Off-Ramp Blockage on Freeways

Wang, Yibing; Pang, Yuqi; Chen, Xinyao; Kan, Yuheng

doi:10.1016/j.ifacol.2016.07.027

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…In the current industrial scenario, the ferrous production industries need to run the processes under very tight optimization, in order to stay competitive and continue strategies of high productivity and less emission along with the other requirements. As demonstrated in a number of recent studies [27][28][29][30][31][32][33] the use of data driven evolutionary approaches, like what has been adopted here, are clearly emerging as one of the very effective strategies to reach that goal.…”

Section: Resultsmentioning

confidence: 99%

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Mahanta

Chakraborti

2018

steel research int.

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Data driven models are constructed for the tuyere cooling heat loss, total blast furnace gas flow, tuyere velocity, productivity, and coke rate for an operational blast furnace of an integrated steel plant by using evolutionary computation methods like bi objective genetic programming (BioGP) and evolutionary neural network (EvoNN), which serve as the objectives for their optimization. The models are used to compute the Pareto tradeoff between these conflicting objectives with the help of predator prey genetic algorithm well tasted for computing the Pareto optimality earlier. The results of optimization and the performances of these models are thoroughly analyzed and accessed and a discussion regarding these data driven models and their influence in the ironmaking process are presented. The results are also compared against a similar calculation performed with the commercial software KIMEME.

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Section: Resultsmentioning

confidence: 99%

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Mahanta

Chakraborti

2018

steel research int.

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“…the condition that the shockwave reaching X m keeps moving upstream is: 19) and the condition that the shockwave reaching X 1 keeps moving further upstream is:…”

Section: Of 23mentioning

confidence: 99%

“…The utilized mainstream demand is presented in Figure 12, and a number of on-ramp demand scenarios are presented in Figure 13, assuming equal demands for all on-ramps for each given scenario. Macroscopic simulation tool METANET is employed for the simulation studies [19]. The ramp metering controller ALINEA [28] is applied at on-ramp O 7 in the ramp metering case, whereby the density in the ramp-merging area is stabilized at the critical density and the flow there is kept at the capacity flow level.…”

Section: A Ramp Merging Casementioning

confidence: 99%

“…Papageorgiou and Papamichail first mentioned off-ramp blockage when dealing with ramp metering [14], while Iordanidou et al considered the same issue for the purpose of variable speed limit control [18]. So far, only a small number of works [14,[18][19][20][21][22] have mentioned off-ramp blockage, and in particular, [20][21][22] just mentioned it as a concept without giving any detail. Overall, off-ramp blockage is readily understood and the resulting impact has been empirically recognized for a long time, but there is a lack of analytical studies, and its detailed mechanism is yet to be brought to light.…”

Section: Introductionmentioning

confidence: 99%

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Bottlenecks, Shockwave, and Off-Ramp Blockage on Freeways

et al. 2019

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Freeway congestion may spill back for several kilometers, blocking a number of on/off-ramps upstream. As a consequence, flows at the off-ramps may be substantially reduced, and vehicles bound for the off-ramps are trapped in the mainstream congestion, causing intensified spillback of congestion that blocks even more off-ramps further upstream. Such off-ramp blockage is readily understood and its impact is empirically recognized, but there is a lack of analysis to provide more insights. In this paper, some flow conditions for the activation of bottlenecks and congestion propagation are first established, and the mechanism of the off-ramp blockage is theoretically explored. Macroscopic and microscopic simulations are conducted to demonstrate the analytical results, and some general relations between the total demand, total inflow, total off-ramp outflow, and the number of vehicles within a freeway system are examined.

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Off-Ramp Blockage on Freeways

Cited by 2 publications

References 12 publications

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Evolutionary Data Driven Modeling and Multi Objective Optimization of Noisy Data Set in Blast Furnace Iron Making Process

Bottlenecks, Shockwave, and Off-Ramp Blockage on Freeways

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