Assigning on-ramp flows to maximize capacity of highway with two on-ramps and one off-ramp in between

Chen, Jing; Lan, Lin; Jiang, Rui

doi:10.1016/j.physa.2016.08.053

Cited by 13 publications

(3 citation statements)

References 33 publications

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“…The capacity drop phenomenon was also confirmed by Hall and Agyemang-Duah [5]. Since then, many empirical studies have been conducted to quantify the capacity drop ranging between 0.5% and 35% [6][7][8][9][10][11][12][13][14][15]. In consideration of overall traffic delays, the rate of queue discharge is of significant importance.…”

Section: Introductionmentioning

confidence: 87%

Empirical Proof of the Characteristics of the Queue Discharge Rate under Different Rainfall Conditions on an Active On-Ramp Bottleneck

Mejia,

Karoonsoontawong,

Kanitpong

2023

Applied Sciences

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Empirical studies show that queue discharge rate is lower than pre-queue capacity in congestion. This is the the capacity drop phenomenon. All previous research about this event used data during clear weather conditions. This is the first time that empirical relationships between queue discharge rate and weather conditions have been studied. Previous studies show that the capacity drop is triggered by a critical density. Once this density is reached, a drop in the discharge rate is expected. We show that this critical density decreases during any weather condition. Previous studies also prove that the capacity drop is related to speed in congestion but that this might not be true during inclement weather. We show that queue discharge rate is correlated to the speed of congestion in any weather condition. We have also shown for the first time that the speed in congestion and the percentage of the capacity drop have a negative linear relationship.

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Section: Introductionmentioning

confidence: 87%

Empirical Proof of the Characteristics of the Queue Discharge Rate under Different Rainfall Conditions on an Active On-Ramp Bottleneck

Mejia,

Karoonsoontawong,

Kanitpong

2023

Applied Sciences

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“…According to the real-time traffic flow information, the control rate is solved by taking the shortest travel time and waiting time as the control objectives and the mainline capacity and ramp queue length as the constraints. Traffic flow models such as the Payne model ( Chang & Li, 2002 ), the cell transmission model ( Chen, Lin & Jiang, 2017 ; Meng & Khoo, 2010 ; Schmitt, Ramesh & Lygeros, 2017 ) and METANET ( Dabiri & Kulcsar, 2017 ; Frejo & Camacho, 2012 ; Kontorinaki, Karafyllis & Papageorgiou, 2019 ) are widely used. Meshkat applied a quantitative hierarchical model to ramp coordination signal optimization for the first time ( Meshkat et al, 2015 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Coordinated ramp signal optimization framework based on time series flux-correlation analysis

Zhi¹,

Shu²,

Shen³

et al. 2021

PeerJ Computer Science

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Urban expressways provide an effective solution to traffic congestion, and ramp signal optimization can ensure the efficiency of expressway traffic. The existing methods are mainly based on the static spatial distance between mainline and ramp to achieve multi-ramp coordinated signal optimization, which lacks the consideration of the dynamic traffic flow and lead to the long time-lag, thus affecting the efficiency. This article develops a coordinated ramp signal optimization framework based on mainline traffic states. The main contribution was traffic flow-series flux-correlation analysis based on cross-correlation, and development of a novel multifactorial matric that combines flow-correlation to assign the excess demand for mainline traffic. Besides, we used the GRU neural network for traffic flow prediction to ensure real-time optimization. To obtain a more accurate correlation between ramps and congested sections, we used gray correlation analysis to determine the percentage of each factor. We used the Simulation of Urban Mobility simulation platform to evaluate the performance of the proposed method under different traffic demand conditions, and the experimental results show that the proposed method can reduce the density of mainline bottlenecks and improve the efficiency of mainline traffic.

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“…In order to solve this problem, a large number of traffic models have been proposed to research the complex traffic phenomena. Such as the substantial traffic models [1][2][3][4][5][6][7][8][9][10] which mainly include car-following models [11][12][13][14][15][16][17][18][19], cellular automation models [20][21][22][23], gas kinetic models [24][25][26], and hydrodynamic lattice models [27][28][29] have been posed to study traffic flow. The optimal velocity model (for short OVM) was firstly proposed by Bando et al [30] in 1995, which has successfully revealed the dynamic evolution of traffic jam in a simple way.…”

Section: Introductionmentioning

confidence: 99%

Energy consumption investigation for a new car-following model considering driver’s memory and average speed of the vehicles

Jin

Yang

2018

Physica A: Statistical Mechanics and its Applications

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Assigning on-ramp flows to maximize capacity of highway with two on-ramps and one off-ramp in between

Cited by 13 publications

References 33 publications

Empirical Proof of the Characteristics of the Queue Discharge Rate under Different Rainfall Conditions on an Active On-Ramp Bottleneck

Empirical Proof of the Characteristics of the Queue Discharge Rate under Different Rainfall Conditions on an Active On-Ramp Bottleneck

Coordinated ramp signal optimization framework based on time series flux-correlation analysis

Energy consumption investigation for a new car-following model considering driver’s memory and average speed of the vehicles

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