, we propose a data-driven AID framework that can leverage large-scale historical traffic data along with the inherent topology of the traffic networks to obtain robust traffic patterns. Such traffic patterns can be compared with the real-time traffic data to detect traffic incidents in the road network. Our AID framework consists of two basic steps for traffic pattern estimation. First, we estimate a robust univariate speed threshold using historical traffic information from individual sensors. This step can be parallelized using MapReduce framework thereby making it feasible to implement the framework over large networks. Our study shows that such robust thresholds can improve incident detection performance significantly compared to traditional threshold determination. Second, we leverage the knowledge of the topology of the road network to construct threshold heatmaps and perform image denoising to obtain spatio-temporally denoised thresholds. We used two image denoising techniques, bilateral filtering and total variation for this purpose. Our study shows that overall AID performance can be improved significantly using bilateral filter denoising compared to the noisy thresholds or thresholds obtained using total variation denoising. AbstractAutomatic incident detection (AID) is crucial for reducing non-recurrent congestion caused by traffic incidents. In this paper we propose a data-driven AID framework that can leverage large-scale historical traffic data along with the inherent topology of the traffic networks to obtain robust traffic patterns. Such traffic patterns can be compared with the real-time traffic data to detect traffic incidents in the road network. Our AID framework consists of two basic steps for traffic pattern estimation. First, we estimate robust univariate speed threshold using historical traffic information from individual sensors. This step can be parallelized using MapReduce framework thereby making it feasible to implement the framework over large networks. Our study shows that such robust thresholds can improve incident detection performance significantly compared to traditional threshold determination. Second, we leverage the knowledge of the topology of the road network to construct threshold heatmaps and perform image denoising to obtain spatio-temporally denoised thresholds. We used two image denoising techniques, bilateral filtering and total variation for this purpose. Our study shows that overall AID performance can be improved significantly using bilateral filter denoising compared to the noisy thresholds or thresholds obtained using total variation denoising.

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“…In another study Snelder et al (2013) estimated the recurrent delay by naive clustering methods using loop detectors as well as traffic incident data. The traffic incident delay in terms of vehicle per hour was calculated based on differences of total delay from speed profiles and recurrent delay.…”

Section: Heuristic Analysismentioning

confidence: 99%

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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Delays Caused by Incidents

Cited by 27 publications

References 15 publications

Evaluating the robustness effects of infrastructure projects based on their topological and geometrical roadway designs

Evaluating the robustness effects of infrastructure projects based on their topological and geometrical roadway designs

Data-driven parallelizable traffic incident detection using spatio-temporally denoised robust thresholds

Modelling the impact of traffic incidents on travel time reliability

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