Reduction of travel times and traffic emissions using model predictive control

Zegeye, S. K.; Schutter, Bart De; Hellendoorn, Hans; Breunesse, E. A.

doi:10.1109/acc.2009.5159942

Cited by 41 publications

(13 citation statements)

References 15 publications

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“…In addition, greenhouse gas emissions are higher during stop-and-go and congested traffic conditions than in free flow conditions. Zegeye et al (2010) used MPC approach to assess the impact of dynamic speed limit control in reducing CO 2 emissions, fuel consumption and travel time. Their study concluded that a reduction of Total Time Spent (TTS) alone could not meet the requirement of reducing emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Khondaker

Kattan

2015

Transportation Research Part C: Emerging Technologies

238

103

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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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Section: Literature Reviewmentioning

confidence: 99%

“…3. Improved throughput and environmental benefits: Since congestion is also associated with increased fuel consumption and emissions, the capability of VSL in improving traffic flow also results in environmental benefits (Zegeye et al, 2010).…”

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

103

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“…Bai et al (2007) applied dynamic assignment techniques to compute emission from on-road vehicles. Zegeye et al (2009) proposed model predictive control to reduce travel time and emission at the same time. Li et al (2005) considered emission concentration standards provided by NAAQS as a constraint and determined the capacity of links.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Environmental Objectives in a System Optimal Dynamic Traffic Assignment Model

Aziz¹,

Ukkusuri²

2012

Computer aided Civil Eng

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Maintaining air‐quality standards is a major concern for transportation planners and policy makers in the United States. This necessitates considering nontraditional emission objectives in transportation systems modeling. In this research, we integrate emission‐based objective into the traditional travel time based dynamic assignment framework. Carbon monoxide (CO) emissions from vehicles are computed as functions of space mean speed (determined from an embedded mesoscopic traffic flow model). Different performance metrics (CO emission, system wide travel time, and speed profiles) from the integrated model are compared with traditional dynamic assignment model (with travel time minimization objective). In addition, results indicate changes in route choice behavior of the road users when emission objective is integrated to dynamic assignment framework.

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“…On the other hand, to minimize the CO 2 emission from the vehicles, the number of stops has large influence. However, since there is a trade-off between the total duration and the number of stops caused by traffic signals as reported in [3], the signal control policy that can minimize the duration of stops might not be efficient for reduction of the number of stops. A famous microscopic signal control method called RHODES [4] can be applied to minimize the CO 2 emission from the vehicles by replacing the objective function of the method with another function that evaluates the amount of exhaust gases.…”

Section: Introductionmentioning

confidence: 96%

A self-learning traffic signal control method for CO<inf>2</inf> reduction using prediction of vehicle arrivals

Umedu

Togashi

Higashino

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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A large part of CO2 emission comes from road transportation. The amount of CO2 emission from vehicles can be reduced by controlling traffic flows using traffic signals, because it is strongly affected by vehicular behavior. Although there are a number of traffic signal control systems to reduce travel time, such systems might not be effective to reduce vehicular emission because of the trade-off between stop duration and stop frequency at signals. In this paper, we propose a decentralized signal control technique for reduction of vehicle stops using vehicle arriving information collected by inter-vehicle communication. The proposing technique controls signals based on an evaluation function to predict CO2 emission amount. The function is gradually improved by an unsupervised learning technique. Through simulation based evaluation, we show that the proposing technique is efficient to reduce CO2 emissions from vehicles without increasing the average travel time not so much.

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Reduction of travel times and traffic emissions using model predictive control

Cited by 41 publications

References 15 publications

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

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

Integration of Environmental Objectives in a System Optimal Dynamic Traffic Assignment Model

A self-learning traffic signal control method for CO<inf>2</inf> reduction using prediction of vehicle arrivals

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