Optimum Vehicle Flows in a Fully Automated Vehicle Network

Abstract

This paper provides a novel assignment method and a solution algorithm that allows to determine the optimum vehicle flows in a fully automated vehicle network. This assignment method incorporates the following specific features: (1) optimal redistribution of occupied and unoccupied vehicles; (2) inter-vehicle spacing is adapted to meet the minimum safe distance criteria on congested link, (no collision in the worst failure case); (3) trip-time minimization of all traffic participants by a centralized vehicle routing. The latter feature allows the realization of a so called system optimum solution, which minimizes the total time of all trips. This assignment method is applied to two, topologically different, test networks at different travel demand levels, in order to determine: the share of unoccupied vehicle, the minimum number of required vehicles, the share of congested links, the lost trip-time of occupied vehicles due to the presents of unoccupied vehicles. Furthermore, the advantage of a centralized vehicle routing is quantified by comparing the total trip-times of a scenario using a system optimum solution with a scenario applying the user equilibrium solution, without considering unoccupied vehicle flows. Regarding the investigated scenarios, the share of unoccupied vehicle flows with centralized vehicle routing in a uniform, random demand scenario is approximately 11% − 14%.

Assessment of Personal Rapid Transit System Configurations Regarding Efficiency and Service Quality

“…The number of vehicles deployed as part of a transportation system is a key component as it will influence various decisive factors, such as capital costs and the level of service [50]. There are various algorithmic solutions aiming to find the optimal fleetsize for a PRT system [4,[51][52][53]. However, we chose a different approach, as optimizing the fleetsize a priori requires us to specify most or all other system parameters beforehand, and, therefore, interactions of said variables cannot be investigated.…”

“…The trip times are short as there are no intermediate stops between the origin and destination of a trip and there is no inference with other traffic. Through the omission of staffing costs for drivers as well as significant reductions in capital expenditures for infrastructure compared to light rail transit (LRT), the launch and operation of PRT systems is financially attractive for municipalities and fares can be kept low [4]. Grade-separated guideways can either be achieved by elevating them or by repurposing existing road infrastructure by erecting separations.…”

“…Grade-separated guideways can either be achieved by elevating them or by repurposing existing road infrastructure by erecting separations. Both options yield noteworthy amounts of flexibility regarding installation requirements (curvatures typically less than 10 m [4]), as well as the possibility of an extension of the network at a later point in time [5].…”

“…The general idea of PRT dates back to 1964 [6] and the aforementioned variety of potential merits is well known. However, only a handful of systems are operational today and the concept's breakthrough has not yet happened as many projects are disregarded due to uncertainties concerning this new technology, flawed designs, skyrocketing development costs, political issues, or regulatory hurdles [4,7,8].…”

Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

“…However, the above mentioned emerging "intelligent" transport technologies are generally difficult to cast in conventional framework of macroscopic models. Nevertheless, there are valid attempts to integrate microscopic effects of new services in aggregate, macroscopic model using certain idealizing or extreme assumptions: for example, in [6] a multi-modal traffic assignment is modeled; in [7] the link flows of autonomous vehicles (AVs) are modeled by increasing the link capacities; in [8] the empty and occupied vehicle flows of SAVs are determined under system optimum flow constraints by solving a linear programming problem and in [9] the stability of the UE with AVs is examined by means of Lijapunov functions.…”