A highway capacity function in Korea: Measurement and calibration

Suh, Sunduck; Park, Chang-Ho; Kim, Tschangho John

doi:10.1016/0191-2607(90)90055-b

Cited by 26 publications

(17 citation statements)

References 11 publications

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“…Link capacity is another important input for the traffic assignment models. As stated earlier, several studies have shown that the BPR function can lead to unfeasible overflow on links (7)(8)(9)(10). When link flows exceed the link capacity, this leads to spillback to the upstream links which can be challenging to model.…”

Section: Link Cost Function and Link Capacitymentioning

confidence: 99%

“…The capacity is not clearly defined in the BPR function, which can lead to unfeasible overflow on links. As a result, the equilibrium solution can have oversaturated links (7)(8)(9)(10). Branston reviewed different link functions and observed that the flow on the link does not exceed the steady-state capacity, which is uniquely defined (11).…”

Section: Link Cost Function and Link Capacitymentioning

confidence: 99%

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Link Cost Function and Link Capacity for Mixed Traffic Networks

Das

Chilukuri

2020

Transportation Research Record

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Link cost function and link capacity are critical factors in traffic assignment modeling. Popular link cost functions like the Bureau of Public Roads (BPR) function have well-known drawbacks and are not suitable for mixed traffic conditions where a variety of vehicle classes use the road in a non-lane-based movement. Similarly, capacity is generally considered as a constant value. However, in mixed traffic conditions, capacity is not constant, but a function of vehicle class composition. Toward addressing these issues, this paper proposes a link cost function in relation to link travel time and link capacity in relation to vehicular traffic flow for mixed traffic conditions. The functions are developed based on the kinematic wave model, which is popularly used for estimating traffic dynamics on the roads. The developed link cost function and link capacity use field measurable parameters that incorporate mixed traffic features. The functions are validated against empirical data obtained from 12 signal cycles from two different signalized intersections in Chennai, India, representing different scenarios of mixed traffic, and it was found that the results match well with the empirical data.

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Section: Link Cost Function and Link Capacitymentioning

confidence: 99%

Section: Link Cost Function and Link Capacitymentioning

confidence: 99%

Link Cost Function and Link Capacity for Mixed Traffic Networks

Das

Chilukuri

2020

Transportation Research Record

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“…Volume-delay functions are typically employed in utility estimation (e.g., time cost) for static or semidynamic traffic assignments, and informing route choices for agent-based modeling (Suh et al, 1990;Çolak et al, 2016). The differentiable form and convex nature of volume-delay functions make them an ideal candidate for optimization-based traffic assignment, such as the assignment that satisfies Wardrop's equilibrium (Lien et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The most widely adopted functional representation of such a macroscopic volume-delay relationship is the Bureau of Public Roads (BPR) relationship proposed in the 1960s (Bureau of Public Roads, 1964). Since then, local traffic agencies worldwide have calibrated the BPR curve coefficients to suit the local needs, such as in Suh et al (1990), Kurth et al (1996), Irawan et al (2010), Mtoi and Moses (2014), and Kucharski and Drabicki (2017). However, the calibration of BPR coefficients to local roads requires an extensive set of volume and delay data, and gathering such data (e.g., through surveys) is often a challenge.…”

Section: Introductionmentioning

confidence: 99%

Context-specific volume–delay curves by combining crowd-sourced traffic data with automated traffic counters: A case study for London

Casey

Zhao

Kumar

et al. 2020

DCE

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Traffic congestion across the world has reached chronic levels. Despite many technological disruptions, one of the most fundamental and widely used functions within traffic modeling, the volume–delay function has seen little in the way of change since it was developed in the 1960s. Traditionally macroscopic methods have been employed to relate traffic volume to vehicular journey time. The general nature of these functions enables their ease of use and gives widespread applicability. However, they lack the ability to consider individual road characteristics (i.e., geometry, presence of traffic furniture, road quality, and surrounding environment). This research investigates the feasibility to reconstruct the model using two different data sources, namely the traffic speed from Google Maps’ Directions Application Programming Interface (API) and traffic volume data from automated traffic counters (ATC). Google’s traffic speed data are crowd-sourced from the smartphone Global Positioning System (GPS) of road users, able to reflect real-time, context-specific traffic condition of a road. On the other hand, the ATCs enable the harvesting of the vehicle volume data over equally fine temporal resolutions (hourly or less). By combining them for different road types in London, new context-specific volume–delay functions can be generated. This method shows promise in selected locations with the generation of robust functions. In other locations, it highlights the need to better understand other influencing factors, such as the presence of on-road parking or weather events.

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“…Developing link-capacity functions is quite important in transportation planning studies. In fact, [16] points out that it is impossible to model driver's route choice behaviour without a suitable link-capacity function for a transportation link or link groups. Moreover, the traffic assignment modelling that takes into account traffic congestion effect needs the mathematical relations embracing some traffic variables such as travel time, and capacity [13].…”

Section: An Overview Of Link-capacity Functionsmentioning

confidence: 99%

Soft Computing and Regression Modelling Approaches for Link-Capacity Functions

Koşun¹,

Tayfur²,

Çelik³

2016

NNW

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Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

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A highway capacity function in Korea: Measurement and calibration

Cited by 26 publications

References 11 publications

Link Cost Function and Link Capacity for Mixed Traffic Networks

Link Cost Function and Link Capacity for Mixed Traffic Networks

Context-specific volume–delay curves by combining crowd-sourced traffic data with automated traffic counters: A case study for London

Soft Computing and Regression Modelling Approaches for Link-Capacity Functions

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