Improving the effectivity of urban freight transport by means of cellular automata simulations

Unloading bays are one of the most popular and simple to implement solutions to support development of sustainable urban freight transport systems. The solution is aimed at reducing congestion on busy city streets, which is often caused by freight vehicles that, in an attempt to load or unload their cargoes, park directly in traffic lanes. Where dedicated unloading bays are in place, the traffic is undisturbed, which helps to prevent extra consumption of energy and fuel, and thus extra pollution, and which also makes it possible to avoid wasting time in traffic jams and ensuing costs. Therefore, the major benefit derived from this solution is its contribution to reduction of traffic congestion, followed by a perceptible decrease in pollutant emissions. This article aims at developing an algorithm for recommended distribution of unloading bays based on the reported locations of delivery points and parameters related to varied quantities of goods and the number of available parking spaces in the vicinity of unloading bays. Additionally, it addresses the issue of using unloading bays as EV charging stations. The proposed solution was implemented on the basis of a dedicated road traffic simulator based on the original cellular automaton model.