Green vehicle routing in urban zones - A neuro-fuzzy approach

Local city authorities are making a serious effort to expand the number of low-greenhouse gas vehicles (green vehicles) at home. There is no reliable methodology , however , to support the implementation of this passenger transportation concept. In order to optimize the green capacity , a system has been developed to support decision making in urban green vehicle routing. The objective of this paper is to propose a green vehicle distribution model in a public transportation network. The problem has been defined as a problem of non-linear optimization with dispersed input parameters , requiring neuro-fuzzy logic. An adaptive neural network was developed , taking into account the costs to be borne by operators and users , and the environmental parameters along the observed vehicle route. Each input parameter of the neuro-fuzzy model has been placed in a complex context. They were divided into the elements describing in more detail the environmental status , the operator and passenger costs. The advantage of the model is that several factors shaping the input parameters have been taken into consideration. On the other hand , the complexity of urban systems management makes it a considerable challenge , and the surrounding circumstances are difficult to predict accurately. Accordingly , the inputs of the green vehicle model were fuzzified. The Index of Performance (IP) is the output , associated with each branch of the passenger transportation network. The model has been tested on a part of the public transport system in central Bel-grade. The results have proven a practical application possible , and a calibration of input parameters allows for full implementation in public transport vehicle routing. Building a good place to live is no easy task today , as economy relies heavily on the use of fossil fuels. The use of fossil fuels has the innate risks threatening the human environment. It is noteworthy that compared to industrial states emissions from developing countries now dominate. As GDP rates grow , exhaust emissions appear to go up , too. Besides , developing countries are yet to define strategies for exhaust emission reductions , but government subsidies for energy production technologies relying on renewable sources of energy have dwindled as well (European Environment Agency (EEA) , 2013). The EEA Report (2013) says that NO x , SO 2 and CO emissions resulting from fuel combustion increased significantly over the growing use of diesel internal combustion engines. As …