Modelling perception updating of travel times
Following the work of Horowitz (), there has been a slowly growing interest in describing transportation systems through their day-to-day dynamics.This trend seems to have particularly grown in momentum in the last several years.In the relevant literature, a novel problem formulation called travelling salesman problem with drone has been introduced, and some modelling and solution approaches have been presented.Existing approaches are based on the main assumption that the truck can dispatch and pick up a drone only at a node, i.e. Here, the authors present a novel approach aimed to maximise the drone usage in parcel delivering.The paper reviews existing models, discusses these conceptual difficulties, and suggests resolutions (e.g. An exemplar dynamical is used to illustrate a consistent way in which discrete- and continuous-time analyses may be brought together, and future research perspectives are identified and discussed.Recently, several prominent logistic companies in Europe and the USA are seriously considering the idea of using drones launched from trucks and working in parallel to deliver packages.The authors consider that a truck can deliver and pick a drone up not only at a node but also along a route arc (en route).
Finally, a deterministic, discrete-event simulation model is proposed to combine the proposed updating approach with a network bottleneck model, with simulation experiments used to gain insights into the properties of the proposed perception updating model.It therefore seems a highly appropriate time for a special issue that is able to reflect the developments and diversity of approach under development.In the history of this field, there has been a strong connection between, in the first case, the use of numerical experiments to push the boundaries of the model assumptions and, in the second case, the subsequent use of theory to capture at least part of the phenomena observed in the numerical work.The motivation of Parry, Hazelton and Watling, in “A New Class of Doubly Stochastic Day-to-Day Dynamic Traffic Assignment Models”, is to develop a richer class of stochastic process traffic assignment model than currently exists, to capture variation in unmeasured factors.Such models are important when attempting to capture the real-life variation in traffic flows, given the non-stationary and complex spatio-temporal correlations that may arise, which are not represented in existing Markov models for transportation systems.