Variable speed limit: an overview

Khondaker, Bidoura; Kattan, Lina

doi:10.1179/1942787514y.0000000053

Cited by 109 publications

(55 citation statements)

References 25 publications

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“…VSL control is another effective congestion management method. All VSL control systems aim to balance traffic speed and homogenize the traffic flow according to current traffic (congestion, incidents) and weather conditions by utilizing the variable speed message [14]. It has also been used for congestion management close to work zones [48,49].…”

Section: Variable Speed Limit Controlmentioning

confidence: 99%

“…The required volume and occupancy values can be measured using traffic simulation software through programming the VSL algorithm, or alternatively, can be predicted based on historical data and mathematical models known as Model Predictive Control (MPC). Via MPC, the future condition is predicted based on historical data and the use of mathematical formulas [14]. In this study, VISSIM and VisVAP was used in order to model traffic conditions and to measure the volume and occupancy rate as well as to design a VSL control algorithm.…”

Section: Variable Speed Limit Controlmentioning

confidence: 99%

“…Recently, several congestion control strategies have been proposed and applied such as ramp metering and variable speed limit in order to prevent or shift the beginning of a bottleneck related capacity drop [13][14][15]. These traffic control measures will be discussed in detail in the following sections.…”

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confidence: 99%

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An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume

Dadashzadeh

Ergun

2019

Sustainability

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Under many circumstances, when providing full bus priority methods, urban transport officials have to operate buses in mixed traffic based on their road network limitations. In the case of Istanbul's Metrobus lane, for instance, when the route comes to the pre-designed Bosphorus Bridge, it has no choice but to merge with highway mixed traffic until it gets to the other side. Much has been written on the relative success of implementing Ramp Metering (RM), for example ALINEA (‘Asservissement line´ aire d’entre´ e autoroutie’) and Variable Speed Limits (VSL), two of the most widely-used “merging congestion” management strategies, in both a separate and combined manner. However, there has been no detailed study regarding the combination of these systems in the face of high bus volume. This being the case, the ultimate goal of this study is to bridge this gap by developing and proposing a combination of VSL and RM strategies in the presence of high bus volume (VSL+ALINEA/B). The proposed model has been coded using microscopic simulation software—VISSIM—and its vehicle actuated programming (VAP) feature; referred to as VisVAP. For current traffic conditions, the proposed model is able to improve total travel time by 9.0%, lower the number of average delays of mixed traffic and buses by 29.1% and 81.5% respectively, increase average speed by 12.7%, boost bottleneck throughout by 2.8%, and lower fuel consumption, Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Volatile Organic Compounds (VOC) emissions by 17.3% compared to the existing “VSL+ALINEA” model. The results of the scenario analysis confirmed that the proposed model is not only able to decrease delay times on the Metrobus system but is also able to improve the adverse effects of high bus volume when subject to adjacent mixed traffic flow along highway sections.

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Section: Variable Speed Limit Controlmentioning

confidence: 99%

Section: Variable Speed Limit Controlmentioning

confidence: 99%

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An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume

Dadashzadeh

Ergun

2019

Sustainability

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“…Such shockwaves result from microscopic driver behavior, such as sudden deceleration, merging or lane changing, leading to uneven headways. The use of a macroscopic traffic model cannot completely reflect the occurrence of such disturbances (Khondaker and Kattan, 2015).…”

Section: Introductionmentioning

confidence: 99%

Variable speed limit: A microscopic analysis in a connected vehicle environment

Khondaker

Kattan

2015

Transportation Research Part C: Emerging Technologies

238

108

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a b s t r a c tThis paper presents a Variable Speed Limit (VSL) control algorithm for simultaneously maximizing the mobility, safety and environmental benefit in a Connected Vehicle environment. Development of Connected Vehicle (CV)/Autonomous Vehicle (AV) technology has the potential to provide essential data at the microscopic level to provide a better understanding of real-time driver behavior. This paper investigated a VSL control algorithm using a microscopic approach by focusing on individual driver's behavior (e.g., acceleration and deceleration) through the use of Model Predictive Control (MPC) approach. A multi-objective optimization function was formulated with the aim of finding a balanced trade-off among mobility, safety and sustainability. A microscopic traffic flow prediction model was used to calculate Total Travel Time (TTT); a surrogate safety measure Time To Collision (TTC) was used to measure instantaneous safety; and, a microscopic fuel consumption model (VT-Micro) was used to measure the environmental impact. Real-time driver's compliance to the posted speed limit was used to adjust the optimal speed limit values. A sensitivity analysis was conducted to compare the performance of the developed approach for different weights in the objective function and for two different percentages of CV. The results showed that with 100% penetration rate, the developed VSL approach outperformed the uncontrolled scenario consistently, resulting in up to 20% of total travel time reductions, 6-11% of safety improvements and 5-16% reduction in fuel consumptions. Our findings revealed that the scenario which optimized for safety alone, resulted in more optimum improvements as compared to the multi-criteria optimization. Thus, one can argue that in case of 100% penetration rates of CVs, optimizing for safety alone is enough to achieve simultaneous and optimum improvements in all measures. However, mixed results were obtained in case of lower % penetration rate which showed higher collision risk when optimizing for only mobility or fuel consumption. This indicates that with such % penetration rate, multi-criteria optimization is crucial to realize optimum and balanced benefits for the examined measures.

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“…Very recent research focused on cooperative variable speed limit system [24,25] and its combination with autonomous vehicle control and individualized speed limits [26]. A more comprehensive review on variable speed limits strategies can be found in the survey articles by Khondanker and Kattan [27] and Lu and Shladover [28].…”

Section: Introductionmentioning

confidence: 99%

Numerical optimal control method for shockwaves reduction at stationary bottlenecks

Strnad

Fijavž

Žura

2016

J of Advced Transportation

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The paper introduces an optimal control method for traffic management with variable speed limits. It consists of traffic flow dynamics prediction with a non-linearized Lighthill-Whitham-Richards macroscopic traffic flow model, introduction of a cost functional, which enables stable shockwaves optimization, and numerical implementation of the optimization process with differential evolution. The method overcomes the discretization issues and provides speed limits that are in general not limited to small number of successive discrete points, i.e. variable message signs locations, nor in rounded speed limits. Performance of the method is demonstrated on a case study, which shows promising reduction of the backward moving shockwave that occurs because of a stationary bottleneck. On differential evolution algorithmsTo handle such an expanded optimal control problem a computationally effective method for numerical optimization process is required. We employ differential evolution (DE) approach introduced by Storn and Price [30,31]. DE is an evolutionary algorithm technique that is best suited for numerical optimization problems and has been found useful in many real-world optimization problems in engineering (e.g.[32] and [33]).There are three control parameters in DE, namely the mutation factor or scale factor, F ∈ [0, 2], the crossover probability, CR ∈ [0, 1], and the population size, NP ≥ 4. The selection of these parameters 842 I. STRNAD ET AL.

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Variable speed limit: an overview

Cited by 109 publications

References 25 publications

An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume

An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume

Variable speed limit: A microscopic analysis in a connected vehicle environment

Numerical optimal control method for shockwaves reduction at stationary bottlenecks

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