Evaluating the systemic effects of automated mobility-on-demand services via large-scale agent-based simulation of auto-dependent prototype cities

Oke, Jimi; Akkinepally, Arun; Chen, Siyu; Xie, Yifei; Aboutaleb, Youssef M.; Azevedo, Carlos Lima; Zegras, P. Christopher; Ferreira, Joseph; Ben‐Akiva, Moshe

doi:10.1016/j.tra.2020.06.013

Cited by 28 publications

(31 citation statements)

References 36 publications

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“…In [19], different prototype cities, populations, and mobility patterns are built to create different scenarios; in particular, the prototype population is constructed by assigning spatial features based on land use characteristics. However, the paper neither explores patterns between residence and workplace location, nor details how a model is applied to fill this gap (note that a gravity model accounts for some estimates of origins and destinations between zones and then relates them through a distance factor, considered as impedance, to generate an amount of trips.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Further details may be found in [20]). It is then left unclear how the solution actually reproduces commuting patterns; moreover, since the assignment of the synthetic population is not at the core of [19], their methodology is not detailed. Another work that applies a gravity model to assign workplaces is [21], but the focus outside of the transport domain led to a simplistic approach, in which only distance and capacity were considered.…”

Section: Literature Reviewmentioning

confidence: 99%

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Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Agriesti

Roncoli

Nahmias-Biran

2022

IJGI

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Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Agriesti

Roncoli

Nahmias-Biran

2022

IJGI

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“…The GDP per capita of Auto Innovative cities is 1.2 higher on average compared to that of Auto Sprawl cities. Given these key differences between the two typologies, prototype cities representing the population, land-use and mobility demand and supply outcomes in both typologies were synthesized 47 . Both prototype cities were built on actual road and transit networks, population microdata and land use categories from representative (or archetype) cities close to the centroid of their respective typologies.…”

Section: Methodsmentioning

confidence: 99%

“…The simulation of the prototype cities was performed in SimMobility, an open-source platform for microscopic demand and mesoscopic supply dynamic traffic assignment modeling 44 . The cities were calibrated for modeshares, activity patterns and network speeds 47 . The inputs to the simulator are: land use, demographic and economic factors, as well as road and transit networks.…”

Section: Methodsmentioning

confidence: 99%

“…1 . These trajectories are obtained from a microscopic travel demand and mesoscopic supply simulator, SimMobility 44 , 47 , 48 that has been calibrated for two prototype cities, which represent sparse and dense automobile-dependent cities, respectively. We demonstrate that PanCitySim can be used to not only predict the spatio-temporal dynamics of an epidemic, but also potentially evaluate the impacts of a variety of interventions, such as employment-based or age-based restrictions, reduction in mass transit services also provide detailed contact-tracing and individual-level analyses of disease impacts.…”

Section: Introductionmentioning

confidence: 99%

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Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas

Kumar¹,

Oke

Nahmias-Biran

2021

Sci Rep

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We build on recent work to develop a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages person-trajectories obtained from an activity-based model calibrated for two full-scale prototype cities, consisting of representative synthetic populations and mobility networks for two contrasting auto-dependent city typologies. We simulate the propagation of the COVID-19 epidemic in both cities to analyze spreading patterns in urban networks across various activity types. Investigating the impact of the transit network, we find that its removal dampens disease propagation significantly, suggesting that transit restriction is more critical for mitigating post-peak disease spreading in transit dense cities. In the latter stages of disease spread, we find that the greatest share of infections occur at work locations. A statistical analysis of the resulting activity-based contact networks indicates that transit contacts are scale-free, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We validate our simulation results against existing case and mortality data across multiple cities in their respective typologies. Our framework demonstrates the potential for tracking epidemic propagation in urban networks, analyzing socio-demographic impacts and assessing activity- and mobility-specific implications of both non-pharmaceutical and pharmaceutical intervention strategies.

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Environmental impacts of autonomous vehicles: A review of the scientific literature

Silva

Cordera

González-González

et al. 2022

Science of The Total Environment

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Evaluating the systemic effects of automated mobility-on-demand services via large-scale agent-based simulation of auto-dependent prototype cities

Cited by 28 publications

References 36 publications

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas

Environmental impacts of autonomous vehicles: A review of the scientific literature

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