AMoDeus, a Simulation-Based Testbed for Autonomous Mobility-on-Demand Systems

Ruch, Claudio; Hörl, Sebastian; Frazzoli, Emilio

doi:10.1109/itsc.2018.8569961

Cited by 49 publications

(45 citation statements)

References 27 publications

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“…MATSim focuses on the simulation of individual mobility over the course of one day, in a great level of details. Its pluggable architecture and completely disaggregate formulation are the drivers of its increasing adoption not only by researchers in the transportation planning community but also in artificial intelligence [31], as well as practitioners, such as the Swiss Federal Railways [32]. It relies on the equilibrium assumption, standard in transportation models [33].…”

Section: Agent-based Modelmentioning

confidence: 99%

Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

Hackl

Dubernet

2019

Future Internet

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Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic.

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Section: Agent-based Modelmentioning

confidence: 99%

Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

Hackl

Dubernet

2019

Future Internet

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“…For the optimization problem in (1), we use m = 10 nodes with prices for electricity sampled from a uniform distribution in [0. 8,3]. The problem was solved for 300 randomly created networks and the results were averaged.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In conjunction with society's interest in AMoD technologies, there is extensive literature emerging that studies the different aspects of AMoD systems. The potential impact of shared mobility services on daily urban mobility [2], the analysis of AMoD systems with realistic demand [3], autonomous vehicle behavior in existing traffic models [4], and rebalancing algorithms [5] have been investigated using simulation frameworks. The interplay between AMoD and public transport has been studied in [6] and [7].…”

Section: Introductionmentioning

confidence: 99%

Smart Charging Benefits in Autonomous Mobility on Demand Systems

Turan

Tucker

Alizadeh

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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In this paper, we study the potential benefits from smart charging for a fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD) services. We first consider a profit-maximizing platform operator who makes decisions for routing, charging, rebalancing, and pricing for rides based on a network flow model. Clearly, each of these decisions directly influence the fleet's smart charging potential; however, it is not possible to directly characterize the effects of various system parameters on smart charging under a classical network flow model. As such, we propose a modeling variation that allows us to decouple the charging and routing problems faced by the operator. This variation allows us to provide closedform mathematical expressions relating the charging costs to the maximum battery capacity of the vehicles as well as the fleet operational costs. We show that investing in larger battery capacities and operating more vehicles for rebalancing reduces the charging costs, while increasing the fleet operational costs. Hence, we study the trade-off the operator faces, analyze the minimum cost fleet charging strategy, and provide numerical results illustrating the smart charging benefits to the operator.

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“…In fact, previous studies [14] have shown that more computationally expensive algorithms may outperform the heuristic, especially with regards to specific objectives such as minimizing overall customer waiting time or minimizing fleet empty mileage. Many of such algorithms have been developed in the AMoDeus framework [29], which adds an open easy-to-use fleet management benchmarking layer on top of the code used here.…”

Section: Simulation Of Automated Vehiclesmentioning

confidence: 99%

Dynamic demand estimation for an AMoD system in Paris

Hörl

Balać

Axhausen

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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A simulation framework is presented that equilibrates a given automated taxi fleet with (a) consistent prices to provide the service and (b) customer behaviour that reacts to costs and level of service alike. In a first attempt, a hypothetical AMoD service within the highway ring of Paris is considered. The "dynamic demand" case yields a demand of around 1.2M trips per day for such a service at the optimal fleet size of 25k vehicles. This number is considerably lower than 2.3M trips that potentially could be served in a "static maximum demand" case which has often been used as a basis for previous fleet sizing studies. While the authors acknowledge a multitude of assumptions that constitute the present model, clear pathways to its improvement, methodologically and datawise, are provided.

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AMoDeus, a Simulation-Based Testbed for Autonomous Mobility-on-Demand Systems

Cited by 49 publications

References 27 publications

Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

Smart Charging Benefits in Autonomous Mobility on Demand Systems

Dynamic demand estimation for an AMoD system in Paris

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