An Improved Algebraic Method for Transit Signal Priority Scheme and Its Impact on Traffic Emission

Ji, Yanjie; Hu, Bo; Han, Jian; Tang, Dounan

doi:10.1155/2014/412132

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…[35] If clustering is not done there redundant data and the communication is also hampered by not having a clear request response pattern. Clustering can be done by the relative speed of the vehicles, direction and the connectivity that vehicles have with other vehicles (Kakkasageri and Manvi 2014) [36].…”

Section: Review On Energy Efficient Data Disseminationmentioning

confidence: 99%

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

Dhasarathan¹,

Dumka

Loganathan

2020

Technol Econ Smart Grids Sustain Energy

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The aim of this paper is to focus on energy dissemination for green vehicle transportation. In current scenario population of urban areas keeps increasing as the people move towards the cities in search of jobs and betterlife style. This leads to an increase in the number of vehicles on the road. But the transport network which is accessible to the citizens is less when compared to their demand. This situation results in more vehicles on the road. The result of which is one of the factors that aggravate the traffic congestion. It is identified the vehicle volume on road is 91.7% which increases the CO2 emission to 93.25% every month. 2M2C-R2ED shows its efficiency in identification and segregation of alternative route by IWS with an average frequency of 3.82%. Traffic congestion occurs when the available transport resources are less when compared to the number of vehicles that share the resource. As the number of vehicles increases the resources become scarce and congestion is more. The demand for the resources is higher than the actual capacity of the roads and the streets. There are some circumstances that which will aggravate the traffic congestion. The circumstances can be the road condition (potholes, road repair), accidents and some natural calamities. There is a lot of research being done to predict the traffic and model it in order to find a solution that will make the condition better. But still it is an open issue. The accuracy of the predictions done is less.

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Section: Review On Energy Efficient Data Disseminationmentioning

confidence: 99%

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

Dhasarathan¹,

Dumka

Loganathan

2020

Technol Econ Smart Grids Sustain Energy

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“…Through robust optimization, the authors are able to reformulate signal optimization problems with emission constraints/objectives as a mixed integer linear program. Ji et al (2014) also have developed a method to optimize transit signal priority scheme by alleviating impact on exhaust emission and reducing traffic vehicle delay. However, it finds, in many cases, traffic and emission objectives are not aligned very well with each other, especially when traffic network is complicated and traffic dynamics are nonlinear.…”

Section: Related Workmentioning

confidence: 99%

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

Song

Han

et al. 2020

Netw Spat Econ

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We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO 2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

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“…It pay the way of benefiting in sinking the total time of travel for the general public in transit (Liu et al, 2016). Ji et al (2014) find the Pareto solution using a genetic algorithm and microscopic emission model that reduces the pedestrians and vehicles getting delay due to massive traffic. The multi-objective behavior of the algorithm monitors the real world traffic data and controls the traffic signal for the betterment of vehicle drivers and the public.…”

Section: Review On Energy Efficient Data Disseminationmentioning

confidence: 99%

Data dissemination for green-VANETs communication: an opportunistic optimization approach

Dhasarathan¹,

Dumka

Dhilipkumar

et al. 2020

IJPCC

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Purpose This paper aims to predict the traffic and helps to find a solution. Unpredictable traffic leads more vehicles on the road. The result of which is one of the factors that aggravate traffic congestion. Traffic congestion occurs when the available transport resources are less when compared to the number of vehicles that share the resource. As the number of vehicles increases the resources become scarce and congestion is more. Design/methodology/approach The population of the urban areas keeps increasing as the people move toward the cities in search of jobs and a better lifestyle. This leads to an increase in the number of vehicles on the road. However, the transport network, which is accessible to the citizens is less when compared to their demand. Findings The demand for resources is higher than the actual capacity of the roads and the streets. There are some circumstances, which will aggravate traffic congestion. The circumstances can be the road condition (pot holes and road repair), accidents and some natural calamities. Originality/value There is a lot of research being done to predict the traffic and model it to find a solution, which will make the condition better. However, still, it is an open issue. The accuracy of the predictions done is less.

show abstract

An Improved Algebraic Method for Transit Signal Priority Scheme and Its Impact on Traffic Emission

Cited by 5 publications

References 14 publications

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

Data dissemination for green-VANETs communication: an opportunistic optimization approach

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