Comparing System Optimum-based and User Decision-based Traffic Models in an Autonomous Transport System

Abstract

The paper introduces a framework to perform the demand management and route planning tasks of a highly developed transport system managing scheme, assuming an autonomous transport system. Two types of autonomous transport system managing models have been introduced. In case of the first model, the assigned number of trips is assumed to be the modified variable related to the optimization problem. In case of the second model, the decision process is directly influenced by the travel prices defined by the optimization method. These approaches represent different demand management strategies. The first model aims to directly assign the incoming user demands to the system, while the second procedure lets the users make the decision. However, in the second case the system can strongly influence the users’ choices through the values of the travel prices. Accordingly, it seems to be a reasonable assumption that the firstly presented model has significantly higher efficiency in distributing the load on the network. On the other hand, the method of the second model would be much more tolerable and acceptable from a social point of view. Therefore, the aim of the paper is to introduce the developed models and to compare their efficiencies.

Black-Spot Analysis in Hungary Based on Kernel Density Estimation

“…A function is called a kernel, if K is a limited, continuous, symmetrical density function for which the following conditions are fulfilled [29,[31][32][33]:…”

“…Usually, K is a unimodal density function, which is symmetric to the origin, so this ensures that fn (x) also becomes a density function [16,29,[31][32][33][35][36][37].…”

Safety aspects of critical scenario identification for autonomous transport

“…Mathematical modeling of road traffic processes has been studied in several previous studies (Lo and Szeto, 2002;Szeto and Lo, 2004;Waller and Ziliaskopoulos, 2006;Yperman, 2007;Yperman et al, 2007;Tampère et al, 2011;Torok et al, 2014;Tettamanti et al, 2016;Pauer and Török, 2019). The automation of transport systems is greatly supported by the spread of infocommunication and vehicle technologies.…”

Modeliranje dispečerskog sustava za podršku u upravljanju željezničkim prometom u kolodvorskom području temeljenog na neizrazitoj logici (fuzzy logic)

“…Because of this it is possible to develop a system that will assist the dispatcher in making such decisions (Törnquist, 2005), or completely replace him. Additionally, this kind of system can be improved by communication links to train driver decision support systems located in trains via GSM-R with the purpose to improve user cost-based goals (Pauer, Török, 2019) such as better energy efficiency of train driving, less maintenance costs etc. In this paper a new model of fuzzy inference system for this purpose is presented.…”

“…In this paper a new model of fuzzy inference system for this purpose is presented. The development of this model is the result of the continuation of previous research related to the modelling of fuzzy logic based dispatcher support system for railway traffic control (Haramina et al 2018).…”