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

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

Drinking water accessibility typologies in low- and middle-income countries

We present a data-driven typology framework for understanding patterns in drinking water accessibility across low- and middle-income countries. Further, we obtain novel typology-specific insights regarding the relationships between possible explanatory variables and typology outcomes. First, we conducted exploratory factor analysis to obtain a smaller set of interpretable factors from the initial set of 17 drinking water accessibility indicators from 73 countries. The resulting seven factors summarize the key drivers for water accessibility, and also serve as a vehicle for framing discussions on country outcomes. We clustered the countries based on their seven-dimensional water accessibility factor scores, referring to the resulting three clusters as ‘typologies,’ namely, Decentralized , Centralized and Hybrid . The typologies serve as a vehicle for analyzing water accessibility among countries with similar patterns, in contrast with geographically-based approaches. Finally, we fitted a decision tree classifier to analyze relationships between a country’s typology membership and socioeconomic, geographic and transportation explanatory variables. We found that private car ownership, population density and per-capita gross domestic product are most relevant in predicting a country’s drinking water accessibility typology

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

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.ISSN:2045-232

Modeling System-Wide Urban Rail Transit Energy Consumption: A Case Study of Boston

Rapid transit systems are critical components of urban public transportation networks in their impact, not only on personal mobility but also on the energy and environmental costs associated with network operations. To facilitate effective planning for current and future needs, a framework is required that provides important consumption metrics and also explains the various contributors to energy consumption, along with their interactions. To address this gap, we estimated models that utilized operational and ridership data for the Massachusetts Bay Transportation Authority’s rapid transit system, as well as ambient temperature, to accurately predict system-wide electricity consumption. The models were trained with data from 2019 and tested with data from 2020. The estimated multiple linear regression (MLR) and random forest (RF) models explained 93% and 95% of the variance in the data set, respectively. The MLR model provided predictions with a root mean squared error (RMSE) of 2.7 MWh and mean absolute percentage error (MAPE) of 4.68%, while the RF model resulted in an RMSE of 2.94 MWh and MAPE of 5.01%. We also investigated the impacts of COVID-19 on the transit system by exploring the effects on ridership, energy consumption, cost, and train movement metrics before and during the pandemic. We find that the models are robust and perform well, even with the significant disruptions associated with the COVID-19 pandemic

Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach

Abstract Autonomous vehicle (AV) technologies are under constant improvement with pilot programs now underway in several urban areas worldwide. Modeling and field-testing efforts are demonstrating that shared mobility coupled with AV technology for automated mobility on-demand (AMoD) service may significantly impact levels of service and environmental outcomes in future cities. Given these rapidly emerging developments, there is an urgent need for methods to adequately quantify the economic impacts of new vehicle technologies and future urban mobility policy. In this paper, we show how broader user-centric impacts can be captured by the activity-based accessibility (ABA) measure, which takes advantage of the rich data and outcomes of utility-maximization activity-based models and its interaction with mesoscale agent-based traffic simulation frameworks. Using the SimMobility simulator, we evaluate shared AMoD strategies applied to a Singapore micromodel city testbed. A near-future strategy of exclusive availability of AMoD service in the central business district (CBD), and a further-horizon strategy of the full operation of AMoD city-wide in the absence of other on-demand services, were tested and evaluated. Our results provide insights into the income and accessibility effects on the population under the implementation of shared and automated mobility policies. The outcomes indicate that the city-wide deployment of AMoD results in greater accessibility and network performance. Moreover, the accessibility of low-income individuals is improved relative to that of mid- and high-income individuals. The restriction of AMoD to the CBD along with the operation of other on-demand services, however, provides a certain level of disbenefit to segments of the population in two exceptional cases. The first is to high-income individuals who live in a suburban zone and rely heavily on on-demand services; the second is to mid-income residents that have excellent public transportation coverage with close proximity to the CBD. We further establish the efficacy of the ABA measure, as these findings motivate the need for measuring socioeconomic impacts at the individual level. The work presented here serves as a foundation for policy evaluation in real-world urban models for future mobility paradigms

From traditional to automated mobility on demand: a comprehensive framework for modeling on-demand services in SimMobility

International audienceMobility on demand (MoD) systems have recently emerged as a promising paradigm for sustainable personal urban mobility in cities. In the context of multi-agent simulation technology, the state-of-the-art lacks a platform that captures the dynamics between decentralized driver decision-making and the centralized coordinated decision-making. This work aims to fill this gap by introducing a comprehensive framework that models various facets of MoD, namely heterogeneous MoD driver decision-making and coordinated fleet management within SimMobility, an agent- and activity-based demand model integrated with a dynamic multi-modal network assignment model. To facilitate such a study, we propose an event-based modeling framework. Behavioral models were estimated to characterize the decision-making of drivers using a GPS dataset from a major MoD fleet operator in Singapore. The proposed framework was designed to accommodate behaviors of multiple on-demand services such as traditional MoD, Lyft-like services, and automated MoD (AMoD) services which interact with traffic simulators and a multi-modal transportation network. We demonstrate the benefits of the proposed framework through a large-scale case study in Singapore comparing the fully decentralized traditional MoD with the future AMoD services in a realistic simulation setting. We found that AMoD results in a more efficient service even with increased demand. Parking strategies and fleet sizes will also have an effect on user satisfaction and network performance

A novel global urban typology framework for sustainable mobility futures

Urban mobility significantly contributes to global carbon dioxide emissions. Given the rapid expansion and growth in urban areas, cities thus require innovative policies to ensure efficient and sustainable mobility. Urban typologies can serve as a vehicle for understanding dynamics of cities, which exhibit high variability in form, economic output, mobility behavior, among others. Yet, typologies relevant for sustainable urban mobility analyses are few, outdated and not large enough in scope. In this paper, we present a new typologization spanning 331 cities in 124 countries. Our sample represents 40% of the global urban population and contains the most recent data from 2008 to date. Using a factor analytic and agglomerative clustering approach, we identify 9 urban factors and 12 typologies. We discuss the implications of this new framework for researchers and planners and investigate the relationships between mobility and environmental sustainability indicators. Notably, we show an immediate application of the urban typologies to better understanding travel behavior and also describe their usage for detailed large-scale simulation in representative prototype cities for insights into sustainable future mobility policy pathways. Our data and results are publicly available for further exploration and will serve as a foundation for future analyses toward desirable urban and environmental outcomes. ©201

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

The growing demand for urban mobility highlights the need for relevant and sustainable solutions in cities worldwide. Thus, we develop and implement a framework to analyze the systemic impacts of future urban mobility trends and policies. We build on prior work in classifying the world’s cities into 12 urban typologies that represent distinct land-use and behavioral characteristics by introducing a generalized approach for creating a detailed, simulatable prototype city that is representative of a given typology. We then generate and simulate two auto-dependent (largely US-specific) prototype cities via a state-of-the-art agent-based platform, SimMobility, for integrated demand microsimulation and supply mesoscopic simulation. We demonstrate the framework by analyzing the impacts of automated mobility on-demand (AMoD) implementation strategies in the cities based on demand, congestion, energy consumption and emissions outcomes. Our results show that the introduction of AMoD cannibalizes mass transit while increasing vehicle kilometers traveled (VKT) and congestion. In sprawling auto-dependent cities with low transit penetration, the congestion and energy consumption effects under best-case assumptions are similar regardless of whether AMoD competes with or complements mass transit. In dense auto-dependent cities with moderate transit modeshare, the integration of AMoD with transit yields better outcomes in terms of VKT and congestion. Such cities cannot afford to disinvest in mass transit, as this would result in unsustainable outcomes. Overall, this framework can provide insights into how AMoD can be sustainably harnessed not only in low-density and high-density auto-dependent cities, but also in other typologies