Impact of optimisation on idle time's fuel consumption and CO<SUB align="right">2 emissions in urban transportation

Abstract

There is ample innovation ensuing in the domain of automated transport and vehicle information and communication systems. This is especially the case in urban transport where idle driving is a significant factor. This work examines the benefits, of reducing idle driving, to be achieved if the ability of routing vehicles (or advising drivers of optimal routes) is acquired via the use of real-time information. This work approximates this real-time environment by an optimal network. The goal of this work is to assess the impact of optimisation on a given supply chain transportation network by contrasting optimal solutions to non-optimal solutions. The optimal solutions would be the result of mathematically programming a given supply chain network and then comparing it to the null case. The overall model is probabilistic one, where statistical distributions are used to generate numerous instances. The outcome of the work would be the presentation of numerical values that emphasise how much an optimal system would render in terms fuel consumption and CO2 emission avoidances.