Relation between traffic density and capacity drop at three freeway bottlenecks

Chung, Kung Ming; Rudjanakanoknad, Jittichai; Cassidy, Michael J.

doi:10.1016/j.trb.2006.02.011

Cited by 215 publications

(105 citation statements)

References 7 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…Experimental findings show that capacity drops are often observed at merges even if downstream traffic conditions are in free-flow, e.g. (Cassidy and Bertini, 1999;Kerner, 2002;Chung et al, 2007;Sarvi et al, 2007;Zheng et al, 2011). The magnitude of the capacity drops is mentioned to be between 10 to 30% of the maximal observed flow.…”

Section: Introductionmentioning

confidence: 91%

Capacity drops at merges: New analytical investigations

Leclercq

Knoop

Marczak

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

This paper focuses on the derivation of analytical formulae to estimate the effective capacity at freeway merges. It extends previous works by proposing a generic framework able to account for (i) heterogeneous vehicle characteristics and (ii) refined description of the physical interactions between upstream waves and downstream voids created by inserting vehicles within the merge area. The provided analytical formulae permit to directly compute the capacity values when the merge is self-active, i.e. when both upstream roads are congested while downstream traffic conditions are free-flow. They show that accounting for vehicle heterogeneity is not necessary when only the mean capacity is targeted. Calculations with the proper mean value for all parameters provide accurate estimates. This result is appealing because the shape of the parameter distributions does not need to be calibrated. However, this paper also shows that vehicle heterogeneity plays a major role in the flow dynamics just upstream of the merge.

show abstract

Section: Introductionmentioning

confidence: 91%

Capacity drops at merges: New analytical investigations

Leclercq

Knoop

Marczak

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…There is a relation between the capacity of a freeway and the density/flow of vehicles [36,37]. Using capacity and density it is easy to detect flowing traffic, when the proposed system cannot detect it.…”

Section: Figure 10mentioning

confidence: 99%

“…However, our system can find traffic/number of vehicles within a specific zone without any initializing and is therefore able to detect bottlenecks. Our method of finding traffic is approximately the same as in other studies, such as [36,37], but the detection of cars is an image processing system that is intelligent and finds normal/heavy traffic using feedback.…”

Section: Figure 10mentioning

confidence: 99%

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

2012

View full text Add to dashboard Cite

Abstract. One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%.

show abstract

“…This phenomenon is called the capacity drop. It is described extensively in literature and estimations for the reduction vary, but are typically around 10% (Cassidy and Bertini, 1999, Chung et al, 2007, Dijker et al, 1997, Hall and Agyemang-Duah, 1991. In the analyses we will only use the queue discharge rate.…”

Section: Queue Discharge Ratementioning

confidence: 99%

Microscopic Traffic Behaviour near Incidents

Knoop

Zuylen

Hoogendoorn

2009

Transportation and Traffic Theory 2009: Golden Jubilee

View full text Add to dashboard Cite

Much of the delays on road networks are caused by incidents. This is partially caused by blockage or closure of lanes, but also by the change of driving behaviour in the remaining lanes. This contribution analyses traffic flow conditions near an incident both microscopically and macroscopically. A theory is proposed to describe drivers' behaviour, which is tested using traffic data of individual vehicles, collected using a helicopter. A bimodal headway distribution is observed, centred around two mean values, 2 seconds and 4 seconds. To understand the underlying mechanisms a car-following model is fitted to the drivers' behaviour. The model parameters show that the reaction time is much higher than usual. Using this model-based analysis, we conclude that the incident distracts the drivers and less attention is paid to the driving process. The consequence is that the queue discharge rate for the unblocked lanes is 30% lower than the usual queue discharge rate per lane.

show abstract

Relation between traffic density and capacity drop at three freeway bottlenecks

Cited by 215 publications

References 7 publications

Capacity drops at merges: New analytical investigations

Capacity drops at merges: New analytical investigations

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

Microscopic Traffic Behaviour near Incidents

Contact Info

Product

Resources

About