Some traffic features at freeway bottlenecks

Cassidy, Michael J.; Bertini, Robert L.

doi:10.1016/s0191-2615(98)00023-x

Cited by 457 publications

(292 citation statements)

References 7 publications

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“…We can easily understand this phenomenon at a junction where two roads merge in one : if there are too many vehicles trying to access the same road, there is a sort of mutual embarrassment between the drivers which results in an outgoing flow lower than the optimal possible flow. This phenomenon has been experimentally observed (see Cassidy and Bertini (1999) and Hall and Agyemang-Duah (1991)). The flow decrease, which may range up to 15 %, has a considerable influence when considering traffic control (Papageorgiou (2002)).…”

Section: Introductionsupporting

confidence: 57%

A Macroscopic Traffic Model for Road Networks With a Representation of the Capacity Drop Phenomenon at the Junctions

Haut

Bastin²,

Chitour

2005

IFAC Proceedings Volumes

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Section: Introductionsupporting

confidence: 57%

A Macroscopic Traffic Model for Road Networks With a Representation of the Capacity Drop Phenomenon at the Junctions

Haut

Bastin²,

Chitour

2005

IFAC Proceedings Volumes

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“…They observed that flow immediately downstream of the bottlenecks decreased by a small amount at the breakdown point (i.e., PQF [ QDF), which is termed as ''capacity drop'' [20,21]. Similarly, Cassidy and Hall et al [22,23] claimed that there was an approximately 10 % reduction in maximum flow rates after the onset of congestion. Such a flow breakdown appeared to be triggered by speed instability.…”

Section: Breakdown Capacity Methodologymentioning

confidence: 99%

An analysis of four methodologies for estimating highway capacity from ITS data

Zhao

Laurence

2015

J. Mod. Transport.

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With the recent advent of Intelligent Transportation Systems (ITS), and their associated data collection and archiving capabilities, there is now a rich data source for transportation professionals to develop capacity values for their own jurisdictions. Unfortunately, there is no consensus on the best approach for estimating capacity from ITS data. The motivation of this paper is to compare and contrast four of the most popular capacity estimation techniques in terms of (1) data requirements, (2) modeling effort required, (3) estimated parameter values, (4) theoretical background, and (5) statistical differences across time and over geographically dispersed locations. Specifically, the first method is the maximum observed value, the second is a standard fundamental diagram curve fitting approach using the popular Van Aerde model, the third method uses the breakdown identification approach, and the fourth method is the survival probability based on product limit method. These four approaches were tested on two test beds: one is located in San Diego, California, U.S., and has data from 112 work days; the other is located in Shanghai, China, and consists of 81 work days. It was found that, irrespective of the estimation methodology and the definition of capacity, the estimated capacity can vary considerably over time. The second finding was that, as expected, the different approaches yielded different capacity results. These estimated capacities varied by as much as 26 % at the San Diego test site and by 34 % at the Shanghai test site. It was also found that each of the methodologies has advantages and disadvantages, and the best method will be the function of the available data, the application, and the goals of the modeler. Consequently, it is critical for users of automatic capacity estimation techniques, which utilize ITS data, to understand the underlying assumptions of each of the different approaches.

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“…Bottlenecks can occur at fixed locations [14], in variable locations [15], or can be produced temporarily (e.g., by incidents, in which capacity is reduced by the blocking of lanes) [8,11,16]. So, they can be classified as static or dynamic.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Methodology for Identification of Urban Bottlenecks Using GPS Data

Jimenez¹,

Rodríguez-Valencia²

2016

JTTE

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Bottlenecks have been widely studied for uninterrupted flow. The study of bottlenecks in urban traffic networks implies a challenge, since delays and queuing are implicit in interrupted flows. The objective of this paper is to discuss bottlenecks in urban traffic networks and present a methodology to identify them, based on GPS (Global Position System) data from taxis in Bogotá, Colombia. In networks, where stops are frequent, the principle we adopted deals with finding recurrent low-speed sections, beyond expected delays. Urban bottlenecks occur in those road network segments that perform poorly in terms of speed, compared with upstream and downstream conditions, producing recurrent and larger than normal delays. The GPS devices reported, via GPRS (General Packet Radio Service), information every 10 s. Results of this exploratory project are promising. The method allowed the identification of six urban bottlenecks out of seven randomly selected low-speed sections. The most valuable application of this methodology is the prioritization of resources investment in traffic infrastructure improvements. As a low cost option, this exploratory method might be especially attractive in identifying critical points in traffic networks in developing cities, without expensive traffic-monitoring systems.

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Some traffic features at freeway bottlenecks

Cited by 457 publications

References 7 publications

A Macroscopic Traffic Model for Road Networks With a Representation of the Capacity Drop Phenomenon at the Junctions

A Macroscopic Traffic Model for Road Networks With a Representation of the Capacity Drop Phenomenon at the Junctions

An analysis of four methodologies for estimating highway capacity from ITS data

Exploratory Methodology for Identification of Urban Bottlenecks Using GPS Data

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