AMoDSim: An Efficient and Modular Simulation Framework for Autonomous Mobility on Demand

Abstract

Urban transportation of next decade is expected to be disrupted by Autonomous Mobility on Demand (AMoD): AMoD providers will collect ride requests from users and will dispatch a fleet of autonomous vehicles to satisfy requests in the most efficient way. Differently from current ride sharing systems, in which driver behavior has a clear impact on the system, AMoD systems will be exclusively determined by the dispatching logic. As a consequence, a recent interest in the Operations Research and Computer Science communities has focused on this control logic. The new propositions and methodologies are generally evaluated via simulation. Unfortunately, there is no simulation platform that has emerged as reference, with the consequence that each author uses her own custom-made simulator, applicable only in her specific study, with no aim of generalization and without public release. This slows down the progress in the area as researchers cannot build on each other's work and cannot share, reproduce and verify the results. The goal of this paper is to present AMoDSim, an open-source simulation platform aimed to fill this gap and accelerate research in future ride sharing systems.

On the Importance of demand Consolidation in Mobility on Demand

“…We present in this section the results of our simulation campaign that we ran on AMoDSim [7], an open-source simulation. The code to reproduce the results presented here is available in the AMoDSim's repository.…”

“…The system model considered in this paper is an extension of the one presented in our previous work [7]. The system is composed by a fleet of vehicles and a coordinator managing them.…”

“…In this case, τ v (q j−1 , q j ) = 0 and t l,i = 0, meaning that the pick-up and drop-off operations are performed one after the other, without any vehicle movements in between. For a more detailed explanation of the vehicle movement model and the computation of the time lost in accelerating and decelerating, refer to § 3.3 of [7]. Each vehicle has a capacity C. When constructing schedules, we must ensure that the number of passengers does not exceed C. Suppose a vehicle has at a certain point in time n 0 passengers on board and a certain schedule S v = [sp 1 , sp2, .…”

“…As in [7], the network consists in a Manhattan grid of 60 Km 2 , representative of the city of Manhattan with static travel times as in [20]. The distance between east-west-oriented roads and north-south oriented roads is 80m and 200m, respectively.…”

“…We show that by just leveraging this knob, with no change in the vehicle dispatch logic, the system effectively changes behavior to shift its configuration toward the left or the right poles of the transportation landscape. We simulate the system in AMoDSim [7] and made all the scripts to reproduce the scenarios available in the repository. We find that, under the assumptions considered here, MoD can effectively offer higher capacity if more consolidation is adopted in change of a reasonable loss in QoS.…”