Online Traffic Signal Control for Reducing Vehicle Carbon Dioxide Emissions

Oda, Toshikane; Niikura, Satoshi

doi:10.1541/ieejias.126.1522

Cited by 6 publications

(6 citation statements)

References 2 publications

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“…Taking the realities of urban roads into account, we use the method in [27] to estimate carbon dioxide emissions of vehicles. The Carbon Dioxide Emissions Model can be described as the following two equations:…”

Section: Carbon Dioxide Emissions Modelmentioning

confidence: 99%

Routing Algorithm and Traffic Light Control based on Vehicular Delay-Tolerant Networks

Huang¹,

Sha²,

Zhang³

et al. 2016

jcm

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There are problems of low delivery radio and high transmission delay in Vehicular Delay-Tolerant Networks (a.b. VDTN. To address these problems, this paper proposes a Vehicular Delay-tolerant Network routing algorithm based on Contention (a.b. VDNC). VDTN algorithm consists of three strategies: the intersection selection strategy based on the Manhattan distance and traffic information, the first competitive strategy based on the direction of movement and the second competitive strategy based on the position information. Moreover, traffic light also plays an important role to ease traffic congestion and reduce transmission delay. So an Adaptive Traffic-light Control algorithm based on Green-Computing (a.b. ATCG) is proposed. ATCG algorithm involves the following two parts: the calculation of the optimal sequence and that of the recommended speed. Simulation results show that compared to other traditional DTN (Delay-Tolerant Networks) protocols, VDNC algorithm has a higher delivery rate and lower transmission delay. And meanwhile, ATCG algorithm enables vehicles pass through the intersection with fewer stoppages and shorter waiting time. At the same time, the carbon dioxide emission is also able to achieve a minimum for the purpose of environmental protection. Consequently, the collaboration between VDNC and ATCG can further improve the traffic efficiency in VDTN. Index Terms-Delay tolerant network, intelligent traffic light control, green computing, routing algorithm, vehicular networks, wireless sensor network I. INTRODUCTION Due to the disadvantages of current vehicular traffic such as the curing time of traffic light, the behindhand traffic control method, vehicle signal disconnection between vehicles and vehicles as well as vehicles and base-stations and, serious urban vehicle congestion, researchers pay more attention to Intelligent

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Section: Carbon Dioxide Emissions Modelmentioning

confidence: 99%

Routing Algorithm and Traffic Light Control based on Vehicular Delay-Tolerant Networks

Huang¹,

Sha²,

Zhang³

et al. 2016

jcm

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“…The following year, Oda et al (2004) used a macroscopic model to optimize traffic control settings to reduce CO2. The authors proved high correlation existed between the number of stops and the CO2 and minimized the number of stops to simplify the calculation burden.…”

Section: Environmental Metrics As Part Of Traffic Signal Optimizationsmentioning

confidence: 99%

“…Logically, since complex stochastic simulation models require evolutionary algorithms to derive (near) optimal solutions (Foy et al, 1992;Park et al, 1999), the field of transportation engineering has seen a significant number of studies where Genetic Algorithms (GAs), and other evolutionary processes, are embedded in the optimization framework to reduce negative mobility and externality performance metrics (Oda et al, 2004;Tan et al, 2012). The next step were multi-objective optimization studies, where firstly competing multiple objectives were integrated in the single objective function, followed by an outburst of studies where Pareto Fronts (2-dimensional objective functions) were developed to address optimization of mobility and externality performance metrics (Li et al, 2004;Kwak et al, 2012;Ma, 2012).…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria optimization of traffic signals: Mobility, safety, and environment

Stevanović

Stevanovic

et al. 2015

Transportation Research Part C: Emerging Technologies

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a b s t r a c tTwo-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.

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“…However, because of the huge computational burden needed to estimate CO 2 for all vehicles in the network, the authors simplified the experiments. Instead of minimizing CO 2 they minimized the number of stops, which they had shown was highly correlated with CO 2 (20). Another integrated VISSIM-CMEM model was used to show that a scenario with optimal traffic control reduced various pollutant emissions (CO, HC, NO x ) from 3% to 15%.…”

mentioning

confidence: 99%

“…An integrated VISSIM-CMEM model was also used to show that the signal timings, optimized for progression in TRANSYT 9, significantly reduced pollutant emissions and fuel consumption on an arterial road (6). Oda et al developed a simulator to estimate CO 2 emissions (20). They used a macroscopic traffic flow model to input traffic activities into the CO 2 simulator.…”

mentioning

confidence: 99%

Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions

Stevanović

Stevanovic

Zhang

et al. 2009

Transportation Research Record: Journal of the Transportation R

170

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tions and frequent stops at intersections. However, low traffic and continuous progression along streets do not guarantee the lowest fuel consumption and emissions. Excessive speeding, which may occur on roads with low traffic, may cause increased emissions for several pollutants. The best flow of traffic on arterial streets, in terms of fuel consumption and emissions, is the one with the fewest stops, shortest delays, and moderate speeds maintained throughout the commute (1).One of the ways to reduce excessive stop-and-go driving on urban streets is to optimize signal timings. Historically, signal timing optimization tools were developed to reduce delays and stops experienced by urban drivers. The concept of optimizing signal timings to reduce fuel consumption and emissions was first addressed by Robertson et al. (2). However, at that time traffic was simulated by macroscopic and analytical tools, and individual driving behavior was not considered. Similarly, the relationship between traffic activity, fuel consumption, and vehicular emissions, which was applied to all vehicles, was a simplistic and linear relationship (2).In recent years powerful tools for traffic modeling, fuel consumption, and emissions modeling have been developed. Microscopic simulation tools, such as VISSIM, have been used for more than a decade to model individual traffic behavior (3). Similarly, emissions models, such as the comprehensive modal emission model (CMEM), were developed to estimate second-by-second emissions of individual vehicles based on modes of a common driving cycle (4). These two types of microscopic models were coupled to estimate instantaneous emissions based on second-by-second activities of individually behaved vehicles (5-7).However, signal timing optimization models have been developed that now use microscopic traffic models to evaluate and improve the quality of signal timings (8,9). Researchers have reported that these new signal optimization tools generate signal timings that reduce delays and stops when compared with the ones generated by macroscopic optimization tools (10). However, no research has been performed that integrates all these new microscopic tools in order to find the best signal timings that would minimize fuel consumption and emissions. The research reported here aims to fill that gap in existing practice by integrating a microscopic traffic simulator, a comprehensive microscopic emission estimation model, and a stochastic signal optimization tool to provide signal timings that minimize fuel consumption and vehicular emissions. BACKGROUNDIn previous decades, many researchers have evaluated the effects of traffic signal timings on the environment (11-18). Effects are evaluated through an investigation of the amount of fuel consumption

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Online Traffic Signal Control for Reducing Vehicle Carbon Dioxide Emissions

Cited by 6 publications

References 2 publications

Routing Algorithm and Traffic Light Control based on Vehicular Delay-Tolerant Networks

Routing Algorithm and Traffic Light Control based on Vehicular Delay-Tolerant Networks

Multi-criteria optimization of traffic signals: Mobility, safety, and environment

Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions

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